# Methodology, Parameters, and Calculations ## Overview This appendix documents all 782 parameters used in the analysis, organized by type: - External sources (peer-reviewed): 223 - Calculated values: 400 - Core definitions: 159 ### Quick Navigation [Calculated Values](#sec-calculated) (400 parameters) • [External Data Sources](#sec-external) (223 parameters) • [Core Definitions](#sec-definitions) (159 parameters) ## Calculated Values Parameters derived from mathematical formulas and economic models. ### Apocalypse Markup: \$2.72 trillion > **NOTE:** > > The Apocalypse Markup: total military spending beyond the Price of Apocalypse. The amount governments spend above what is needed to trigger nuclear winter and end civilization once. > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Price of Apocalypse (Minimum Viable Apocalypse)](#sec-price_of_apocalypse) 🔢: \$752 million > > $$ \begin{gathered} M_{apocalypse} \\ = Spending_{mil} - P_{apocalypse} \\ = \$2.72T - \$752M \\ = \$2.72T \end{gathered} $$ where: $$ \begin{gathered} P_{apocalypse} \\ = \frac{S_{nuke}}{Overkill_{winter}} \\ = \frac{\$92B}{122} \\ = \$752M \end{gathered} $$ where: $$ \begin{gathered} Overkill_{winter} \\ = \frac{W_{global}}{W_{winter}} \\ = \frac{12{,}200}{100} \\ = 122 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Apocalypse Markup** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Price of Apocalypse (Minimum Viable Apocalypse) (USD) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Apocalypse Markup (10,000 simulations) > > **Simulation Results Summary: Apocalypse Markup** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$2.72 trillion | > | Mean (expected value) | \$2.72 trillion | > | Median (50th percentile) | \$2.72 trillion | > | Standard Deviation | \$544 million | > | 90% Range (5th-95th percentile) | \[\$2.72 trillion, \$2.72 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Apocalypse Markup; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Apocalypse Markup > > *This exceedance probability chart shows the likelihood that Apocalypse Markup will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Apocalypse Markup Multiplier: 3.6kx > **NOTE:** > > How many times total military spending exceeds the Price of Apocalypse. The markup multiplier on the cost of ending civilization. > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Price of Apocalypse (Minimum Viable Apocalypse)](#sec-price_of_apocalypse) 🔢: \$752 million > > $$ \begin{gathered} M_{apocalypse,x} \\ = \frac{Spending_{mil}}{P_{apocalypse}} \\ = \frac{\$2.72T}{\$752M} \\ = 3{,}620 \end{gathered} $$ where: $$ \begin{gathered} P_{apocalypse} \\ = \frac{S_{nuke}}{Overkill_{winter}} \\ = \frac{\$92B}{122} \\ = \$752M \end{gathered} $$ where: $$ \begin{gathered} Overkill_{winter} \\ = \frac{W_{global}}{W_{winter}} \\ = \frac{12{,}200}{100} \\ = 122 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Apocalypse Markup Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Price of Apocalypse (Minimum Viable Apocalypse) (USD) | -0.9008 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Apocalypse Markup Multiplier (10,000 simulations) > > **Simulation Results Summary: Apocalypse Markup Multiplier** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 3.6kx | > | Mean (expected value) | 2.6kx | > | Median (50th percentile) | 2.1kx | > | Standard Deviation | 1.4kx | > | 90% Range (5th-95th percentile) | \[1.3kx, 5.9kx\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Apocalypse Markup Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Apocalypse Markup Multiplier > > *This exceedance probability chart shows the likelihood that Apocalypse Markup Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Automated Revenue Service Annual Operating Cost: \$150 million > **NOTE:** > > Annual operating cost of the Automated Revenue Service: design transaction volume times the all-in comparator-anchored cost per settlement. Uncertainty propagates from the component distributions via Monte Carlo. > > **Inputs**: > > - [Automated Revenue Service Annual Transaction Volume](#sec-automated_revenue_service_annual_transaction_volume): 500 billion transactions > - [All-In Cost per Settlement Transaction](#sec-automated_revenue_service_all_in_cost_per_transaction): \$0.0003 (95% CI: \$0.0001 - \$0.001) > > $$ \begin{gathered} Cost_{opex,ann} \\ = Automated_{annual} \times Cost_{all} \\ = 500B \times \$0.0003 \\ = \$150M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Automated Revenue Service Annual Operating Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | All-In Cost per Settlement Transaction (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Automated Revenue Service Annual Operating Cost (10,000 simulations) > > **Simulation Results Summary: Automated Revenue Service Annual Operating Cost** > > | Statistic | Value | > |:--------------------------------|--------------------------------:| > | Baseline (deterministic) | \$150 million | > | Mean (expected value) | \$150 million | > | Median (50th percentile) | \$121 million | > | Standard Deviation | \$99.9 million | > | 90% Range (5th-95th percentile) | \[\$50 million, \$365 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Automated Revenue Service Annual Operating Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Automated Revenue Service Annual Operating Cost > > *This exceedance probability chart shows the likelihood that Automated Revenue Service Annual Operating Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Automated Revenue Service Annual Savings per American: \$1,666 > **NOTE:** > > Annual savings per American from replacing the IRS and the tax-compliance burden with the Automated Revenue Service, net of the padded replacement budget. > > **Inputs**: > > - [Tax Compliance Waste](#sec-us_gov_waste_tax_compliance) 📊: \$546 billion (95% CI: \$450 billion - \$650 billion) > - [IRS Annual Operating Budget](#sec-irs_annual_operating_budget) 📊: \$12.3 billion > - [Automated Revenue Service Annual Operating Cost](#sec-automated_revenue_service_annual_opex) 🔢: \$150 million > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} Savings_{ann} \\ = (\text{TAX\_COMPLIANCE\_BURDEN} \\ + \text{IRS\_BUDGET} − \text{ARS\_BUDGET}) / \text{US\_POPULATION} \end{gathered} $$ where: $$ \begin{gathered} Cost_{opex,ann} \\ = Automated_{annual} \times Cost_{all} \\ = 500B \times \$0.0003 \\ = \$150M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Automated Revenue Service Annual Savings per American** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Tax Compliance Waste (USD) | 0.9955 | Strong driver | > | US Population in 2024 (people) | -0.0830 | Minimal effect | > | Automated Revenue Service Annual Operating Cost (USD) | -0.0021 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Automated Revenue Service Annual Savings per American (10,000 simulations) > > **Simulation Results Summary: Automated Revenue Service Annual Savings per American** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$1,666 | > | Mean (expected value) | \$1,667 | > | Median (50th percentile) | \$1,666 | > | Standard Deviation | \$145 | > | 90% Range (5th-95th percentile) | \[\$1,418, \$1,917\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Automated Revenue Service Annual Savings per American; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Automated Revenue Service Annual Savings per American > > *This exceedance probability chart shows the likelihood that Automated Revenue Service Annual Savings per American will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Bed Nets Cost per DALY: \$184 > **NOTE:** > > Cost per DALY for insecticide-treated bed nets: GiveWell’s Against Malaria Foundation cost per life saved divided by the discounted life-years of a child death averted (a full life expectancy at the standard discount rate, life-years accruing continuously). Discounted at the same rate and with the same continuous timing as the dFDA and treaty cost-per-DALY figures so comparisons are like for like. GiveWell publishes cost per life saved, not cost per DALY. Bed nets are the standard benchmark for cost-effective global health spending. > > **Inputs**: > > - [GiveWell Cost per Life Saved (AMF, Highest Top-Charity Estimate)](#sec-givewell_cost_per_life_max) 📊: \$5,500 (95% CI: \$3,000 - \$8,000) > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > > $$ \begin{gathered} Cost_{nets} \\ = Cost_{GW,max} / ((1 - (1 \\ + r_{discount})^{-LE_{global}}) / \ln\left(1 + r_{discount}\right)) \end{gathered} $$ > > **Methodology**:^([1](../references.llms.md#ref-givewell-cost-per-life-saved)) > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Bed Nets Cost per DALY** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | GiveWell Cost per Life Saved (AMF, Highest Top-Charity Estimate) (USD/life) | 0.9992 | Strong driver | > | Global Life Expectancy (2024) (years) | -0.0326 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Bed Nets Cost per DALY (10,000 simulations) > > **Simulation Results Summary: Bed Nets Cost per DALY** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | \$184 | > | Mean (expected value) | \$184 | > | Median (50th percentile) | \$184 | > | Standard Deviation | \$41 | > | 90% Range (5th-95th percentile) | \[\$113, \$252\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Bed Nets Cost per DALY; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Bed Nets Cost per DALY > > *This exceedance probability chart shows the likelihood that Bed Nets Cost per DALY will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Best-Practice Life Expectancy Gain: 10.7 years > **NOTE:** > > Gap between current global life expectancy and the best life expectancy achieved by a major country today. Used as a non-arbitrary governance/public-health uplift benchmark rather than capping Wishonia at today’s global average. > > **Inputs**: > > - [Switzerland Life Expectancy](#sec-switzerland_life_expectancy) 📊: 84 years > - [Singapore Life Expectancy](#sec-singapore_life_expectancy) 📊: 84.1 years > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > > $$ \begin{gathered} \Delta LE_{best} \\ = \max\left(LE_{CH}, LE_{SG}\right) - LE_{global} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Best-Practice Life Expectancy Gain** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Life Expectancy (2024) (years) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Best-Practice Life Expectancy Gain (10,000 simulations) > > **Simulation Results Summary: Best-Practice Life Expectancy Gain** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 10.7 | > | Mean (expected value) | 10.7 | > | Median (50th percentile) | 10.7 | > | Standard Deviation | 1.91 | > | 90% Range (5th-95th percentile) | \[7.46, 14\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Best-Practice Life Expectancy Gain; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Best-Practice Life Expectancy Gain > > *This exceedance probability chart shows the likelihood that Best-Practice Life Expectancy Gain will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Bullets Purchasable Per Person Per Year: 850 rounds/person/year > **NOTE:** > > Number of bullets per person on Earth that could be purchased annually with the global military budget. A purchasing power metric illustrating the scale of military spending. > > **Inputs**: > > - [Bullets Purchasable with Global Military Budget](#sec-global_bullets_purchasable_annual) 🔢: 6.8 trillion rounds > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} n_{bullets/person} \\ = \frac{N_{bullets,yr}}{Pop_{global}} \\ = \frac{6.8T}{8B} \\ = 850 \end{gathered} $$ where: $$ \begin{gathered} N_{bullets,yr} \\ = \frac{Spending_{mil}}{c_{bullet}} \\ = \frac{\$2.72T}{\$0.4} \\ = 6.8T \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Bullets Purchasable Per Person Per Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Bullets Purchasable with Global Military Budget (rounds) | 0.9987 | Strong driver | > | Global Population in 2024 (of people) | -0.0477 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Bullets Purchasable Per Person Per Year (10,000 simulations) > > **Simulation Results Summary: Bullets Purchasable Per Person Per Year** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 850 | > | Mean (expected value) | 849 | > | Median (50th percentile) | 799 | > | Standard Deviation | 217 | > | 90% Range (5th-95th percentile) | \[584, 1,271\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Bullets Purchasable Per Person Per Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Bullets Purchasable Per Person Per Year > > *This exceedance probability chart shows the likelihood that Bullets Purchasable Per Person Per Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Engagement Rate: 10% > **NOTE:** > > Probability someone engages with the idea (1 - dismissal rate) > > **Inputs**: > > - [Dismissal Rate](#sec-chain_dismiss_probability): 90% (95% CI: 80% - 97%) > > $$ P_{engage} = 1 - P_{dismiss} = 1 - 90\% = 10\% $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Engagement Rate** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------|------------------------:|:---------------| > | Dismissal Rate (rate) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Engagement Rate (10,000 simulations) > > **Simulation Results Summary: Engagement Rate** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 10% | > | Mean (expected value) | 9.93% | > | Median (50th percentile) | 9.39% | > | Standard Deviation | 4.16% | > | 90% Range (5th-95th percentile) | \[3.92%, 18.2%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Engagement Rate; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Engagement Rate > > *This exceedance probability chart shows the likelihood that Engagement Rate will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Expected Engaged Implementers: 3.48 people > **NOTE:** > > Expected number of implementers who engage (orbit reached x engagement rate x implementer count) > > **Inputs**: > > - [Implementer Orbit Size](#sec-chain_implementer_orbit_size): 1,000 people (95% CI: 150 people - 5,000 people) > - [Effective R](#sec-chain_effective_r): 0.15:1 (95% CI: 0.04:1 - 0.8:1) > - [Initial Audience](#sec-chain_initial_audience): 50,000 people (95% CI: 10,000 people - 500 thousand people) > - [Engagement Rate](#sec-chain_engage_probability) 🔢: 10% > - [Potential Implementers](#sec-chain_implementer_count) 🔢: 2,976 people > > $$ E[N_{engaged}] = P_{reach} \times P_{engage} \times N_{impl} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Expected Engaged Implementers** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:--------------------------------|------------------------:|:----------------| > | Initial Audience (people) | 0.5996 | Strong driver | > | Implementer Orbit Size (people) | 0.3824 | Moderate driver | > | Engagement Rate (rate) | 0.1646 | Weak driver | > | Effective R (ratio) | 0.0973 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Expected Engaged Implementers (10,000 simulations) > > **Simulation Results Summary: Expected Engaged Implementers** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 3.48 | > | Mean (expected value) | 3.02 | > | Median (50th percentile) | 0.896 | > | Standard Deviation | 7.59 | > | 90% Range (5th-95th percentile) | \[0.12, 12.3\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Expected Engaged Implementers; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Expected Engaged Implementers > > *This exceedance probability chart shows the likelihood that Expected Engaged Implementers will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Potential Implementers: 2,976 people > **NOTE:** > > Total potential implementers (billionaires + world leaders) > > **Inputs**: > > - [Global Billionaire Count](#sec-chain_global_billionaire_count) 📊: 2,781 people > - [World Leader Count](#sec-chain_world_leader_count): 195 countries > > $$ \begin{gathered} N_{impl} \\ = N_{billionaire} + N_{leader} \\ = 2{,}780 + 195 \\ = 2{,}980 \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Potential Implementers is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.976e-09) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 2,976 | > | Mean (expected value) | 2,976 | > | Median (50th percentile) | 2,976 | > | Standard Deviation | 0 | > | 90% Range (5th-95th percentile) | \[2,976, 2,976\] | > > #### Exceedance Probability > > **Exceedance note:** Potential Implementers collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.976e-09) > > Approximate deterministic value: **2,976 people** ### P(At Least One Engages): 96.9% > **NOTE:** > > Probability at least one implementer engages (information diffusion only; dominant strategy proof handles action) > > **Inputs**: > > - [P(No Implementer Engages)](#sec-chain_p_no_implementer_engages) 🔢: 3.08% > > $$ P_{reach} = 1 - P_{none} = 1 - 3.08\% = 96.9\% $$ where: $$ \begin{gathered} P_{none} \\ = \left(1 - P_{reach} \cdot P_{engage}\right)^{N_{impl}} \end{gathered} $$ where: $$ P_{engage} = 1 - P_{dismiss} = 1 - 90\% = 10\% $$ where: $$ \begin{gathered} N_{impl} \\ = N_{billionaire} + N_{leader} \\ = 2{,}780 + 195 \\ = 2{,}980 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for P(At Least One Engages)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---------------------------------|------------------------:|:---------------| > | P(No Implementer Engages) (rate) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: P(At Least One Engages) (10,000 simulations) > > **Simulation Results Summary: P(At Least One Engages)** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 96.9% | > | Mean (expected value) | 59.1% | > | Median (50th percentile) | 59.2% | > | Standard Deviation | 31.4% | > | 90% Range (5th-95th percentile) | \[11.3%, 100%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for P(At Least One Engages); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: P(At Least One Engages) > > *This exceedance probability chart shows the likelihood that P(At Least One Engages) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Implementer Orbit Reach Probability: 1.17% > **NOTE:** > > Probability a given implementer’s information orbit is reached by the content cascade > > **Inputs**: > > - [Implementer Orbit Size](#sec-chain_implementer_orbit_size): 1,000 people (95% CI: 150 people - 5,000 people) > - [Effective R](#sec-chain_effective_r): 0.15:1 (95% CI: 0.04:1 - 0.8:1) > - [Initial Audience](#sec-chain_initial_audience): 50,000 people (95% CI: 10,000 people - 500 thousand people) > > $$ \begin{gathered} P_{reach} \\ = 1 - \left(1 - \frac{O_{impl}}{N}\right)^{N_0 \cdot \sum_{i=0}^{3} R_{eff}^i} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Implementer Orbit Reach Probability** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:--------------------------------|------------------------:|:----------------| > | Initial Audience (people) | 0.6446 | Strong driver | > | Implementer Orbit Size (people) | 0.4206 | Moderate driver | > | Effective R (ratio) | 0.1076 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Implementer Orbit Reach Probability (10,000 simulations) > > **Simulation Results Summary: Implementer Orbit Reach Probability** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 1.17% | > | Mean (expected value) | 1.03% | > | Median (50th percentile) | 0.331% | > | Standard Deviation | 2.39% | > | 90% Range (5th-95th percentile) | \[0.0485%, 4.14%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Implementer Orbit Reach Probability; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Implementer Orbit Reach Probability > > *This exceedance probability chart shows the likelihood that Implementer Orbit Reach Probability will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### P(No Implementer Engages): 3.08% > **NOTE:** > > Probability that NO implementer engages (all orbits missed or all dismiss) > > **Inputs**: > > - [Implementer Orbit Size](#sec-chain_implementer_orbit_size): 1,000 people (95% CI: 150 people - 5,000 people) > - [Effective R](#sec-chain_effective_r): 0.15:1 (95% CI: 0.04:1 - 0.8:1) > - [Initial Audience](#sec-chain_initial_audience): 50,000 people (95% CI: 10,000 people - 500 thousand people) > - [Engagement Rate](#sec-chain_engage_probability) 🔢: 10% > - [Potential Implementers](#sec-chain_implementer_count) 🔢: 2,976 people > > $$ \begin{gathered} P_{none} \\ = \left(1 - P_{reach} \cdot P_{engage}\right)^{N_{impl}} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for P(No Implementer Engages)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:--------------------------------|------------------------:|:----------------| > | Implementer Orbit Size (people) | -0.5291 | Strong driver | > | Initial Audience (people) | -0.4753 | Moderate driver | > | Engagement Rate (rate) | -0.2890 | Weak driver | > | Effective R (ratio) | -0.1234 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: P(No Implementer Engages) (10,000 simulations) > > **Simulation Results Summary: P(No Implementer Engages)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | 3.08% | > | Mean (expected value) | 40.9% | > | Median (50th percentile) | 40.8% | > | Standard Deviation | 31.4% | > | 90% Range (5th-95th percentile) | \[0.000449%, 88.7%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for P(No Implementer Engages); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: P(No Implementer Engages) > > *This exceedance probability chart shows the likelihood that P(No Implementer Engages) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Chronic Disease Patients Treated: 982 million people > **NOTE:** > > Estimated unique patients receiving chronic disease treatment annually. Derived from IQVIA days of therapy (1.28T) divided by 365 days divided by 2.5 average medications per patient times 70% post-1962 drugs. > > **Inputs**: > > - [Annual Days of Chronic Disease Therapy](#sec-global_chronic_therapy_days_annual) 📊: 1.28 trillion days (95% CI: 1 trillion days - 1.5 trillion days) > > $$ \begin{gathered} N_{treated} \\ = DOT_{chronic} \times 0.000767 \\ = 1.28T \times 0.000767 \\ = 982M \end{gathered} $$ > > **Methodology**:^([2](../references.llms.md#ref-iqvia-global-medicines-2024)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Chronic Disease Patients Treated** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Days of Chronic Disease Therapy (days) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Chronic Disease Patients Treated (10,000 simulations) > > **Simulation Results Summary: Annual Chronic Disease Patients Treated** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 982 million | > | Mean (expected value) | 983 million | > | Median (50th percentile) | 979 million | > | Standard Deviation | 98 million | > | 90% Range (5th-95th percentile) | \[831 million, 1.15 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Chronic Disease Patients Treated; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Chronic Disease Patients Treated > > *This exceedance probability chart shows the likelihood that Annual Chronic Disease Patients Treated will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Combination Therapy Space: 45.1 billion combinations > **NOTE:** > > Total combination therapy space (pairwise drug combinations × diseases). Standard in oncology, HIV, cardiology. > > **Inputs**: > > - [Pairwise Drug Combinations](#sec-combination_therapy_pairs) 🔢: 45.1 million combinations > - [Trial-Relevant Diseases](#sec-trial_relevant_diseases_count): 1,000 diseases (95% CI: 800 diseases - 1,200 diseases) > > $$ \begin{gathered} Space_{combo} \\ = N_{combo} \times N_{diseases,trial} \\ = 45.1M \times 1{,}000 \\ = 45.1B \end{gathered} $$ where: $$ N_{combo} = \frac{N_{safe} \cdot (N_{safe} - 1)}{2} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Combination Therapy Space** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pairwise Drug Combinations (combinations) | 0.9212 | Strong driver | > | Trial-Relevant Diseases (diseases) | 0.3584 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Combination Therapy Space (10,000 simulations) > > **Simulation Results Summary: Combination Therapy Space** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | 45.1 billion | > | Mean (expected value) | 46.1 billion | > | Median (50th percentile) | 44.5 billion | > | Standard Deviation | 14.9 billion | > | 90% Range (5th-95th percentile) | \[25 billion, 72.6 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Combination Therapy Space; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Combination Therapy Space > > *This exceedance probability chart shows the likelihood that Combination Therapy Space will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pairwise Drug Combinations: 45.1 million combinations > **NOTE:** > > Unique pairwise drug combinations from known safe compounds (n choose 2) > > **Inputs**: > > - [Safe Compounds Available for Testing](#sec-safe_compounds_count): 9,500 compounds (95% CI: 7,000 compounds - 12,000 compounds) > > $$ N_{combo} = \frac{N_{safe} \cdot (N_{safe} - 1)}{2} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pairwise Drug Combinations** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Safe Compounds Available for Testing (compounds) | 0.9977 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pairwise Drug Combinations (10,000 simulations) > > **Simulation Results Summary: Pairwise Drug Combinations** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 45.1 million | > | Mean (expected value) | 46.1 million | > | Median (50th percentile) | 44.9 million | > | Standard Deviation | 13.7 million | > | 90% Range (5th-95th percentile) | \[26.2 million, 68.9 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pairwise Drug Combinations; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pairwise Drug Combinations > > *This exceedance probability chart shows the likelihood that Pairwise Drug Combinations will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### DALYs Averted per Percentage Point: 5.65 billion DALYs > **NOTE:** > > DALYs averted per percentage point of implementation probability shift. One percent of total DALYs from eliminating trial capacity bottleneck and efficacy lag. > > **Inputs**: > > - [Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_dalys) 🔢: 565 billion DALYs > > $$ \begin{gathered} DALYs_{pp} \\ = DALYs_{max} \times 0.01 \\ = 565B \times 0.01 \\ = 5.65B \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for DALYs Averted per Percentage Point** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: DALYs Averted per Percentage Point (10,000 simulations) > > **Simulation Results Summary: DALYs Averted per Percentage Point** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 5.65 billion | > | Mean (expected value) | 6.35 billion | > | Median (50th percentile) | 6 billion | > | Standard Deviation | 2.37 billion | > | 90% Range (5th-95th percentile) | \[3.09 billion, 10.8 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for DALYs Averted per Percentage Point; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: DALYs Averted per Percentage Point > > *This exceedance probability chart shows the likelihood that DALYs Averted per Percentage Point will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Contribution EV per Percentage Point (Treaty): \$5,189 > **NOTE:** > > Personal expected value per percentage point of implementation probability shift under Treaty Trajectory. One percent of the per-capita lifetime income gain. > > **Inputs**: > > - [Treaty Trajectory Lifetime Income Gain (Per Capita)](#sec-treaty_trajectory_lifetime_income_gain_per_capita) 🔢: \$518,879 > > $$ \begin{gathered} EV_{pp,treaty} \\ = \Delta Y_{lifetime,treaty} \times 0.01 \\ = \$519K \times 0.01 \\ = \$5.19K \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Contribution EV per Percentage Point (Treaty)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Lifetime Income Gain (Per Capita) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Contribution EV per Percentage Point (Treaty) (10,000 simulations) > > **Simulation Results Summary: Contribution EV per Percentage Point (Treaty)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$5,189 | > | Mean (expected value) | \$5,321 | > | Median (50th percentile) | \$5,220 | > | Standard Deviation | \$1,902 | > | 90% Range (5th-95th percentile) | \[\$2,217, \$8,609\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Contribution EV per Percentage Point (Treaty); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Contribution EV per Percentage Point (Treaty) > > *This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Treaty) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Contribution EV per Percentage Point (Treaty, Income + Health): \$29,339 > **NOTE:** > > Income-plus-health personal expected value per percentage point of implementation probability shift under Treaty Trajectory. > > **Inputs**: > > - [Treaty Personal Upside (Income + Health)](#sec-treaty_personal_upside_income_plus_health) 🔢: \$2.93 million > > $$ \begin{gathered} EV_{pp,treaty,income+health} \\ = Upside_{income+health,treaty} \times 0.01 \\ = \$2.93M \times 0.01 \\ = \$29.3K \end{gathered} $$ where: $$ \begin{gathered} Upside_{income+health,treaty} \\ = \Delta Y_{lifetime,treaty} + Value_{HALE,treaty} \\ = \$519K + \$2.42M \\ = \$2.93M \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} Value_{HALE,treaty} \\ = \Delta HALE_{treaty,15} \times Value_{QALY} \\ = 16.1 \times \$150K \\ = \$2.42M \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \Delta_{HALE} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Contribution EV per Percentage Point (Treaty, Income + Health)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Personal Upside (Income + Health) (USD/person) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Contribution EV per Percentage Point (Treaty, Income + Health) (10,000 simulations) > > **Simulation Results Summary: Contribution EV per Percentage Point (Treaty, Income + Health)** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$29,339 | > | Mean (expected value) | \$27,792 | > | Median (50th percentile) | \$25,811 | > | Standard Deviation | \$12,146 | > | 90% Range (5th-95th percentile) | \[\$12,866, \$49,122\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Contribution EV per Percentage Point (Treaty, Income + Health); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Contribution EV per Percentage Point (Treaty, Income + Health) > > *This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Treaty, Income + Health) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Contribution EV per Percentage Point (Wishonia): \$364,454 > **NOTE:** > > Personal expected value per percentage point of implementation probability shift under Wishonia Trajectory. One percent of the per-capita lifetime income gain. > > **Inputs**: > > - [Wishonia Trajectory Lifetime Income Gain (Per Capita)](#sec-wishonia_trajectory_lifetime_income_gain_per_capita) 🔢: \$36.4 million > > $$ \begin{gathered} EV_{pp,wish} \\ = \Delta Y_{lifetime,wish} \times 0.01 \\ = \$36.4M \times 0.01 \\ = \$364K \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,wish} \\ = Y_{cum,wish} - Y_{cum,earth} \\ = \$37.3M - \$904K \\ = \$36.4M \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,wish} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,wish})((1+g_{pc,wish})^{20}-1)}{g_{pc,wish}} \\ + \bar{y}_{wish,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{wish,20} \\ = \frac{GDP_{wish,20}}{Pop_{2045}} \\ = \frac{\$10700T}{9.2B} \\ = \$1.16M \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Contribution EV per Percentage Point (Wishonia)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory Lifetime Income Gain (Per Capita) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Contribution EV per Percentage Point (Wishonia) (10,000 simulations) > > **Simulation Results Summary: Contribution EV per Percentage Point (Wishonia)** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | \$364,454 | > | Mean (expected value) | \$422,156 | > | Median (50th percentile) | \$348,951 | > | Standard Deviation | \$283,249 | > | 90% Range (5th-95th percentile) | \[\$134,597, \$969,785\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Contribution EV per Percentage Point (Wishonia); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Contribution EV per Percentage Point (Wishonia) > > *This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Wishonia) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Contribution EV per Percentage Point (Wishonia, Income + Health): \$404,654 > **NOTE:** > > Income-plus-health personal expected value per percentage point of implementation probability shift under Wishonia Trajectory. > > **Inputs**: > > - [Wishonia Personal Upside (Income + Health)](#sec-wishonia_personal_upside_income_plus_health) 🔢: \$40.5 million > > $$ \begin{gathered} EV_{pp,wish,income+health} \\ = Upside_{income+health,wish} \times 0.01 \\ = \$40.5M \times 0.01 \\ = \$405K \end{gathered} $$ where: $$ \begin{gathered} Upside_{income+health,wish} \\ = \Delta Y_{lifetime,wish} + Value_{HALE,wish} \\ = \$36.4M + \$4.02M \\ = \$40.5M \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,wish} \\ = Y_{cum,wish} - Y_{cum,earth} \\ = \$37.3M - \$904K \\ = \$36.4M \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,wish} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,wish})((1+g_{pc,wish})^{20}-1)}{g_{pc,wish}} \\ + \bar{y}_{wish,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{wish,20} \\ = \frac{GDP_{wish,20}}{Pop_{2045}} \\ = \frac{\$10700T}{9.2B} \\ = \$1.16M \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} Value_{HALE,wish} \\ = \Delta HALE_{wish,15} \times Value_{QALY} \\ = 26.8 \times \$150K \\ = \$4.02M \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \Delta_{HALE} \\ + \Delta HALE_{wish,extra,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,extra,15} \\ = f_{cure,15,wish} \times (\Delta LE_{best} \\ + T_{extend} \times \rho_{HALE,15}) \end{gathered} $$ where: $$ \begin{gathered} \Delta LE_{best} \\ = \max\left(LE_{CH}, LE_{SG}\right) - LE_{global} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Contribution EV per Percentage Point (Wishonia, Income + Health)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Personal Upside (Income + Health) (USD/person) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Contribution EV per Percentage Point (Wishonia, Income + Health) (10,000 simulations) > > **Simulation Results Summary: Contribution EV per Percentage Point (Wishonia, Income + Health)** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$404,654 | > | Mean (expected value) | \$462,203 | > | Median (50th percentile) | \$389,419 | > | Standard Deviation | \$283,563 | > | 90% Range (5th-95th percentile) | \[\$172,761, \$1.01 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Contribution EV per Percentage Point (Wishonia, Income + Health); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Contribution EV per Percentage Point (Wishonia, Income + Health) > > *This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Wishonia, Income + Health) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Lives Saved per Percentage Point: 107 million lives > **NOTE:** > > Lives saved per percentage point of implementation probability shift. One percent of total lives saved from eliminating trial capacity bottleneck and efficacy lag. > > **Inputs**: > > - [Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_lives_saved) 🔢: 10.7 billion deaths > > $$ \begin{gathered} Lives_{pp} \\ = Lives_{max} \times 0.01 \\ = 10.7B \times 0.01 \\ = 107M \end{gathered} $$ where: $$ \begin{gathered} Lives_{max} \\ = Deaths_{disease,daily} \times T_{accel,max} \times 338 \\ = 150{,}000 \times 212 \times 338 \\ = 10.7B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Lives Saved per Percentage Point** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (deaths) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Lives Saved per Percentage Point (10,000 simulations) > > **Simulation Results Summary: Lives Saved per Percentage Point** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 107 million | > | Mean (expected value) | 121 million | > | Median (50th percentile) | 115 million | > | Standard Deviation | 42.8 million | > | 90% Range (5th-95th percentile) | \[62.4 million, 203 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Lives Saved per Percentage Point; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Lives Saved per Percentage Point > > *This exceedance probability chart shows the likelihood that Lives Saved per Percentage Point will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Suffering Hours Prevented per Percentage Point: 19.3 trillion hours > **NOTE:** > > Suffering hours prevented per percentage point of implementation probability shift. One percent of total suffering hours from eliminating trial capacity bottleneck and efficacy lag. > > **Inputs**: > > - [Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_suffering_hours) 🔢: 1.93 quadrillion hours > > $$ \begin{gathered} Hours_{pp} \\ = Hours_{suffer,max} \times 0.01 \\ = 1930T \times 0.01 \\ = 19.3T \end{gathered} $$ where: $$ \begin{gathered} Hours_{suffer,max} \\ = DALYs_{max} \times Pct_{YLD} \times 8760 \\ = 565B \times 0.39 \times 8760 \\ = 1930T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Suffering Hours Prevented per Percentage Point** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (hours) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Suffering Hours Prevented per Percentage Point (10,000 simulations) > > **Simulation Results Summary: Suffering Hours Prevented per Percentage Point** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | 19.3 trillion | > | Mean (expected value) | 21.7 trillion | > | Median (50th percentile) | 20.4 trillion | > | Standard Deviation | 8.28 trillion | > | 90% Range (5th-95th percentile) | \[10.4 trillion, 37.5 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Suffering Hours Prevented per Percentage Point; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Suffering Hours Prevented per Percentage Point > > *This exceedance probability chart shows the likelihood that Suffering Hours Prevented per Percentage Point will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Conventional Retirement Horizon Multiple: 2.57x > **NOTE:** > > Compound multiple for conventional retirement investing over the prize pool resolution horizon (tied to the destructive economy 50% threshold year). > > **Inputs**: > > - [Conventional Retirement Return (After Fees)](#sec-conventional_retirement_return) 📊: 6.5% (95% CI: 5% - 8%) > - [Year Destructive Economy Reaches 50% of GDP](#sec-destructive_economy_50pct_year) 🔢: 2040 > - [Destructive Economy Base Year](#sec-destructive_economy_base_year): 2025 > > $$ M_{retire} = (1 + r_{retire})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Conventional Retirement Horizon Multiple** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Conventional Retirement Return (After Fees) (percent) | 0.9984 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Conventional Retirement Horizon Multiple (10,000 simulations) > > **Simulation Results Summary: Conventional Retirement Horizon Multiple** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 2.57x | > | Mean (expected value) | 2.58x | > | Median (50th percentile) | 2.57x | > | Standard Deviation | 0.267x | > | 90% Range (5th-95th percentile) | \[2.15x, 3.07x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Conventional Retirement Horizon Multiple; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Conventional Retirement Horizon Multiple > > *This exceedance probability chart shows the likelihood that Conventional Retirement Horizon Multiple will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Drugs Never Developed VSL: \$3 quadrillion > **NOTE:** > > Corporate-defendant wrongful-death valuation for the aggressive prosecutor estimate of deaths from drugs never developed. > > **Inputs**: > > - [Corporate Damages Drugs Never Developed Deaths](#sec-corporate_damages_drugs_never_developed_deaths): 300 million deaths > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} V_{neverdev,VSL} \\ = Deaths_{neverdev} \times VSL \\ = 300M \times \$10M \\ = \$3000T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Drugs Never Developed VSL** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:--------------------------------|------------------------:|:---------------| > | Value of Statistical Life (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Drugs Never Developed VSL (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Drugs Never Developed VSL** > > | Statistic | Value | > |:--------------------------------|-----------------------------------------:| > | Baseline (deterministic) | \$3 quadrillion | > | Mean (expected value) | \$2.96 quadrillion | > | Median (50th percentile) | \$2.91 quadrillion | > | Standard Deviation | \$812 trillion | > | 90% Range (5th-95th percentile) | \[\$1.7 quadrillion, \$4.5 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Drugs Never Developed VSL; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Drugs Never Developed VSL > > *This exceedance probability chart shows the likelihood that Corporate Damages Drugs Never Developed VSL will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Efficacy Lag Deaths VSL: \$1.02 quadrillion > **NOTE:** > > Corporate-defendant wrongful-death valuation for existing-drug efficacy-lag deaths using the standard value of a statistical life. > > **Inputs**: > > - [Total Deaths from Historical Progress Delays](#sec-existing_drugs_efficacy_lag_deaths_total) 🔢: 102 million deaths > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Efficacy Lag Deaths VSL** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Deaths from Historical Progress Delays (deaths) | 0.7742 | Strong driver | > | Value of Statistical Life (USD) | 0.5919 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Efficacy Lag Deaths VSL (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Efficacy Lag Deaths VSL** > > | Statistic | Value | > |:--------------------------------|---------------------------------------:| > | Baseline (deterministic) | \$1.02 quadrillion | > | Mean (expected value) | \$1 quadrillion | > | Median (50th percentile) | \$921 trillion | > | Standard Deviation | \$462 trillion | > | 90% Range (5th-95th percentile) | \[\$405 trillion, \$1.89 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Efficacy Lag Deaths VSL; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Efficacy Lag Deaths VSL > > *This exceedance probability chart shows the likelihood that Corporate Damages Efficacy Lag Deaths VSL will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Forward Settlement Value Per Capita: \$940,697 > **NOTE:** > > Forward treaty settlement value per living human from the 1% Treaty impact model. Kept separate from historical corporate damages. > > **Inputs**: > > - [Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_economic_value) 🔢: \$7.53 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} V_{settlement,pc} \\ = \frac{Value_{max}}{Pop_{global}} \\ = \frac{\$7530T}{8B} \\ = \$941K \end{gathered} $$ where: $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Forward Settlement Value Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.9997 | Strong driver | > | Global Population in 2024 (of people) | -0.0322 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Forward Settlement Value Per Capita (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Forward Settlement Value Per Capita** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | \$940,697 | > | Mean (expected value) | \$974,494 | > | Median (50th percentile) | \$966,091 | > | Standard Deviation | \$368,164 | > | 90% Range (5th-95th percentile) | \[\$385,781, \$1.6 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Forward Settlement Value Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Forward Settlement Value Per Capita > > *This exceedance probability chart shows the likelihood that Corporate Damages Forward Settlement Value Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Pentagon FCA Penalty Increment: \$4.92 trillion > **NOTE:** > > False Claims Act-style penalty increment on Pentagon unaccounted funds, calculated as treble exposure minus principal so the principal is not counted twice. > > **Inputs**: > > - [Pentagon Unaccounted Funds False Claims Analog Exposure](#sec-pentagon_unaccounted_false_claims_analog_exposure) 🔢: \$7.38 trillion > - [Pentagon Unaccounted Funds](#sec-pentagon_unaccounted_funds) 📊: \$2.46 trillion > > $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Corporate Damages Pentagon FCA Penalty Increment is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 4.920e+00) > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$4.92 trillion | > | Mean (expected value) | \$4.92 trillion | > | Median (50th percentile) | \$4.92 trillion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$4.92 trillion, \$4.92 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** Corporate Damages Pentagon FCA Penalty Increment collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 4.920e+00) > > Approximate deterministic value: **\$4.92 trillion** ### Corporate Damages Property Plus Environmental Destruction: \$50 trillion > **NOTE:** > > Property and environmental destruction from war since 1900, separated from war death valuation to avoid adding the QALY component embedded in the broader historical sunk-cost parameter. > > **Inputs**: > > - [Cumulative Property Destruction from War Since 1900](#sec-war_property_destruction_since_1900): \$45 trillion (95% CI: \$30 trillion - \$60 trillion) > - [Cumulative Environmental Destruction from War Since 1900](#sec-war_environmental_destruction_since_1900): \$5 trillion (95% CI: \$2 trillion - \$10 trillion) > > $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Property Plus Environmental Destruction** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Cumulative Property Destruction from War Since 1900 (USD) | 0.9772 | Strong driver | > | Cumulative Environmental Destruction from War Since 1900 (USD) | 0.2130 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Property Plus Environmental Destruction (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Property Plus Environmental Destruction** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$50 trillion | > | Mean (expected value) | \$49.9 trillion | > | Median (50th percentile) | \$50 trillion | > | Standard Deviation | \$8.84 trillion | > | 90% Range (5th-95th percentile) | \[\$36.1 trillion, \$63.6 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Property Plus Environmental Destruction; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Property Plus Environmental Destruction > > *This exceedance probability chart shows the likelihood that Corporate Damages Property Plus Environmental Destruction will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Base Ask Per Capita: \$913,219 > **NOTE:** > > Aggressive corporate-liability prosecutor base ask per living human. > > **Inputs**: > > - [Corporate Damages Prosecutor Base Ask Total](#sec-corporate_damages_prosecutor_base_ask_total) 🔢: \$7.31 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} D_{corp,ask,pc} \\ = \frac{D_{corp,ask}}{Pop_{global}} \\ = \frac{\$7310T}{8B} \\ = \$913K \end{gathered} $$ where: $$ \begin{gathered} D_{corp,ask} \\ = D_{corp,floor} + V_{neverdev,VSL} \\ = \$4310T + \$3000T \\ = \$7310T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} V_{neverdev,VSL} \\ = Deaths_{neverdev} \times VSL \\ = 300M \times \$10M \\ = \$3000T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Base Ask Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Prosecutor Base Ask Total (USD) | 0.9992 | Strong driver | > | Global Population in 2024 (of people) | -0.0435 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Base Ask Per Capita (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Base Ask Per Capita** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$913,219 | > | Mean (expected value) | \$853,047 | > | Median (50th percentile) | \$835,043 | > | Standard Deviation | \$238,313 | > | 90% Range (5th-95th percentile) | \[\$490,901, \$1.27 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Base Ask Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Base Ask Per Capita > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Base Ask Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Base Ask Total: \$7.31 quadrillion > **NOTE:** > > Aggressive corporate-liability prosecutor base ask: strict floor plus the aggressive pleading estimate for deaths from drugs never developed. Excludes punitive damages, disgorgement, ongoing lost-income damages, and forward treaty settlement value. > > **Inputs**: > > - [Corporate Damages Strict Floor Total](#sec-corporate_damages_strict_floor_total) 🔢: \$4.31 quadrillion > - [Corporate Damages Drugs Never Developed VSL](#sec-corporate_damages_drugs_never_developed_vsl) 🔢: \$3 quadrillion > > $$ \begin{gathered} D_{corp,ask} \\ = D_{corp,floor} + V_{neverdev,VSL} \\ = \$4310T + \$3000T \\ = \$7310T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} V_{neverdev,VSL} \\ = Deaths_{neverdev} \times VSL \\ = 300M \times \$10M \\ = \$3000T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Base Ask Total** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Strict Floor Total (USD) | 0.6061 | Strong driver | > | Corporate Damages Drugs Never Developed VSL (USD) | 0.4262 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Base Ask Total (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Base Ask Total** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$7.31 quadrillion | > | Mean (expected value) | \$6.82 quadrillion | > | Median (50th percentile) | \$6.69 quadrillion | > | Standard Deviation | \$1.9 quadrillion | > | 90% Range (5th-95th percentile) | \[\$3.92 quadrillion, \$10.2 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Base Ask Total; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Base Ask Total > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Base Ask Total will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Gross Aging Intake Exposure: \$17.8 quadrillion > **NOTE:** > > Gross pleading exposure for the overlapping aging intake class valued at VSL. Displayed separately because it overlaps with the broader disease-death class. > > **Inputs**: > > - [War Trial Redirect Post-Cutoff Aging Plaintiffs](#sec-war_trial_redirect_post_cutoff_aging_plaintiffs) 🔢: 1.78 billion plaintiffs > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} D_{corp,aging,gross} \\ = N_{plaintiffs,aging} \times VSL \\ = 1.78B \times \$10M \\ = \$17800T \end{gathered} $$ where: $$ \begin{gathered} N_{plaintiffs,aging} \\ = T_{post,aging} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 35 \times 55M \times 92.6\% \\ = 1.78B \end{gathered} $$ where: $$ T_{post,aging} = Y_{plead,end} - Y_{aging,plead} + 1 $$ where: $$ \begin{gathered} Y_{aging,plead} \\ = Y_{disease,plead} + T_{aging,lag} \\ = 1{,}950 + 40 \\ = 1{,}990 \end{gathered} $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Gross Aging Intake Exposure** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War Trial Redirect Post-Cutoff Aging Plaintiffs (plaintiffs) | 0.9344 | Strong driver | > | Value of Statistical Life (USD) | 0.2416 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Gross Aging Intake Exposure (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Gross Aging Intake Exposure** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$17.8 quadrillion | > | Mean (expected value) | \$15.7 quadrillion | > | Median (50th percentile) | \$17.8 quadrillion | > | Standard Deviation | $17.8 quadrillion | | 90% Range (5th-95th percentile) | [$-16.4 quadrillion, \$39.8 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Gross Aging Intake Exposure; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Gross Aging Intake Exposure > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Aging Intake Exposure will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Gross Disease DALY Exposure: \$30 quadrillion > **NOTE:** > > Gross pleading exposure for post-cutoff disease DALYs valued at the standard QALY value. This is disease-year and suffering exposure, not a final non-duplicative award. > > **Inputs**: > > - [War Trial Redirect Post-Cutoff Disease DALYs](#sec-war_trial_redirect_post_cutoff_disease_dalys) 🔢: 200 billion DALYs > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} D_{corp,DALY,gross} \\ = DALYs_{post,disease} \times Value_{QALY} \\ = 200B \times \$150K \\ = \$30000T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{post,disease} \\ = T_{post,disease} \times DALYs_{global,ann} \times Pct_{avoid,DALY} \\ = 75 \times 2.88B \times 92.6\% \\ = 200B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Gross Disease DALY Exposure** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War Trial Redirect Post-Cutoff Disease DALYs (DALYs) | 0.9206 | Strong driver | > | Standard Economic Value per QALY (USD/QALY) | 0.3486 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Gross Disease DALY Exposure (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Gross Disease DALY Exposure** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$30 quadrillion | > | Mean (expected value) | \$28.4 quadrillion | > | Median (50th percentile) | \$30.1 quadrillion | > | Standard Deviation | \$14.8 quadrillion | > | 90% Range (5th-95th percentile) | \[\$1.81 quadrillion, \$48.6 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Gross Disease DALY Exposure; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Gross Disease DALY Exposure > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Disease DALY Exposure will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Gross Medical Misallocation Exposure: \$38.2 quadrillion > **NOTE:** > > Gross pleading exposure for post-cutoff medical misallocation disease plaintiffs valued at VSL. This is pleading exposure, not a final non-duplicative award. > > **Inputs**: > > - [War Trial Redirect Post-Cutoff Disease Plaintiffs](#sec-war_trial_redirect_post_cutoff_disease_plaintiffs) 🔢: 3.82 billion plaintiffs > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} D_{corp,med,gross} \\ = N_{plaintiffs,disease} \times VSL \\ = 3.82B \times \$10M \\ = \$38200T \end{gathered} $$ where: $$ \begin{gathered} N_{plaintiffs,disease} \\ = T_{post,disease} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 75 \times 55M \times 92.6\% \\ = 3.82B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Gross Medical Misallocation Exposure** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War Trial Redirect Post-Cutoff Disease Plaintiffs (plaintiffs) | 0.8449 | Strong driver | > | Value of Statistical Life (USD) | 0.4753 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Gross Medical Misallocation Exposure (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Gross Medical Misallocation Exposure** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$38.2 quadrillion | > | Mean (expected value) | \$35.7 quadrillion | > | Median (50th percentile) | \$36 quadrillion | > | Standard Deviation | \$20.6 quadrillion | > | 90% Range (5th-95th percentile) | \[\$2.44 quadrillion, \$68.8 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Gross Medical Misallocation Exposure; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Gross Medical Misallocation Exposure > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Medical Misallocation Exposure will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human: \$5.31 million > **NOTE:** > > Gross death-based pleading exposure per living human if the judgment were distributed as universal residual restitution. This is not a final award. > > **Inputs**: > > - [Corporate Damages Prosecutor Gross Pleading Exposure Total](#sec-corporate_damages_prosecutor_gross_pleading_exposure_total) 🔢: \$42.5 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} D_{corp,plead,gross,pc} \\ = \frac{D_{corp,plead,gross}}{Pop_{global}} \\ = \frac{\$42500T}{8B} \\ = \$5.31M \end{gathered} $$ where: $$ \begin{gathered} D_{corp,plead,gross} \\ = D_{corp,floor} + D_{corp,med,gross} \\ = \$4310T + \$38200T \\ = \$42500T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,med,gross} \\ = N_{plaintiffs,disease} \times VSL \\ = 3.82B \times \$10M \\ = \$38200T \end{gathered} $$ where: $$ \begin{gathered} N_{plaintiffs,disease} \\ = T_{post,disease} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 75 \times 55M \times 92.6\% \\ = 3.82B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Prosecutor Gross Pleading Exposure Total (USD) | 0.9998 | Strong driver | > | Global Population in 2024 (of people) | -0.0228 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$5.31 million | > | Mean (expected value) | \$4.94 million | > | Median (50th percentile) | \$4.95 million | > | Standard Deviation | \$2.63 million | > | 90% Range (5th-95th percentile) | \[\$719,051, \$9.23 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Gross Pleading Exposure Total: \$42.5 quadrillion > **NOTE:** > > Aggressive prosecutor gross pleading exposure: strict non-duplicative floor plus gross medical misallocation exposure. This is gross pleading exposure, not a final award. > > **Inputs**: > > - [Corporate Damages Strict Floor Total](#sec-corporate_damages_strict_floor_total) 🔢: \$4.31 quadrillion > - [Corporate Damages Prosecutor Gross Medical Misallocation Exposure](#sec-corporate_damages_prosecutor_gross_medical_misallocation_exposure) 🔢: \$38.2 quadrillion > > $$ \begin{gathered} D_{corp,plead,gross} \\ = D_{corp,floor} + D_{corp,med,gross} \\ = \$4310T + \$38200T \\ = \$42500T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,med,gross} \\ = N_{plaintiffs,disease} \times VSL \\ = 3.82B \times \$10M \\ = \$38200T \end{gathered} $$ where: $$ \begin{gathered} N_{plaintiffs,disease} \\ = T_{post,disease} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 75 \times 55M \times 92.6\% \\ = 3.82B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Gross Pleading Exposure Total** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Prosecutor Gross Medical Misallocation Exposure (USD) | 0.9762 | Strong driver | > | Corporate Damages Strict Floor Total (USD) | 0.0548 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Gross Pleading Exposure Total (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Gross Pleading Exposure Total** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$42.5 quadrillion | > | Mean (expected value) | \$39.5 quadrillion | > | Median (50th percentile) | \$39.6 quadrillion | > | Standard Deviation | \$21.1 quadrillion | > | 90% Range (5th-95th percentile) | \[\$5.75 quadrillion, \$74.1 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Gross Pleading Exposure Total; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Gross Pleading Exposure Total > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure Total will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Gross Pleading Exposure With DALYs: \$72.5 quadrillion > **NOTE:** > > Aggressive prosecutor stacked gross pleading exposure: strict floor plus post-cutoff disease-death VSL exposure plus post-cutoff disease DALY exposure. This intentionally shows the full pleading stack and may overlap; it is not a final non-duplicative award. > > **Inputs**: > > - [Corporate Damages Strict Floor Total](#sec-corporate_damages_strict_floor_total) 🔢: \$4.31 quadrillion > - [Corporate Damages Prosecutor Gross Medical Misallocation Exposure](#sec-corporate_damages_prosecutor_gross_medical_misallocation_exposure) 🔢: \$38.2 quadrillion > - [Corporate Damages Prosecutor Gross Disease DALY Exposure](#sec-corporate_damages_prosecutor_gross_disease_daly_exposure) 🔢: \$30 quadrillion > > $$ \begin{gathered} D_{corp,plead,DALY} \\ = D_{corp,floor} + D_{corp,med,gross} + D_{corp,DALY,gross} \\ = \$4310T + \$38200T + \$30000T \\ = \$72500T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,med,gross} \\ = N_{plaintiffs,disease} \times VSL \\ = 3.82B \times \$10M \\ = \$38200T \end{gathered} $$ where: $$ \begin{gathered} N_{plaintiffs,disease} \\ = T_{post,disease} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 75 \times 55M \times 92.6\% \\ = 3.82B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} D_{corp,DALY,gross} \\ = DALYs_{post,disease} \times Value_{QALY} \\ = 200B \times \$150K \\ = \$30000T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{post,disease} \\ = T_{post,disease} \times DALYs_{global,ann} \times Pct_{avoid,DALY} \\ = 75 \times 2.88B \times 92.6\% \\ = 200B \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Gross Pleading Exposure With DALYs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Prosecutor Gross Medical Misallocation Exposure (USD) | 0.6248 | Strong driver | > | Corporate Damages Prosecutor Gross Disease DALY Exposure (USD) | 0.4502 | Moderate driver | > | Corporate Damages Strict Floor Total (USD) | 0.0351 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Gross Pleading Exposure With DALYs (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Gross Pleading Exposure With DALYs** > > | Statistic | Value | > |:--------------------------------|------------------------------------------:| > | Baseline (deterministic) | \$72.5 quadrillion | > | Mean (expected value) | \$67.9 quadrillion | > | Median (50th percentile) | \$71.9 quadrillion | > | Standard Deviation | \$32.9 quadrillion | > | 90% Range (5th-95th percentile) | \[\$8.55 quadrillion, \$113 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Gross Pleading Exposure With DALYs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Gross Pleading Exposure With DALYs > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure With DALYs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human: \$9.07 million > **NOTE:** > > Gross stacked pleading exposure per living human if the death and DALY pleading stack were distributed as universal residual restitution. This is not a final award. > > **Inputs**: > > - [Corporate Damages Prosecutor Gross Pleading Exposure With DALYs](#sec-corporate_damages_prosecutor_gross_pleading_exposure_with_dalys) 🔢: \$72.5 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} D_{corp,plead,DALY,pc} \\ = \frac{D_{corp,plead,DALY}}{Pop_{global}} \\ = \frac{\$72500T}{8B} \\ = \$9.07M \end{gathered} $$ where: $$ \begin{gathered} D_{corp,plead,DALY} \\ = D_{corp,floor} + D_{corp,med,gross} + D_{corp,DALY,gross} \\ = \$4310T + \$38200T + \$30000T \\ = \$72500T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,med,gross} \\ = N_{plaintiffs,disease} \times VSL \\ = 3.82B \times \$10M \\ = \$38200T \end{gathered} $$ where: $$ \begin{gathered} N_{plaintiffs,disease} \\ = T_{post,disease} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 75 \times 55M \times 92.6\% \\ = 3.82B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} D_{corp,DALY,gross} \\ = DALYs_{post,disease} \times Value_{QALY} \\ = 200B \times \$150K \\ = \$30000T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{post,disease} \\ = T_{post,disease} \times DALYs_{global,ann} \times Pct_{avoid,DALY} \\ = 75 \times 2.88B \times 92.6\% \\ = 200B \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Prosecutor Gross Pleading Exposure With DALYs (USD) | 0.9998 | Strong driver | > | Global Population in 2024 (of people) | -0.0251 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$9.07 million | > | Mean (expected value) | \$8.49 million | > | Median (50th percentile) | \$8.99 million | > | Standard Deviation | \$4.11 million | > | 90% Range (5th-95th percentile) | \[\$1.06 million, \$14.1 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human > > *This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita: \$9.13 million > **NOTE:** > > Constitutional-ceiling exposure per living human under State Farm v. Campbell. This is exposure, not a typical award. > > **Inputs**: > > - [Corporate Damages State Farm Constitutional-Ceiling Exposure Total](#sec-corporate_damages_state_farm_ceiling_exposure_total) 🔢: \$73.1 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} D_{corp,StateFarm,pc} \\ = \frac{D_{corp,StateFarm}}{Pop_{global}} \\ = \frac{\$73100T}{8B} \\ = \$9.13M \end{gathered} $$ where: $$ \begin{gathered} D_{corp,StateFarm} \\ = D_{corp,ask} \times m_{StateFarm} \\ = \$7310T \times 10 \\ = \$73100T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,ask} \\ = D_{corp,floor} + V_{neverdev,VSL} \\ = \$4310T + \$3000T \\ = \$7310T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} V_{neverdev,VSL} \\ = Deaths_{neverdev} \times VSL \\ = 300M \times \$10M \\ = \$3000T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages State Farm Constitutional-Ceiling Exposure Total (USD) | 0.9992 | Strong driver | > | Global Population in 2024 (of people) | -0.0435 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita (10,000 simulations) > > **Simulation Results Summary: Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$9.13 million | > | Mean (expected value) | \$8.53 million | > | Median (50th percentile) | \$8.35 million | > | Standard Deviation | \$2.38 million | > | 90% Range (5th-95th percentile) | \[\$4.91 million, \$12.7 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita > > *This exceedance probability chart shows the likelihood that Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages State Farm Constitutional-Ceiling Exposure Total: \$73.1 quadrillion > **NOTE:** > > Constitutional-ceiling exposure under State Farm v. Campbell: prosecutor base ask plus a 9:1 punitive-to-compensatory multiplier. This is exposure, not a typical award. > > **Inputs**: > > - [Corporate Damages Prosecutor Base Ask Total](#sec-corporate_damages_prosecutor_base_ask_total) 🔢: \$7.31 quadrillion > - [State Farm Constitutional-Ceiling Exposure Multiplier](#sec-corporate_damages_state_farm_ceiling_multiplier) 📊: 10x > > $$ \begin{gathered} D_{corp,StateFarm} \\ = D_{corp,ask} \times m_{StateFarm} \\ = \$7310T \times 10 \\ = \$73100T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,ask} \\ = D_{corp,floor} + V_{neverdev,VSL} \\ = \$4310T + \$3000T \\ = \$7310T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} V_{neverdev,VSL} \\ = Deaths_{neverdev} \times VSL \\ = 300M \times \$10M \\ = \$3000T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages State Farm Constitutional-Ceiling Exposure Total** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Prosecutor Base Ask Total (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages State Farm Constitutional-Ceiling Exposure Total (10,000 simulations) > > **Simulation Results Summary: Corporate Damages State Farm Constitutional-Ceiling Exposure Total** > > | Statistic | Value | > |:--------------------------------|------------------------------------------:| > | Baseline (deterministic) | \$73.1 quadrillion | > | Mean (expected value) | \$68.2 quadrillion | > | Median (50th percentile) | \$66.9 quadrillion | > | Standard Deviation | \$19 quadrillion | > | 90% Range (5th-95th percentile) | \[\$39.2 quadrillion, \$102 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages State Farm Constitutional-Ceiling Exposure Total; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages State Farm Constitutional-Ceiling Exposure Total > > *This exceedance probability chart shows the likelihood that Corporate Damages State Farm Constitutional-Ceiling Exposure Total will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Strict Floor Per Capita: \$538,219 > **NOTE:** > > Strict non-duplicative corporate damages floor per living human. > > **Inputs**: > > - [Corporate Damages Strict Floor Total](#sec-corporate_damages_strict_floor_total) 🔢: \$4.31 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} D_{corp,floor,pc} \\ = \frac{D_{corp,floor}}{Pop_{global}} \\ = \frac{\$4310T}{8B} \\ = \$538K \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Strict Floor Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Strict Floor Total (USD) | 0.9992 | Strong driver | > | Global Population in 2024 (of people) | -0.0406 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Strict Floor Per Capita (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Strict Floor Per Capita** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | \$538,219 | > | Mean (expected value) | \$482,700 | > | Median (50th percentile) | \$468,459 | > | Standard Deviation | \$144,437 | > | 90% Range (5th-95th percentile) | \[\$270,512, \$743,608\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Strict Floor Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Strict Floor Per Capita > > *This exceedance probability chart shows the likelihood that Corporate Damages Strict Floor Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Strict Floor Total: \$4.31 quadrillion > **NOTE:** > > Strict non-duplicative corporate damages floor: war-death VSL, existing-drug efficacy-lag VSL, property and environmental destruction, excess military spending above the 1900 freeze, and the Pentagon FCA-style penalty increment. > > **Inputs**: > > - [Corporate Damages War Deaths VSL](#sec-corporate_damages_war_deaths_vsl) 🔢: \$3.1 quadrillion > - [Corporate Damages Efficacy Lag Deaths VSL](#sec-corporate_damages_efficacy_lag_deaths_vsl) 🔢: \$1.02 quadrillion > - [Corporate Damages Property Plus Environmental Destruction](#sec-corporate_damages_property_environmental_destruction) 🔢: \$50 trillion > - [Excess Military Spending Above 1900 Freeze](#sec-war_trial_redirect_excess_military_spending_above_1900_freeze) 🔢: \$135 trillion > - [Corporate Damages Pentagon FCA Penalty Increment](#sec-corporate_damages_pentagon_fca_penalty_increment) 🔢: \$4.92 trillion > > $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Strict Floor Total** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages War Deaths VSL (USD) | 0.7308 | Strong driver | > | Corporate Damages Efficacy Lag Deaths VSL (USD) | 0.4003 | Moderate driver | > | Corporate Damages Property Plus Environmental Destruction (USD) | 0.0077 | Minimal effect | > | Excess Military Spending Above 1900 Freeze (USD) | 0.0011 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Strict Floor Total (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Strict Floor Total** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$4.31 quadrillion | > | Mean (expected value) | \$3.86 quadrillion | > | Median (50th percentile) | \$3.75 quadrillion | > | Standard Deviation | \$1.15 quadrillion | > | 90% Range (5th-95th percentile) | \[\$2.16 quadrillion, \$5.94 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Strict Floor Total; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Strict Floor Total > > *This exceedance probability chart shows the likelihood that Corporate Damages Strict Floor Total will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Treble-Style Exposure Per Capita: \$2.74 million > **NOTE:** > > Treble-style exposure per living human under the False Claims Act-style corporate penalty analogy. > > **Inputs**: > > - [Corporate Damages Treble-Style Exposure Total](#sec-corporate_damages_treble_exposure_total) 🔢: \$21.9 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} D_{corp,treble,pc} \\ = \frac{D_{corp,treble}}{Pop_{global}} \\ = \frac{\$21900T}{8B} \\ = \$2.74M \end{gathered} $$ where: $$ \begin{gathered} D_{corp,treble} \\ = D_{corp,ask} \times m_{FCA} \\ = \$7310T \times 3 \\ = \$21900T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,ask} \\ = D_{corp,floor} + V_{neverdev,VSL} \\ = \$4310T + \$3000T \\ = \$7310T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} V_{neverdev,VSL} \\ = Deaths_{neverdev} \times VSL \\ = 300M \times \$10M \\ = \$3000T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Treble-Style Exposure Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Treble-Style Exposure Total (USD) | 0.9992 | Strong driver | > | Global Population in 2024 (of people) | -0.0435 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Treble-Style Exposure Per Capita (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Treble-Style Exposure Per Capita** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$2.74 million | > | Mean (expected value) | \$2.56 million | > | Median (50th percentile) | \$2.51 million | > | Standard Deviation | \$714,940 | > | 90% Range (5th-95th percentile) | \[\$1.47 million, \$3.82 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Treble-Style Exposure Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Treble-Style Exposure Per Capita > > *This exceedance probability chart shows the likelihood that Corporate Damages Treble-Style Exposure Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages Treble-Style Exposure Total: \$21.9 quadrillion > **NOTE:** > > Treble-style exposure if the prosecutor base ask is multiplied under a False Claims Act-style corporate penalty analogy. > > **Inputs**: > > - [Corporate Damages Prosecutor Base Ask Total](#sec-corporate_damages_prosecutor_base_ask_total) 🔢: \$7.31 quadrillion > - [Corporate Analog False Claims Act Treble Multiplier](#sec-corporate_analog_false_claims_treble_multiplier) 📊: 3x > > $$ \begin{gathered} D_{corp,treble} \\ = D_{corp,ask} \times m_{FCA} \\ = \$7310T \times 3 \\ = \$21900T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,ask} \\ = D_{corp,floor} + V_{neverdev,VSL} \\ = \$4310T + \$3000T \\ = \$7310T \end{gathered} $$ where: $$ \begin{gathered} D_{corp,floor} \\ = V_{war,VSL} + V_{lag,VSL} + D_{property+env} \\ + Spending_{mil,excess1900} + Penalty_{pentagon,FCA} \\ = \$3100T + \$1020T + \$50T + \$135T + \$4.92T \\ = \$4310T \end{gathered} $$ where: $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ where: $$ \begin{gathered} V_{lag,VSL} \\ = Deaths_{lag,total} \times VSL \\ = 102M \times \$10M \\ = \$1020T \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} D_{property+env} \\ = D_{property} + D_{env} \\ = \$45T + \$5T \\ = \$50T \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ where: $$ \begin{gathered} Penalty_{pentagon,FCA} \\ = Exposure_{pentagon,FCA} - Funds_{pentagon,unaccounted} \\ = \$7.38T - \$2.46T \\ = \$4.92T \end{gathered} $$ where: $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ where: $$ \begin{gathered} V_{neverdev,VSL} \\ = Deaths_{neverdev} \times VSL \\ = 300M \times \$10M \\ = \$3000T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages Treble-Style Exposure Total** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Corporate Damages Prosecutor Base Ask Total (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages Treble-Style Exposure Total (10,000 simulations) > > **Simulation Results Summary: Corporate Damages Treble-Style Exposure Total** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$21.9 quadrillion | > | Mean (expected value) | \$20.5 quadrillion | > | Median (50th percentile) | \$20.1 quadrillion | > | Standard Deviation | \$5.71 quadrillion | > | 90% Range (5th-95th percentile) | \[\$11.7 quadrillion, \$30.6 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages Treble-Style Exposure Total; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages Treble-Style Exposure Total > > *This exceedance probability chart shows the likelihood that Corporate Damages Treble-Style Exposure Total will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Corporate Damages War Deaths VSL: \$3.1 quadrillion > **NOTE:** > > Corporate-defendant wrongful-death valuation for war deaths since 1900 using the standard value of a statistical life. > > **Inputs**: > > - [Total War and Conflict Deaths Since 1900](#sec-war_deaths_since_1900): 310 million deaths (95% CI: 200 million deaths - 340 million deaths) > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} V_{war,VSL} \\ = Deaths_{war,1900} \times VSL \\ = 310M \times \$10M \\ = \$3100T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Corporate Damages War Deaths VSL** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Value of Statistical Life (USD) | 0.8668 | Strong driver | > | Total War and Conflict Deaths Since 1900 (deaths) | 0.4791 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Corporate Damages War Deaths VSL (10,000 simulations) > > **Simulation Results Summary: Corporate Damages War Deaths VSL** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$3.1 quadrillion | > | Mean (expected value) | \$2.67 quadrillion | > | Median (50th percentile) | \$2.58 quadrillion | > | Standard Deviation | \$844 trillion | > | 90% Range (5th-95th percentile) | \[\$1.43 quadrillion, \$4.21 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Corporate Damages War Deaths VSL; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Corporate Damages War Deaths VSL > > *This exceedance probability chart shows the likelihood that Corporate Damages War Deaths VSL will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Military Spending in Government Clinical Trial Years: 37,778 years > **NOTE:** > > Cumulative military spending since 1913 expressed in equivalent years of government clinical trial spending (\$170T / \$4.5B per year) > > **Inputs**: > > - [Cumulative Military Spending (Fed Era)](#sec-cumulative_military_spending_fed_era): \$170 trillion > - [Annual Global Government Spending on Clinical Trials](#sec-global_government_clinical_trials_spending_annual) 📊: \$4.5 billion (95% CI: \$3 billion - \$6 billion) > > $$ \begin{gathered} Years_{mil \to trials,gov} \\ = \frac{Spending_{mil,cum,fed}}{Spending_{trials,gov}} \\ = \frac{\$170T}{\$4.5B} \\ = 37{,}800 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Military Spending in Government Clinical Trial Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Government Spending on Clinical Trials (USD) | -0.9789 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Military Spending in Government Clinical Trial Years (10,000 simulations) > > **Simulation Results Summary: Military Spending in Government Clinical Trial Years** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 37,778 | > | Mean (expected value) | 39,668 | > | Median (50th percentile) | 38,840 | > | Standard Deviation | 7,844 | > | 90% Range (5th-95th percentile) | \[28,333, 55,502\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Military Spending in Government Clinical Trial Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Military Spending in Government Clinical Trial Years > > *This exceedance probability chart shows the likelihood that Military Spending in Government Clinical Trial Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Combination Therapy Exploration Time (Current): 13.7 million years > **NOTE:** > > Years to test all pairwise drug combinations at current trial capacity. Combination therapy is standard in oncology, HIV, cardiology. > > **Inputs**: > > - [Combination Therapy Space](#sec-combination_therapy_disease_space) 🔢: 45.1 billion combinations > - [Current Global Clinical Trials per Year](#sec-current_trials_per_year) 📊: 3,300 trials/year (95% CI: 2,640 trials/year - 3,960 trials/year) > > $$ \begin{gathered} T_{explore,combo} \\ = \frac{Space_{combo}}{Trials_{ann,curr}} \\ = \frac{45.1B}{3{,}300} \\ = 13.7M \end{gathered} $$ where: $$ \begin{gathered} Space_{combo} \\ = N_{combo} \times N_{diseases,trial} \\ = 45.1M \times 1{,}000 \\ = 45.1B \end{gathered} $$ where: $$ N_{combo} = \frac{N_{safe} \cdot (N_{safe} - 1)}{2} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Combination Therapy Exploration Time (Current)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Combination Therapy Space (combinations) | 0.9556 | Strong driver | > | Current Global Clinical Trials per Year (trials/year) | -0.2867 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Combination Therapy Exploration Time (Current) (10,000 simulations) > > **Simulation Results Summary: Combination Therapy Exploration Time (Current)** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 13.7 million | > | Mean (expected value) | 14.1 million | > | Median (50th percentile) | 13.5 million | > | Standard Deviation | 4.76 million | > | 90% Range (5th-95th percentile) | \[7.45 million, 22.6 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Combination Therapy Exploration Time (Current); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Combination Therapy Exploration Time (Current) > > *This exceedance probability chart shows the likelihood that Combination Therapy Exploration Time (Current) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Known Safe Exploration Time (Current): 2,879 years > **NOTE:** > > Years to test all known safe drug-disease combinations at current global trial capacity > > **Inputs**: > > - [Possible Drug-Disease Combinations](#sec-drug_disease_combinations_possible) 🔢: 9.5 million combinations > - [Current Global Clinical Trials per Year](#sec-current_trials_per_year) 📊: 3,300 trials/year (95% CI: 2,640 trials/year - 3,960 trials/year) > > $$ \begin{gathered} T_{explore,safe} \\ = \frac{N_{combos}}{Trials_{ann,curr}} \\ = \frac{9.5M}{3{,}300} \\ = 2{,}880 \end{gathered} $$ where: $$ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Known Safe Exploration Time (Current)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Possible Drug-Disease Combinations (combinations) | 0.8940 | Strong driver | > | Current Global Clinical Trials per Year (trials/year) | -0.4473 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Known Safe Exploration Time (Current) (10,000 simulations) > > **Simulation Results Summary: Known Safe Exploration Time (Current)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 2,879 | > | Mean (expected value) | 2,904 | > | Median (50th percentile) | 2,843 | > | Standard Deviation | 628 | > | 90% Range (5th-95th percentile) | \[1,976, 4,041\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Known Safe Exploration Time (Current); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Known Safe Exploration Time (Current) > > *This exceedance probability chart shows the likelihood that Known Safe Exploration Time (Current) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Current Patient Participation Rate in Clinical Trials: 0.0792% > **NOTE:** > > Current patient participation rate in clinical trials (0.08% = 1.9M participants / 2.4B disease patients) > > **Inputs**: > > - [Annual Global Clinical Trial Participants](#sec-current_trial_slots_available) 📊: 1.9 million patients/year (95% CI: 1.5 million patients/year - 2.3 million patients/year) > - [Global Population with Chronic Diseases](#sec-current_disease_patients_global) 📊: 2.4 billion people (95% CI: 2 billion people - 2.8 billion people) > > $$ \begin{gathered} Rate_{part} \\ = \frac{Slots_{curr}}{N_{patients}} \\ = \frac{1.9M}{2.4B} \\ = 0.0792\% \end{gathered} $$ > > **Methodology**:^([3](../references.llms.md#ref-clinical-trial-patient-participation-rate)) > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Current Patient Participation Rate in Clinical Trials** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Clinical Trial Participants (patients/year) | 0.7814 | Strong driver | > | Global Population with Chronic Diseases (people) | -0.6138 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Current Patient Participation Rate in Clinical Trials (10,000 simulations) > > **Simulation Results Summary: Current Patient Participation Rate in Clinical Trials** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | 0.0792% | > | Mean (expected value) | 0.0795% | > | Median (50th percentile) | 0.0789% | > | Standard Deviation | 0.0104% | > | 90% Range (5th-95th percentile) | \[0.0631%, 0.0975%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Current Patient Participation Rate in Clinical Trials; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Current Patient Participation Rate in Clinical Trials > > *This exceedance probability chart shows the likelihood that Current Patient Participation Rate in Clinical Trials will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Current Trajectory Average Income at Year 15: \$18,714 > **NOTE:** > > Average income (GDP per capita) at year 15 under current trajectory. > > **Inputs**: > > - [Current Trajectory GDP at Year 15](#sec-current_trajectory_gdp_year_15) 🔢: \$167 trillion > - [Global Population 2040 (Projected)](#sec-global_population_2040_projected) 📊: 8.9 billion of people > > $$ \begin{gathered} \bar{y}_{base,15} \\ = \frac{GDP_{base,15}}{Pop_{2040}} \\ = \frac{\$167T}{8.9B} \\ = \$18.7K \end{gathered} $$ where: $$ GDP_{base,15} = GDP_{global} \times (1 + g_{base})^{15} $$ \#### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Current Trajectory Average Income at Year 15 is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.871e-08) > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$18,714 | > | Mean (expected value) | \$18,714 | > | Median (50th percentile) | \$18,714 | > | Standard Deviation | \$3.64e-12 | > | 90% Range (5th-95th percentile) | \[\$18,714, \$18,714\] | > > #### Exceedance Probability > > **Exceedance note:** Current Trajectory Average Income at Year 15 collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.871e-08) > > Approximate deterministic value: **\$18,714** ### Current Trajectory Average Income at Year 20: \$20,483 > **NOTE:** > > Average income (GDP per capita) at year 20 under current trajectory trajectory. > > **Inputs**: > > - [Current Trajectory GDP at Year 20](#sec-current_trajectory_gdp_year_20) 🔢: \$188 trillion > - [Global Population 2045 (Projected)](#sec-global_population_2045_projected) 📊: 9.2 billion of people > > $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ \#### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Current Trajectory Average Income at Year 20 is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.048e-08) > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$20,483 | > | Mean (expected value) | \$20,483 | > | Median (50th percentile) | \$20,483 | > | Standard Deviation | \$3.64e-12 | > | 90% Range (5th-95th percentile) | \[\$20,483, \$20,483\] | > > #### Exceedance Probability > > **Exceedance note:** Current Trajectory Average Income at Year 20 collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.048e-08) > > Approximate deterministic value: **\$20,483** ### Current Trajectory Cumulative Lifetime Income (Per Capita): \$903,908 > **NOTE:** > > Cumulative per-capita income over an average remaining lifespan under current trajectory baseline trajectory. Uses the implied per-capita baseline CAGR from 2025 to 2045. > > **Inputs**: > > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > - [Current Trajectory Average Income at Year 20](#sec-current_trajectory_avg_income_year_20) 🔢: \$20,483 > - [Average Remaining Years (Median Person)](#sec-global_avg_remaining_years) 🔢: 42.9 years > > $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Current Trajectory Cumulative Lifetime Income (Per Capita)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Average Remaining Years (Median Person) (years) | 0.9882 | Strong driver | > | Global Average Income (2025 Baseline) (USD) | -0.0400 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Current Trajectory Cumulative Lifetime Income (Per Capita) (10,000 simulations) > > **Simulation Results Summary: Current Trajectory Cumulative Lifetime Income (Per Capita)** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$903,908 | > | Mean (expected value) | \$917,975 | > | Median (50th percentile) | \$907,227 | > | Standard Deviation | \$59,848 | > | 90% Range (5th-95th percentile) | \[\$815,233, \$1.03 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Current Trajectory Cumulative Lifetime Income (Per Capita); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Current Trajectory Cumulative Lifetime Income (Per Capita) > > *This exceedance probability chart shows the likelihood that Current Trajectory Cumulative Lifetime Income (Per Capita) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Current Trajectory GDP at Year 15: \$167 trillion > **NOTE:** > > Global GDP at year 15 under status-quo current trajectory growth. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > > $$ GDP_{base,15} = GDP_{global} \times (1 + g_{base})^{15} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Current Trajectory GDP at Year 15 is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.666e+02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$167 trillion | > | Mean (expected value) | \$167 trillion | > | Median (50th percentile) | \$167 trillion | > | Standard Deviation | \$0.031 | > | 90% Range (5th-95th percentile) | \[\$167 trillion, \$167 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** Current Trajectory GDP at Year 15 collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.666e+02) > > Approximate deterministic value: **\$167 trillion** ### Current Trajectory GDP at Year 20: \$188 trillion > **NOTE:** > > Global GDP at year 20 under status-quo current trajectory growth. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > > $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Current Trajectory GDP at Year 20 is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.884e+02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$188 trillion | > | Mean (expected value) | \$188 trillion | > | Median (50th percentile) | \$188 trillion | > | Standard Deviation | \$0.031 | > | 90% Range (5th-95th percentile) | \[\$188 trillion, \$188 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** Current Trajectory GDP at Year 20 collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.884e+02) > > Approximate deterministic value: **\$188 trillion** ### Median After-Tax Consumable Income, Status Quo (Year 20): \$3,033 > **NOTE:** > > Median after-tax consumable income at year 20 under the status quo: the world more or less as trended. Mean income grows at the baseline rate, the military share drifts up at its measured SIPRI-vs-GDP differential, the median’s share holds (best guess zero erosion, range covers both directions), so the median grows roughly with GDP per capita. v1’s compounding destructive share and assumed erosion made the median mysteriously die against 35 years of contrary observed history; that branch is gone. The collapse scenario (extraction crosses the rational-crime threshold and GDP craters with the median) is narrated separately, not smuggled into this baseline. > > **Inputs**: > > - [Current Trajectory Average Income at Year 20](#sec-current_trajectory_avg_income_year_20) 🔢: \$20,483 > - [Military Share of Global GDP](#sec-global_military_share_of_gdp) 🔢: 2.37% > - [Military Spending Real CAGR (10-Year)](#sec-global_military_spending_real_cagr_10yr) 📊: 3.4% > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Global Median-to-Mean Income Ratio](#sec-global_median_to_mean_income_ratio) 🔢: 0.203:1 > - [Median Share Erosion Rate (Annual)](#sec-global_median_share_erosion_rate_annual): 0% (95% CI: -0.5% - 0.78%) > - [Effective Tax Rate on Median Earner](#sec-global_effective_tax_rate_median): 25% > > $$ \begin{gathered} \tilde{m}_{base,20} \\ = \bar{y}_{base,20} \times (1 - s_{mil} \times \left(\frac{1+g_{mil,10yr}}{1+g_{base}}\right)^{20}) \times \rho_{med} \times (1 - e_{med})^{20} \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ where: $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Median After-Tax Consumable Income, Status Quo (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Median-to-Mean Income Ratio (ratio) | 0.8841 | Strong driver | > | Median Share Erosion Rate (Annual) (rate) | -0.4671 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Median After-Tax Consumable Income, Status Quo (Year 20) (10,000 simulations) > > **Simulation Results Summary: Median After-Tax Consumable Income, Status Quo (Year 20)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$3,033 | > | Mean (expected value) | \$3,031 | > | Median (50th percentile) | \$3,017 | > | Standard Deviation | \$399 | > | 90% Range (5th-95th percentile) | \[\$2,402, \$3,716\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Median After-Tax Consumable Income, Status Quo (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Median After-Tax Consumable Income, Status Quo (Year 20) > > *This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Status Quo (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Decentralized Congress Savings per Citizen per Year: \$1,391 > **NOTE:** > > Annual savings per citizen from putting the zombie policies to direct evidence-attached votes: tariffs, corporate welfare, agricultural and fossil subsidies, the policies that survive only by never being asked about. > > **Inputs**: > > - [Tariff Cost (GDP Loss)](#sec-us_gov_waste_tariffs) 📊: \$160 billion (95% CI: \$90 billion - \$250 billion) > - [Corporate Welfare Waste](#sec-us_gov_waste_corporate_welfare) 📊: \$181 billion (95% CI: \$150 billion - \$220 billion) > - [Agricultural Subsidies Deadweight Loss](#sec-us_gov_waste_agricultural_subsidies) 📊: \$75 billion (95% CI: \$50 billion - \$120 billion) > - [Fossil Fuel Subsidies (Explicit)](#sec-us_gov_waste_fossil_fuel_subsidies) 📊: \$50 billion (95% CI: \$30 billion - \$80 billion) > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} Savings_{ann} \\ = (\text{TARIFFS} + \text{CORPORATE\_WELFARE} \\ + \text{AG\_SUBSIDIES} \\ + \text{FOSSIL\_SUBSIDIES}) / \text{US\_POPULATION} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Decentralized Congress Savings per Citizen per Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Tariff Cost (GDP Loss) (USD) | 0.8185 | Strong driver | > | Agricultural Subsidies Deadweight Loss (USD) | 0.3820 | Moderate driver | > | Corporate Welfare Waste (USD) | 0.3393 | Moderate driver | > | Fossil Fuel Subsidies (Explicit) (USD) | 0.2437 | Weak driver | > | US Population in 2024 (people) | -0.0615 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Decentralized Congress Savings per Citizen per Year (10,000 simulations) > > **Simulation Results Summary: Decentralized Congress Savings per Citizen per Year** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$1,391 | > | Mean (expected value) | \$1,395 | > | Median (50th percentile) | \$1,390 | > | Standard Deviation | \$164 | > | 90% Range (5th-95th percentile) | \[\$1,132, \$1,669\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Decentralized Congress Savings per Citizen per Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Decentralized Congress Savings per Citizen per Year > > *This exceedance probability chart shows the likelihood that Decentralized Congress Savings per Citizen per Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Military Activist-Stake Cost (Realistic Entry): \$48.4 billion > **NOTE:** > > Realistic entry cost: capital to take an activist (non-control) equity position across all major Western military contractors, bought near market price with no control premium. Board influence comes from the financial argument plus the index-fund votes, not from outright control. This capital buys shares the fund keeps, so the true net cost is far lower than this gross figure. Contrast with the buy-outright ceiling (DEFENSE_TAKEOVER_COST_TOTAL). > > **Inputs**: > > - [US Military Primes Market Cap](#sec-defense_primes_market_cap_us) 📊: \$836 billion > - [Allied Military Primes Market Cap](#sec-defense_primes_market_cap_allied) 📊: \$132 billion > - [Activist Stake Fraction](#sec-influence_activist_stake_fraction): 0.05:1 (95% CI: 0.01:1 - 0.1:1) > > $$ \begin{gathered} C_{activist} \\ = f_{activist} \times (MarketCap_{US} + MarketCap_{allied}) \\ = 0.05 \times (\$836B + \$132B) \\ = \$48.4B \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** Military Activist-Stake Cost (Realistic Entry) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 4.840e-02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$48.4 billion | > | Mean (expected value) | \$48.4 billion | > | Median (50th percentile) | \$48.4 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$48.4 billion, \$48.4 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Military Activist-Stake Cost (Realistic Entry) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 4.840e-02) > > Approximate deterministic value: **\$48.4 billion** ### Military Activist-Stake Cost as Share of Global Investable Assets: 0.0159% > **NOTE:** > > Activist-stake entry cost across the defense primes as a share of total global investable assets. The realistic-path floor of the cost-in-context range, well below the buy-outright ceiling. > > **Inputs**: > > - [Military Activist-Stake Cost (Realistic Entry)](#sec-defense_takeover_cost_activist) 🔢: \$48.4 billion > - [Global Investable Financial Assets](#sec-global_investable_assets) 📊: \$305 trillion > > $$ \begin{gathered} C_{activist}/A_{investable} \\ = \frac{C_{activist}}{Assets_{invest}} \\ = \frac{\$48.4B}{\$305T} \\ = 0.0159\% \end{gathered} $$ where: $$ \begin{gathered} C_{activist} \\ = f_{activist} \times (MarketCap_{US} + MarketCap_{allied}) \\ = 0.05 \times (\$836B + \$132B) \\ = \$48.4B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** Military Activist-Stake Cost as Share of Global Investable Assets is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | 0.0159% | > | Mean (expected value) | 0.0159% | > | Median (50th percentile) | 0.0159% | > | Standard Deviation | 2.71e-18% | > | 90% Range (5th-95th percentile) | \[0.0159%, 0.0159%\] | > > #### Exceedance Probability > > **Exceedance note:** Military Activist-Stake Cost as Share of Global Investable Assets collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **0.0159%** ### Military Takeover Cost per Human: \$109 > **NOTE:** > > Per-person cost of the military takeover distributed across global population > > **Inputs**: > > - [Military Takeover Cost (Outright-Control Ceiling)](#sec-defense_takeover_cost_total) 🔢: \$873 billion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} C_{takeover,pp} \\ = \frac{C_{takeover}}{Pop_{global}} \\ = \frac{\$873B}{8B} \\ = \$109 \end{gathered} $$ where: $$ \begin{gathered} C_{takeover} \\ = (MarketCap_{US} \\ + MarketCap_{allied}) \times f_{control} \times m_{premium} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Military Takeover Cost per Human** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Population in 2024 (of people) | -0.9999 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Military Takeover Cost per Human (10,000 simulations) > > **Simulation Results Summary: Military Takeover Cost per Human** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | \$109 | > | Mean (expected value) | \$109 | > | Median (50th percentile) | \$109 | > | Standard Deviation | \$1.33 | > | 90% Range (5th-95th percentile) | \[\$107, \$111\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Military Takeover Cost per Human; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Military Takeover Cost per Human > > *This exceedance probability chart shows the likelihood that Military Takeover Cost per Human will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Military Takeover Cost (Outright-Control Ceiling): \$873 billion > **NOTE:** > > UPPER-BOUND cost to acquire outright controlling stakes (50.1%) in all major Western military contractors, including the acquisition premium. This is the buy-it-outright ceiling, not the expected entry cost: the realistic path is an activist stake (DEFENSE_TAKEOVER_COST_ACTIVIST) plus index-fund votes, which costs far less. Headline only as a worst case. > > **Inputs**: > > - [US Military Primes Market Cap](#sec-defense_primes_market_cap_us) 📊: \$836 billion > - [Allied Military Primes Market Cap](#sec-defense_primes_market_cap_allied) 📊: \$132 billion > - [Control Fraction](#sec-defense_takeover_control_fraction): 0.501:1 > - [Acquisition Premium Multiplier](#sec-defense_takeover_acquisition_premium): 1.8x (95% CI: 1.5x - 3x) > > $$ \begin{gathered} C_{takeover} \\ = (MarketCap_{US} \\ + MarketCap_{allied}) \times f_{control} \times m_{premium} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** Military Takeover Cost (Outright-Control Ceiling) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 8.729e-01) > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$873 billion | > | Mean (expected value) | \$873 billion | > | Median (50th percentile) | \$873 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$873 billion, \$873 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Military Takeover Cost (Outright-Control Ceiling) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 8.729e-01) > > Approximate deterministic value: **\$873 billion** ### Loving Takeover ROI (Income Only): 4.8kx > **NOTE:** > > Conditional return multiple on the per-human takeover contribution, counting lifetime income gains only: the treaty trajectory’s per-capita lifetime income gain divided by the per-human cost of the outright-control ceiling. Conditional on campaign success; excludes the dollar value of the added healthy years (see the blend variant). > > **Inputs**: > > - [Treaty Trajectory Lifetime Income Gain (Per Capita)](#sec-treaty_trajectory_lifetime_income_gain_per_capita) 🔢: \$518,879 > - [Military Takeover Cost per Human](#sec-defense_takeover_cost_per_human) 🔢: \$109 > > $$ \begin{gathered} ROI_{takeover,income} \\ = \frac{\Delta Y_{lifetime,treaty}}{C_{takeover,pp}} \\ = \frac{\$519K}{\$109} \\ = 4{,}760 \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} C_{takeover,pp} \\ = \frac{C_{takeover}}{Pop_{global}} \\ = \frac{\$873B}{8B} \\ = \$109 \end{gathered} $$ where: $$ \begin{gathered} C_{takeover} \\ = (MarketCap_{US} \\ + MarketCap_{allied}) \times f_{control} \times m_{premium} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Loving Takeover ROI (Income Only)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Lifetime Income Gain (Per Capita) (USD) | 0.9992 | Strong driver | > | Military Takeover Cost per Human (USD) | -0.0339 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Loving Takeover ROI (Income Only) (10,000 simulations) > > **Simulation Results Summary: Loving Takeover ROI (Income Only)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 4.8kx | > | Mean (expected value) | 4.9kx | > | Median (50th percentile) | 4.8kx | > | Standard Deviation | 1.7kx | > | 90% Range (5th-95th percentile) | \[2.0kx, 7.9kx\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Loving Takeover ROI (Income Only); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Loving Takeover ROI (Income Only) > > *This exceedance probability chart shows the likelihood that Loving Takeover ROI (Income Only) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Loving Takeover ROI (Income + Health): 26.9kx > **NOTE:** > > Conditional return multiple on the per-human takeover contribution, counting lifetime income gains plus valued healthy-life gains (the blended personal upside) divided by the per-human cost of the outright-control ceiling. Conditional on campaign success. > > **Inputs**: > > - [Treaty Personal Upside (Income + Health)](#sec-treaty_personal_upside_income_plus_health) 🔢: \$2.93 million > - [Military Takeover Cost per Human](#sec-defense_takeover_cost_per_human) 🔢: \$109 > > $$ \begin{gathered} ROI_{takeover,income+health} \\ = \frac{Upside_{income+health,treaty}}{C_{takeover,pp}} \\ = \frac{\$2.93M}{\$109} \\ = 26{,}900 \end{gathered} $$ where: $$ \begin{gathered} Upside_{income+health,treaty} \\ = \Delta Y_{lifetime,treaty} + Value_{HALE,treaty} \\ = \$519K + \$2.42M \\ = \$2.93M \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} Value_{HALE,treaty} \\ = \Delta HALE_{treaty,15} \times Value_{QALY} \\ = 16.1 \times \$150K \\ = \$2.42M \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \Delta_{HALE} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ where: $$ \begin{gathered} C_{takeover,pp} \\ = \frac{C_{takeover}}{Pop_{global}} \\ = \frac{\$873B}{8B} \\ = \$109 \end{gathered} $$ where: $$ \begin{gathered} C_{takeover} \\ = (MarketCap_{US} \\ + MarketCap_{allied}) \times f_{control} \times m_{premium} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Loving Takeover ROI (Income + Health)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Personal Upside (Income + Health) (USD/person) | 0.9993 | Strong driver | > | Military Takeover Cost per Human (USD) | -0.0279 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Loving Takeover ROI (Income + Health) (10,000 simulations) > > **Simulation Results Summary: Loving Takeover ROI (Income + Health)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 26.9kx | > | Mean (expected value) | 25.5kx | > | Median (50th percentile) | 23.6kx | > | Standard Deviation | 11.1kx | > | 90% Range (5th-95th percentile) | \[11.8kx, 45.1kx\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Loving Takeover ROI (Income + Health); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Loving Takeover ROI (Income + Health) > > *This exceedance probability chart shows the likelihood that Loving Takeover ROI (Income + Health) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Department of Peace Savings per American per Year: \$2,096 > **NOTE:** > > Annual savings per American from trimming military spending to the first-principles homeland-defense baseline: the audit’s overspend divided across the population. > > **Inputs**: > > - [Military Overspend](#sec-us_gov_waste_military_overspend) 🔢: \$702 billion > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} Savings_{ann} \\ = \frac{W_{military}}{Pop_{US}} \\ = \frac{\$702B}{335M} \\ = \$2.1K \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Department of Peace Savings per American per Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-------------------------------|------------------------:|:---------------| > | Military Overspend (USD) | 0.9577 | Strong driver | > | US Population in 2024 (people) | -0.2773 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Department of Peace Savings per American per Year (10,000 simulations) > > **Simulation Results Summary: Department of Peace Savings per American per Year** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$2,096 | > | Mean (expected value) | \$2,093 | > | Median (50th percentile) | \$2,094 | > | Standard Deviation | \$54 | > | 90% Range (5th-95th percentile) | \[\$1,999, \$2,180\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Department of Peace Savings per American per Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Department of Peace Savings per American per Year > > *This exceedance probability chart shows the likelihood that Department of Peace Savings per American per Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Year Destructive Economy Reaches 25% of GDP: 2033 > **NOTE:** > > Calendar year when the destructive economy (military + cybercrime) reaches 25% of GDP at current growth rates. Historical precedent suggests societies become unstable when extraction rates exceed 20-30% of economic output. > > **Inputs**: > > - [Destructive Economy Base Year](#sec-destructive_economy_base_year): 2025 > - [Destructive Economy as % of GDP](#sec-global_destructive_economy_pct_gdp) 🔢: 11.5% > - [Cybercrime Cost CAGR](#sec-global_cybercrime_cagr) 📊: 15% > - [Military Spending Real CAGR (10-Year)](#sec-global_military_spending_real_cagr_10yr) 📊: 3.4% > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Global Destructive Economy (2025)](#sec-global_destructive_economy_annual_2025) 🔢: \$13.2 trillion > - [Global Cybercrime Costs (2025)](#sec-global_cybercrime_cost_annual_2025) 📊: \$10.5 trillion > > $$ \begin{gathered} Y_{25\%} \\ = Y_0 \\ + \frac{\ln\left(0.25 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Year Destructive Economy Reaches 25% of GDP is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.033e-09) > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 2033 | > | Mean (expected value) | 2033 | > | Median (50th percentile) | 2033 | > | Standard Deviation | 0 | > | 90% Range (5th-95th percentile) | \[2033, 2033\] | > > #### Exceedance Probability > > **Exceedance note:** Year Destructive Economy Reaches 25% of GDP collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.033e-09) > > Approximate deterministic value: **2033** ### Year Destructive Economy Reaches 35% of GDP (Terminal Parasitic Load): 2037 > **NOTE:** > > Calendar year when the destructive economy (military + cybercrime) reaches 35% of GDP at current growth rates. Historical evidence from the Soviet Union, Yugoslavia, Argentina, and Zimbabwe shows that total extractive burdens of 35-45% consistently trigger self-reinforcing death spirals. This is the empirically-derived terminal parasitic load threshold. > > **Inputs**: > > - [Destructive Economy Base Year](#sec-destructive_economy_base_year): 2025 > - [Destructive Economy as % of GDP](#sec-global_destructive_economy_pct_gdp) 🔢: 11.5% > - [Cybercrime Cost CAGR](#sec-global_cybercrime_cagr) 📊: 15% > - [Military Spending Real CAGR (10-Year)](#sec-global_military_spending_real_cagr_10yr) 📊: 3.4% > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Global Destructive Economy (2025)](#sec-global_destructive_economy_annual_2025) 🔢: \$13.2 trillion > - [Global Cybercrime Costs (2025)](#sec-global_cybercrime_cost_annual_2025) 📊: \$10.5 trillion > > $$ \begin{gathered} Y_{35\%} \\ = Y_0 \\ + \frac{\ln\left(0.35 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Year Destructive Economy Reaches 35% of GDP (Terminal Parasitic Load) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.037e-09) > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 2037 | > | Mean (expected value) | 2037 | > | Median (50th percentile) | 2037 | > | Standard Deviation | 0 | > | 90% Range (5th-95th percentile) | \[2037, 2037\] | > > #### Exceedance Probability > > **Exceedance note:** Year Destructive Economy Reaches 35% of GDP (Terminal Parasitic Load) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.037e-09) > > Approximate deterministic value: **2037** ### Year Destructive Economy Reaches 50% of GDP: 2040 > **NOTE:** > > Calendar year when the destructive economy (military + cybercrime) reaches 50% of GDP at current growth rates. At that point, half of all economic activity is destructive, so stealing starts to beat creating for individuals, firms, and states because whatever gets created gets looted fast enough to kill productive investment. > > **Inputs**: > > - [Destructive Economy Base Year](#sec-destructive_economy_base_year): 2025 > - [Destructive Economy as % of GDP](#sec-global_destructive_economy_pct_gdp) 🔢: 11.5% > - [Cybercrime Cost CAGR](#sec-global_cybercrime_cagr) 📊: 15% > - [Military Spending Real CAGR (10-Year)](#sec-global_military_spending_real_cagr_10yr) 📊: 3.4% > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Global Destructive Economy (2025)](#sec-global_destructive_economy_annual_2025) 🔢: \$13.2 trillion > - [Global Cybercrime Costs (2025)](#sec-global_cybercrime_cost_annual_2025) 📊: \$10.5 trillion > > $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Year Destructive Economy Reaches 50% of GDP is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.040e-09) > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 2040 | > | Mean (expected value) | 2040 | > | Median (50th percentile) | 2040 | > | Standard Deviation | 0 | > | 90% Range (5th-95th percentile) | \[2040, 2040\] | > > #### Exceedance Probability > > **Exceedance note:** Year Destructive Economy Reaches 50% of GDP collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.040e-09) > > Approximate deterministic value: **2040** ### Total Annual Pragmatic Trial Platform Operational Costs: \$40 million > **NOTE:** > > Total annual pragmatic trial platform operational costs (sum of all components: platform + staff + infra + regulatory + community) > > **Inputs**: > > - [Pragmatic Trial Platform Maintenance Costs](#sec-dfda_opex_platform_maintenance): \$15 million (95% CI: \$10 million - \$22 million) > - [Pragmatic Trial Platform Staff Costs](#sec-dfda_opex_staff): \$10 million (95% CI: \$7 million - \$15 million) > - [Pragmatic Trial Platform Infrastructure Costs](#sec-dfda_opex_infrastructure): \$8 million (95% CI: \$5 million - \$12 million) > - [Pragmatic Trial Platform Regulatory Coordination Costs](#sec-dfda_opex_regulatory): \$5 million (95% CI: \$3 million - \$8 million) > - [Pragmatic Trial Platform Community Support Costs](#sec-dfda_opex_community): \$2 million (95% CI: \$1 million - \$3 million) > > $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Pragmatic Trial Platform Operational Costs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Platform Maintenance Costs (USD/year) | 0.7128 | Strong driver | > | Pragmatic Trial Platform Staff Costs (USD/year) | 0.4775 | Moderate driver | > | Pragmatic Trial Platform Infrastructure Costs (USD/year) | 0.4127 | Moderate driver | > | Pragmatic Trial Platform Regulatory Coordination Costs (USD/year) | 0.2936 | Weak driver | > | Pragmatic Trial Platform Community Support Costs (USD/year) | 0.1156 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Pragmatic Trial Platform Operational Costs (10,000 simulations) > > **Simulation Results Summary: Total Annual Pragmatic Trial Platform Operational Costs** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$40 million | > | Mean (expected value) | \$39.9 million | > | Median (50th percentile) | \$39.7 million | > | Standard Deviation | \$4.09 million | > | 90% Range (5th-95th percentile) | \[\$33.5 million, \$47.1 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Pragmatic Trial Platform Operational Costs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Pragmatic Trial Platform Operational Costs > > *This exceedance probability chart shows the likelihood that Total Annual Pragmatic Trial Platform Operational Costs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual R&D Savings from Pragmatic Trials: \$40.5 billion > **NOTE:** > > Annual benefit from pragmatic trial R&D savings: trial cost reduction applied to the Phase 2/3 efficacy share of global trial spending (Phase 1 safety trials retain traditional design and cost) > > **Inputs**: > > - [Annual Global Spending on Clinical Trials](#sec-global_clinical_trials_spending_annual) 📊: \$60 billion (95% CI: \$50 billion - \$75 billion) > - [Phase 2/3 Share of Clinical Trial Costs](#sec-phase_2_3_clinical_trial_cost_pct) 📊: 69% (SE: ±5%) > - [Pragmatic Trial Cost Reduction Percentage](#sec-dfda_trial_cost_reduction_pct) 🔢: 97.7% > > $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual R&D Savings from Pragmatic Trials** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Spending on Clinical Trials (USD) | 0.8666 | Strong driver | > | Phase 2/3 Share of Clinical Trial Costs (percentage) | 0.4697 | Moderate driver | > | Pragmatic Trial Cost Reduction Percentage (percentage) | 0.1657 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual R&D Savings from Pragmatic Trials (10,000 simulations) > > **Simulation Results Summary: Annual R&D Savings from Pragmatic Trials** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$40.5 billion | > | Mean (expected value) | \$40.3 billion | > | Median (50th percentile) | \$39.6 billion | > | Standard Deviation | \$6.21 billion | > | 90% Range (5th-95th percentile) | \[\$31.4 billion, \$51.5 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual R&D Savings from Pragmatic Trials; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual R&D Savings from Pragmatic Trials > > *This exceedance probability chart shows the likelihood that Annual R&D Savings from Pragmatic Trials will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Direct Pragmatic Trial Funding Cost per DALY: \$9.62 > **NOTE:** > > Cost per DALY at direct funding level for the therapeutic space exploration period, with costs and DALYs discounted at the same standard rate. Still highly cost-effective vs bed nets. > > **Inputs**: > > - [Direct Pragmatic Trial Funding NPV (Exploration Period)](#sec-dfda_direct_funding_queue_clearance_npv) 🔢: \$483 billion > - [Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_dalys_discounted) 🔢: 50.2 billion DALYs > > $$ \begin{gathered} Cost_{direct,DALY} \\ = \frac{NPV_{direct}}{DALYs_{max}^{PV}} \\ = \frac{\$483B}{50.2B} \\ = \$9.62 \end{gathered} $$ where: $$ \begin{gathered} NPV_{direct} \\ = Funding_{trial,ref} \times (1 - (1 \\ + r_{discount})^{-T_{queue,trial}}) / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Direct Pragmatic Trial Funding Cost per DALY** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | -0.6914 | Strong driver | > | Direct Pragmatic Trial Funding NPV (Exploration Period) (USD) | 0.1929 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Direct Pragmatic Trial Funding Cost per DALY (10,000 simulations) > > **Simulation Results Summary: Direct Pragmatic Trial Funding Cost per DALY** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | \$9.62 | > | Mean (expected value) | \$11 | > | Median (50th percentile) | \$7.91 | > | Standard Deviation | \$10 | > | 90% Range (5th-95th percentile) | \[\$2.15, \$32\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Direct Pragmatic Trial Funding Cost per DALY; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Direct Pragmatic Trial Funding Cost per DALY > > *This exceedance probability chart shows the likelihood that Direct Pragmatic Trial Funding Cost per DALY will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Direct Pragmatic Trial Funding NPV (Exploration Period): \$483 billion > **NOTE:** > > NPV of annual direct funding for the therapeutic space exploration period, spent continuously through each year (the same timing convention as the discounted DALYs it is divided by). Funding period equals exploration time (queue clearance years at given capacity multiplier). After exploration completes, the full timeline shift benefit is realized. > > **Inputs**: > > - [Reference Annual Pragmatic Trial Funding](#sec-dfda_annual_trial_funding): \$21.8 billion > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > - [Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity](#sec-dfda_queue_clearance_years) 🔢: 36 years > > $$ \begin{gathered} NPV_{direct} \\ = Funding_{trial,ref} \times (1 - (1 \\ + r_{discount})^{-T_{queue,trial}}) / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Direct Pragmatic Trial Funding NPV (Exploration Period)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity (years) | 0.8645 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Direct Pragmatic Trial Funding NPV (Exploration Period) (10,000 simulations) > > **Simulation Results Summary: Direct Pragmatic Trial Funding NPV (Exploration Period)** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$483 billion | > | Mean (expected value) | \$431 billion | > | Median (50th percentile) | \$429 billion | > | Standard Deviation | \$172 billion | > | 90% Range (5th-95th percentile) | \[\$158 billion, \$705 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Direct Pragmatic Trial Funding NPV (Exploration Period); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Direct Pragmatic Trial Funding NPV (Exploration Period) > > *This exceedance probability chart shows the likelihood that Direct Pragmatic Trial Funding NPV (Exploration Period) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput: 15.6 thousand:1 > **NOTE:** > > ROI from directly funding pragmatic clinical trials over the therapeutic space exploration period. > > **Inputs**: > > - [Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_economic_value) 🔢: \$7.53 quadrillion > - [Direct Pragmatic Trial Funding NPV (Exploration Period)](#sec-dfda_direct_funding_queue_clearance_npv) 🔢: \$483 billion > > $$ \begin{gathered} ROI_{direct,max} \\ = \frac{Value_{max}}{NPV_{direct}} \\ = \frac{\$7530T}{\$483B} \\ = 15{,}600 \end{gathered} $$ where: $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} NPV_{direct} \\ = Funding_{trial,ref} \times (1 - (1 \\ + r_{discount})^{-T_{queue,trial}}) / \ln\left(1 + r_{discount}\right) \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Direct Pragmatic Trial Funding NPV (Exploration Period) (USD) | -0.5716 | Strong driver | > | Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.2992 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput (10,000 simulations) > > **Simulation Results Summary: Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | 15.6 thousand:1 | > | Mean (expected value) | 25.8 thousand:1 | > | Median (50th percentile) | 18.7 thousand:1 | > | Standard Deviation | 24.4 thousand:1 | > | 90% Range (5th-95th percentile) | \[4,525:1, 71.5 thousand:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput > > *This exceedance probability chart shows the likelihood that Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total DALYs Lost from Disease Eradication Delay: 8.77 billion DALYs > **NOTE:** > > Total Disability-Adjusted Life Years lost from disease eradication delay (PRIMARY estimate) > > **Inputs**: > > - [Years of Life Lost from Disease Eradication Delay](#sec-dfda_efficacy_lag_elimination_yll) 🔢: 7.9 billion years > - [Years Lived with Disability During Disease Eradication Delay](#sec-dfda_efficacy_lag_elimination_yld) 🔢: 873 million years > > $$ DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.9B + 873M = 8.77B $$ where: $$ \begin{gathered} YLL_{lag} \\ = \text{DEATHS\_TOTAL} \times (REMAINING_LIFE_EXPECTANCY_AT_60 - (\text{MEAN\_AGE\_OF\_DEATH} - 60)) \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} $$ where: $$ \begin{gathered} YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total DALYs Lost from Disease Eradication Delay** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Years of Life Lost from Disease Eradication Delay (years) | 0.9243 | Strong driver | > | Years Lived with Disability During Disease Eradication Delay (years) | 0.1328 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total DALYs Lost from Disease Eradication Delay (10,000 simulations) > > **Simulation Results Summary: Total DALYs Lost from Disease Eradication Delay** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 8.77 billion | > | Mean (expected value) | 8.78 billion | > | Median (50th percentile) | 8.61 billion | > | Standard Deviation | 2.55 billion | > | 90% Range (5th-95th percentile) | \[4.88 billion, 13.2 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total DALYs Lost from Disease Eradication Delay; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total DALYs Lost from Disease Eradication Delay > > *This exceedance probability chart shows the likelihood that Total DALYs Lost from Disease Eradication Delay will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Deaths from Disease Eradication Delay: 416 million deaths > **NOTE:** > > Total eventually avoidable deaths from delaying disease eradication by 8.2 years (PRIMARY estimate, conservative). Excludes fundamentally unavoidable deaths (primarily accidents ~7.9%). > > **Inputs**: > > - [Regulatory Delay for Efficacy Testing Post-Safety Verification](#sec-efficacy_lag_years) 📊: 8.2 years (SE: ±2 years) > - [Global Daily Deaths from Disease and Aging](#sec-global_disease_deaths_daily) 📊: 150 thousand deaths/day (SE: ±7,500 deaths/day) > > $$ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Deaths from Disease Eradication Delay** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Regulatory Delay for Efficacy Testing Post-Safety Verification (years) | 0.9809 | Strong driver | > | Global Daily Deaths from Disease and Aging (deaths/day) | 0.2015 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Deaths from Disease Eradication Delay (10,000 simulations) > > **Simulation Results Summary: Total Deaths from Disease Eradication Delay** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | 416 million | > | Mean (expected value) | 416 million | > | Median (50th percentile) | 414 million | > | Standard Deviation | 103 million | > | 90% Range (5th-95th percentile) | \[244 million, 587 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Deaths from Disease Eradication Delay; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Deaths from Disease Eradication Delay > > *This exceedance probability chart shows the likelihood that Total Deaths from Disease Eradication Delay will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Economic Loss from Disease Eradication Delay: \$1.32 quadrillion > **NOTE:** > > Total economic loss from delaying disease eradication by 8.2 years (PRIMARY estimate, 2024 USD). Values global DALYs at standardized US/International normative rate (\$150k) rather than local ability-to-pay, representing the full human capital loss. > > **Inputs**: > > - [Total DALYs Lost from Disease Eradication Delay](#sec-dfda_efficacy_lag_elimination_dalys) 🔢: 8.77 billion DALYs > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} Value_{lag} \\ = DALYs_{lag} \times Value_{QALY} \\ = 8.77B \times \$150K \\ = \$1320T \end{gathered} $$ where: $$ DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.9B + 873M = 8.77B $$ where: $$ \begin{gathered} YLL_{lag} \\ = \text{DEATHS\_TOTAL} \times (REMAINING_LIFE_EXPECTANCY_AT_60 - (\text{MEAN\_AGE\_OF\_DEATH} - 60)) \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} $$ where: $$ \begin{gathered} YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Economic Loss from Disease Eradication Delay** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total DALYs Lost from Disease Eradication Delay (DALYs) | 0.8449 | Strong driver | > | Standard Economic Value per QALY (USD/QALY) | 0.5305 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Economic Loss from Disease Eradication Delay (10,000 simulations) > > **Simulation Results Summary: Total Economic Loss from Disease Eradication Delay** > > | Statistic | Value | > |:--------------------------------|---------------------------------------:| > | Baseline (deterministic) | \$1.32 quadrillion | > | Mean (expected value) | \$1.31 quadrillion | > | Median (50th percentile) | \$1.26 quadrillion | > | Standard Deviation | \$452 trillion | > | 90% Range (5th-95th percentile) | \[\$676 trillion, \$2.14 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Economic Loss from Disease Eradication Delay; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Economic Loss from Disease Eradication Delay > > *This exceedance probability chart shows the likelihood that Total Economic Loss from Disease Eradication Delay will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Years Lived with Disability During Disease Eradication Delay: 873 million years > **NOTE:** > > Years Lived with Disability during disease eradication delay (PRIMARY estimate) > > **Inputs**: > > - [Total Deaths from Disease Eradication Delay](#sec-dfda_efficacy_lag_elimination_deaths_averted) 🔢: 416 million deaths > - [Pre-Death Suffering Period During Post-Safety Efficacy Delay](#sec-regulatory_delay_suffering_period_years) 📊: 6 years (95% CI: 4 years - 9 years) > - [Disability Weight for Untreated Chronic Conditions](#sec-chronic_disease_disability_weight) 📊: 0.35 weight (SE: ±0.07 weight) > > $$ \begin{gathered} YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Years Lived with Disability During Disease Eradication Delay** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Deaths from Disease Eradication Delay (deaths) | 0.6338 | Strong driver | > | Pre-Death Suffering Period During Post-Safety Efficacy Delay (years) | 0.5588 | Strong driver | > | Disability Weight for Untreated Chronic Conditions (weight) | 0.5016 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Years Lived with Disability During Disease Eradication Delay (10,000 simulations) > > **Simulation Results Summary: Years Lived with Disability During Disease Eradication Delay** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | 873 million | > | Mean (expected value) | 875 million | > | Median (50th percentile) | 825 million | > | Standard Deviation | 338 million | > | 90% Range (5th-95th percentile) | \[418 million, 1.5 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Years Lived with Disability During Disease Eradication Delay; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Years Lived with Disability During Disease Eradication Delay > > *This exceedance probability chart shows the likelihood that Years Lived with Disability During Disease Eradication Delay will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Years of Life Lost from Disease Eradication Delay: 7.9 billion years > **NOTE:** > > Years of Life Lost from disease eradication delay deaths (PRIMARY estimate). Years lost per death = WHO conditional remaining life expectancy at 60, adjusted down to the mean lag-death age of 62 (~19 years/death). Replaces life-expectancy-at-birth minus age, which mixed an at-birth measure (carrying child mortality the deceased already survived) with a conditional question and understated the loss by ~40%. The linear age adjustment slightly understates remaining years (conditional life expectancy falls by less than one year per year of age). > > **Inputs**: > > - [Total Deaths from Disease Eradication Delay](#sec-dfda_efficacy_lag_elimination_deaths_averted) 🔢: 416 million deaths > - [Remaining Life Expectancy at Age 60 (Global)](#sec-global_life_expectancy_remaining_at_age_60) 📊: 21 years (95% CI: 19.6 years - 22 years) > - [Mean Age of Preventable Death from Post-Safety Efficacy Delay](#sec-regulatory_delay_mean_age_of_death) 📊: 62 years (SE: ±3 years) > > $$ \begin{gathered} YLL_{lag} \\ = \text{DEATHS\_TOTAL} \times (REMAINING_LIFE_EXPECTANCY_AT_60 - (\text{MEAN\_AGE\_OF\_DEATH} - 60)) \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Years of Life Lost from Disease Eradication Delay** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Deaths from Disease Eradication Delay (deaths) | 0.8307 | Strong driver | > | Mean Age of Preventable Death from Post-Safety Efficacy Delay (years) | -0.5276 | Strong driver | > | Remaining Life Expectancy at Age 60 (Global) (years) | 0.1031 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Years of Life Lost from Disease Eradication Delay (10,000 simulations) > > **Simulation Results Summary: Years of Life Lost from Disease Eradication Delay** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | 7.9 billion | > | Mean (expected value) | 7.9 billion | > | Median (50th percentile) | 7.73 billion | > | Standard Deviation | 2.35 billion | > | 90% Range (5th-95th percentile) | \[4.34 billion, 12 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Years of Life Lost from Disease Eradication Delay; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Years of Life Lost from Disease Eradication Delay > > *This exceedance probability chart shows the likelihood that Years of Life Lost from Disease Eradication Delay will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### New Treatments Per Year at Treaty-Scale Trial Capacity: 185 diseases/year > **NOTE:** > > Diseases per year receiving their first effective treatment with treaty-scale pragmatic trial capacity. Scales proportionally with trial capacity multiplier. > > **Inputs**: > > - [Diseases Getting First Treatment Per Year](#sec-new_disease_first_treatments_per_year) 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year) > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > > $$ \begin{gathered} Treatments_{trial,ann} \\ = Treatments_{new,ann} \times k_{capacity} \\ = 15 \times 12.3 \\ = 185 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for New Treatments Per Year at Treaty-Scale Trial Capacity** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (x) | 0.8722 | Strong driver | > | Diseases Getting First Treatment Per Year (diseases/year) | 0.3967 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: New Treatments Per Year at Treaty-Scale Trial Capacity (10,000 simulations) > > **Simulation Results Summary: New Treatments Per Year at Treaty-Scale Trial Capacity** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 185 | > | Mean (expected value) | 306 | > | Median (50th percentile) | 226 | > | Standard Deviation | 270 | > | 90% Range (5th-95th percentile) | \[63.8, 816\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for New Treatments Per Year at Treaty-Scale Trial Capacity; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: New Treatments Per Year at Treaty-Scale Trial Capacity > > *This exceedance probability chart shows the likelihood that New Treatments Per Year at Treaty-Scale Trial Capacity will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Known Safe Exploration Time at Treaty-Scale Trial Capacity: 234 years > **NOTE:** > > Years to test all known safe drug-disease combinations with treaty-scale pragmatic trial capacity > > **Inputs**: > > - [Possible Drug-Disease Combinations](#sec-drug_disease_combinations_possible) 🔢: 9.5 million combinations > - [Maximum Trials per Year at Treaty-Scale Trial Capacity](#sec-dfda_trials_per_year_capacity) 🔢: 40,682 trials/year > > $$ \begin{gathered} T_{safe,trial} \\ = \frac{N_{combos}}{Capacity_{trials}} \\ = \frac{9.5M}{40{,}700} \\ = 234 \end{gathered} $$ where: $$ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} $$ where: $$ \begin{gathered} Capacity_{trials} \\ = Trials_{ann,curr} \times k_{capacity} \\ = 3{,}300 \times 12.3 \\ = 40{,}700 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Known Safe Exploration Time at Treaty-Scale Trial Capacity** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Maximum Trials per Year at Treaty-Scale Trial Capacity (trials/year) | -0.6387 | Strong driver | > | Possible Drug-Disease Combinations (combinations) | 0.2582 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Known Safe Exploration Time at Treaty-Scale Trial Capacity (10,000 simulations) > > **Simulation Results Summary: Known Safe Exploration Time at Treaty-Scale Trial Capacity** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 234 | > | Mean (expected value) | 229 | > | Median (50th percentile) | 178 | > | Standard Deviation | 175 | > | 90% Range (5th-95th percentile) | \[53.7, 598\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Known Safe Exploration Time at Treaty-Scale Trial Capacity; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Known Safe Exploration Time at Treaty-Scale Trial Capacity > > *This exceedance probability chart shows the likelihood that Known Safe Exploration Time at Treaty-Scale Trial Capacity will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Maximum Trial Capacity Multiplier (Physical Limit): 566x > **NOTE:** > > Physical upper bound on trial-capacity multiplier from participant availability. Even with unlimited funding, annual trial enrollment cannot exceed willing participant pool. > > **Inputs**: > > - [Global Patients Willing to Participate in Clinical Trials](#sec-willing_trial_participants_global) 🔢: 1.08 billion people > - [Annual Global Clinical Trial Participants](#sec-current_trial_slots_available) 📊: 1.9 million patients/year (95% CI: 1.5 million patients/year - 2.3 million patients/year) > > $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Maximum Trial Capacity Multiplier (Physical Limit)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Clinical Trial Participants (patients/year) | -0.7220 | Strong driver | > | Global Patients Willing to Participate in Clinical Trials (people) | 0.6789 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Maximum Trial Capacity Multiplier (Physical Limit) (10,000 simulations) > > **Simulation Results Summary: Maximum Trial Capacity Multiplier (Physical Limit)** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 566x | > | Mean (expected value) | 573x | > | Median (50th percentile) | 567x | > | Standard Deviation | 81.3x | > | 90% Range (5th-95th percentile) | \[450x, 718x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Maximum Trial Capacity Multiplier (Physical Limit); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Maximum Trial Capacity Multiplier (Physical Limit) > > *This exceedance probability chart shows the likelihood that Maximum Trial Capacity Multiplier (Physical Limit) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Net Savings from Pragmatic Trials (R&D Only): \$40.4 billion > **NOTE:** > > Annual net savings from R&D cost reduction only (gross savings minus operational costs, excludes regulatory delay value) > > **Inputs**: > > - [Annual R&D Savings from Pragmatic Trials](#sec-dfda_benefit_rd_only_annual) 🔢: \$40.5 billion > - [Total Annual Pragmatic Trial Platform Operational Costs](#sec-dfda_annual_opex) 🔢: \$40 million > > $$ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{trial} \\ = \$40.5B - \$40M \\ = \$40.4B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Net Savings from Pragmatic Trials (R&D Only)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual R&D Savings from Pragmatic Trials (USD/year) | 1.0000 | Strong driver | > | Total Annual Pragmatic Trial Platform Operational Costs (USD/year) | -0.0007 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Net Savings from Pragmatic Trials (R&D Only) (10,000 simulations) > > **Simulation Results Summary: Annual Net Savings from Pragmatic Trials (R&D Only)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$40.4 billion | > | Mean (expected value) | \$40.3 billion | > | Median (50th percentile) | \$39.6 billion | > | Standard Deviation | \$6.21 billion | > | 90% Range (5th-95th percentile) | \[\$31.4 billion, \$51.4 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Net Savings from Pragmatic Trials (R&D Only); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Net Savings from Pragmatic Trials (R&D Only) > > *This exceedance probability chart shows the likelihood that Annual Net Savings from Pragmatic Trials (R&D Only) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Platform Total NPV Annual OPEX: \$40 million > **NOTE:** > > Total NPV annual opex (pragmatic trial platform core + DIH initiatives) > > **Inputs**: > > - [Pragmatic Trial Platform Core Framework Annual OPEX](#sec-dfda_npv_annual_opex): \$18.9 million (95% CI: \$11 million - \$26.5 million) > - [DIH Broader Initiatives Annual OPEX](#sec-dih_npv_annual_opex_initiatives): \$21.1 million (95% CI: \$14 million - \$32 million) > > $$ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Platform Total NPV Annual OPEX** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | DIH Broader Initiatives Annual OPEX (USD/year) | 0.7686 | Strong driver | > | Pragmatic Trial Platform Core Framework Annual OPEX (USD/year) | 0.6508 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Platform Total NPV Annual OPEX (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Platform Total NPV Annual OPEX** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$40 million | > | Mean (expected value) | \$39.8 million | > | Median (50th percentile) | \$39.5 million | > | Standard Deviation | \$5.65 million | > | 90% Range (5th-95th percentile) | \[\$31.1 million, \$49.6 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Platform Total NPV Annual OPEX; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Platform Total NPV Annual OPEX > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Total NPV Annual OPEX will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted): \$269 billion > **NOTE:** > > NPV of pragmatic trial R&D savings only with 5-year adoption ramp (10-year horizon, most conservative financial estimate) > > **Inputs**: > > - [Annual Net Savings from Pragmatic Trials (R&D Only)](#sec-dfda_net_savings_rd_only_annual) 🔢: \$40.4 billion > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > > $$ \begin{gathered} NPV_{RD} \\ = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \cdot \frac{\min(t,5)}{5}}{(1+r)^t} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Net Savings from Pragmatic Trials (R&D Only) (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) (10,000 simulations) > > **Simulation Results Summary: NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted)** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$269 billion | > | Mean (expected value) | \$268 billion | > | Median (50th percentile) | \$263 billion | > | Standard Deviation | \$41.3 billion | > | 90% Range (5th-95th percentile) | \[\$208 billion, \$342 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) > > *This exceedance probability chart shows the likelihood that NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### NPV Net Benefit (R&D Only): \$268 billion > **NOTE:** > > NPV net benefit using R&D savings only (benefits minus costs) > > **Inputs**: > > - [NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted)](#sec-dfda_npv_benefit_rd_only) 🔢: \$269 billion > - [Pragmatic Trial Platform Total NPV Cost](#sec-dfda_npv_total_cost) 🔢: \$611 million > > $$ \begin{gathered} NPV_{net,RD} \\ = NPV_{RD} - Cost_{platform,total} \\ = \$269B - \$611M \\ = \$268B \end{gathered} $$ where: $$ \begin{gathered} NPV_{RD} \\ = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \cdot \frac{\min(t,5)}{5}}{(1+r)^t} \end{gathered} $$ where: $$ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{trial} \\ = \$40.5B - \$40M \\ = \$40.4B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Cost_{platform,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \end{gathered} $$ where: $$ \begin{gathered} PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \end{gathered} $$ where: $$ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for NPV Net Benefit (R&D Only)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) (USD) | 1.0000 | Strong driver | > | Pragmatic Trial Platform Total NPV Cost (USD) | -0.0017 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: NPV Net Benefit (R&D Only) (10,000 simulations) > > **Simulation Results Summary: NPV Net Benefit (R&D Only)** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$268 billion | > | Mean (expected value) | \$267 billion | > | Median (50th percentile) | \$263 billion | > | Standard Deviation | \$41.3 billion | > | 90% Range (5th-95th percentile) | \[\$208 billion, \$341 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for NPV Net Benefit (R&D Only); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: NPV Net Benefit (R&D Only) > > *This exceedance probability chart shows the likelihood that NPV Net Benefit (R&D Only) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years: \$342 million > **NOTE:** > > Present value of annual opex over 10 years (NPV formula) > > **Inputs**: > > - [Pragmatic Trial Platform Total NPV Annual OPEX](#sec-dfda_npv_annual_opex_total) 🔢: \$40 million > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > - [Standard Time Horizon for NPV Analysis](#sec-npv_time_horizon_years): 10 years > > $$ \begin{gathered} PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \end{gathered} $$ where: $$ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Platform Total NPV Annual OPEX (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$342 million | > | Mean (expected value) | \$340 million | > | Median (50th percentile) | \$337 million | > | Standard Deviation | \$48.2 million | > | 90% Range (5th-95th percentile) | \[\$265 million, \$423 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Platform Total NPV Cost: \$611 million > **NOTE:** > > Total NPV cost (upfront + PV of annual opex) > > **Inputs**: > > - [Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years](#sec-dfda_npv_pv_annual_opex) 🔢: \$342 million > - [Pragmatic Trial Platform Total NPV Upfront Costs](#sec-dfda_npv_upfront_cost_total) 🔢: \$270 million > > $$ \begin{gathered} Cost_{platform,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \end{gathered} $$ where: $$ \begin{gathered} PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \end{gathered} $$ where: $$ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Platform Total NPV Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Platform Total NPV Upfront Costs (USD) | 0.7087 | Strong driver | > | Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years (USD) | 0.6952 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Platform Total NPV Cost (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Platform Total NPV Cost** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$611 million | > | Mean (expected value) | \$609 million | > | Median (50th percentile) | \$606 million | > | Standard Deviation | \$69.3 million | > | 90% Range (5th-95th percentile) | \[\$499 million, \$729 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Platform Total NPV Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Platform Total NPV Cost > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Total NPV Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Platform Total NPV Upfront Costs: \$270 million > **NOTE:** > > Total NPV upfront costs (pragmatic trial platform core + DIH initiatives) > > **Inputs**: > > - [Pragmatic Trial Platform Core Framework Build Cost](#sec-dfda_npv_upfront_cost): \$40 million (95% CI: \$25 million - \$65 million) > - [DIH Broader Initiatives Upfront Cost](#sec-dih_npv_upfront_cost_initiatives): \$230 million (95% CI: \$150 million - \$350 million) > > $$ \begin{gathered} Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Platform Total NPV Upfront Costs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | DIH Broader Initiatives Upfront Cost (USD) | 0.9826 | Strong driver | > | Pragmatic Trial Platform Core Framework Build Cost (USD) | 0.1958 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Platform Total NPV Upfront Costs (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Platform Total NPV Upfront Costs** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$270 million | > | Mean (expected value) | \$269 million | > | Median (50th percentile) | \$264 million | > | Standard Deviation | \$49.1 million | > | 90% Range (5th-95th percentile) | \[\$196 million, \$363 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Platform Total NPV Upfront Costs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Platform Total NPV Upfront Costs > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Total NPV Upfront Costs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Platform Overhead Percentage of Treaty Funding: 0.147% > **NOTE:** > > Percentage of treaty funding allocated to pragmatic trial platform overhead > > **Inputs**: > > - [Total Annual Pragmatic Trial Platform Operational Costs](#sec-dfda_annual_opex) 🔢: \$40 million > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > > $$ \begin{gathered} OPEX_{pct} \\ = \frac{OPEX_{trial}}{Funding_{treaty}} \\ = \frac{\$40M}{\$27.2B} \\ = 0.147\% \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Platform Overhead Percentage of Treaty Funding** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Pragmatic Trial Platform Operational Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Platform Overhead Percentage of Treaty Funding (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Platform Overhead Percentage of Treaty Funding** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.147% | > | Mean (expected value) | 0.147% | > | Median (50th percentile) | 0.146% | > | Standard Deviation | 0.015% | > | 90% Range (5th-95th percentile) | \[0.123%, 0.173%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Platform Overhead Percentage of Treaty Funding; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Platform Overhead Percentage of Treaty Funding > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Overhead Percentage of Treaty Funding will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Patients Fundable Annually at Reference Funding: 23.4 million patients/year > **NOTE:** > > Number of patients fundable annually at the reference pragmatic trial funding level and empirical pragmatic trial cost. Source-agnostic counterpart of DIH_PATIENTS_FUNDABLE_ANNUALLY. > > **Inputs**: > > - [Reference Annual Trial Subsidies](#sec-dfda_trial_subsidies_annual) 🔢: \$21.8 billion > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > > $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Patients Fundable Annually at Reference Funding** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Cost per Patient (USD/patient) | -0.6862 | Strong driver | > | Reference Annual Trial Subsidies (USD/year) | 0.0014 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Patients Fundable Annually at Reference Funding (10,000 simulations) > > **Simulation Results Summary: Patients Fundable Annually at Reference Funding** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 23.4 million | > | Mean (expected value) | 38.4 million | > | Median (50th percentile) | 30 million | > | Standard Deviation | 29.5 million | > | 90% Range (5th-95th percentile) | \[9.23 million, 93.9 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Patients Fundable Annually at Reference Funding; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Patients Fundable Annually at Reference Funding > > *This exceedance probability chart shows the likelihood that Patients Fundable Annually at Reference Funding will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity: 36 years > **NOTE:** > > Years to explore the entire therapeutic search space with treaty-scale pragmatic trial capacity. At increased discovery rate, finding first treatments for all currently untreatable diseases takes ~36 years instead of ~443. > > **Inputs**: > > - [Status Quo Therapeutic Space Exploration Time](#sec-status_quo_queue_clearance_years) 🔢: 443 years > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > > $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (x) | -0.5900 | Strong driver | > | Status Quo Therapeutic Space Exploration Time (years) | 0.4217 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity (10,000 simulations) > > **Simulation Results Summary: Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 36 | > | Mean (expected value) | 39.5 | > | Median (50th percentile) | 29.5 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[8.15, 106\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity > > *This exceedance probability chart shows the likelihood that Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Daily R&D Savings from Trial Cost Reduction: \$111 million > **NOTE:** > > Daily R&D savings from trial cost reduction (opportunity cost of delay) > > **Inputs**: > > - [Annual R&D Savings from Pragmatic Trials](#sec-dfda_benefit_rd_only_annual) 🔢: \$40.5 billion > > $$ \begin{gathered} Savings_{RD,daily} \\ = Benefit_{RD,ann} \times 0.00274 \\ = \$40.5B \times 0.00274 \\ = \$111M \end{gathered} $$ where: $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Daily R&D Savings from Trial Cost Reduction** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual R&D Savings from Pragmatic Trials (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Daily R&D Savings from Trial Cost Reduction (10,000 simulations) > > **Simulation Results Summary: Daily R&D Savings from Trial Cost Reduction** > > | Statistic | Value | > |:--------------------------------|--------------------------------:| > | Baseline (deterministic) | \$111 million | > | Mean (expected value) | \$111 million | > | Median (50th percentile) | \$109 million | > | Standard Deviation | \$17 million | > | 90% Range (5th-95th percentile) | \[\$86 million, \$141 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Daily R&D Savings from Trial Cost Reduction; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Daily R&D Savings from Trial Cost Reduction > > *This exceedance probability chart shows the likelihood that Daily R&D Savings from Trial Cost Reduction will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### ROI from Pragmatic Trial R&D Savings Only: 439:1 > **NOTE:** > > ROI from pragmatic trial R&D savings only (10-year NPV, most conservative estimate) > > **Inputs**: > > - [NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted)](#sec-dfda_npv_benefit_rd_only) 🔢: \$269 billion > - [Pragmatic Trial Platform Total NPV Cost](#sec-dfda_npv_total_cost) 🔢: \$611 million > > $$ \begin{gathered} ROI_{RD} \\ = \frac{NPV_{RD}}{Cost_{platform,total}} \\ = \frac{\$269B}{\$611M} \\ = 439 \end{gathered} $$ where: $$ \begin{gathered} NPV_{RD} \\ = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \cdot \frac{\min(t,5)}{5}}{(1+r)^t} \end{gathered} $$ where: $$ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{trial} \\ = \$40.5B - \$40M \\ = \$40.4B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Cost_{platform,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \end{gathered} $$ where: $$ \begin{gathered} PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \end{gathered} $$ where: $$ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for ROI from Pragmatic Trial R&D Savings Only** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) (USD) | 0.8007 | Strong driver | > | Pragmatic Trial Platform Total NPV Cost (USD) | -0.5884 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: ROI from Pragmatic Trial R&D Savings Only (10,000 simulations) > > **Simulation Results Summary: ROI from Pragmatic Trial R&D Savings Only** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 439:1 | > | Mean (expected value) | 445:1 | > | Median (50th percentile) | 436:1 | > | Standard Deviation | 85.7:1 | > | 90% Range (5th-95th percentile) | \[321:1, 600:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for ROI from Pragmatic Trial R&D Savings Only; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: ROI from Pragmatic Trial R&D Savings Only > > *This exceedance probability chart shows the likelihood that ROI from Pragmatic Trial R&D Savings Only will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Maximum Trials per Year at Treaty-Scale Trial Capacity: 40,682 trials/year > **NOTE:** > > Maximum trials per year possible with trial capacity multiplier > > **Inputs**: > > - [Current Global Clinical Trials per Year](#sec-current_trials_per_year) 📊: 3,300 trials/year (95% CI: 2,640 trials/year - 3,960 trials/year) > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > > $$ \begin{gathered} Capacity_{trials} \\ = Trials_{ann,curr} \times k_{capacity} \\ = 3{,}300 \times 12.3 \\ = 40{,}700 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Maximum Trials per Year at Treaty-Scale Trial Capacity** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (x) | 0.9866 | Strong driver | > | Current Global Clinical Trials per Year (trials/year) | 0.1212 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Maximum Trials per Year at Treaty-Scale Trial Capacity (10,000 simulations) > > **Simulation Results Summary: Maximum Trials per Year at Treaty-Scale Trial Capacity** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | 40,682 | > | Mean (expected value) | 67,548 | > | Median (50th percentile) | 52,092 | > | Standard Deviation | 53,631 | > | 90% Range (5th-95th percentile) | \[16,041, 169 thousand\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Maximum Trials per Year at Treaty-Scale Trial Capacity; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Maximum Trials per Year at Treaty-Scale Trial Capacity > > *This exceedance probability chart shows the likelihood that Maximum Trials per Year at Treaty-Scale Trial Capacity will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding: 12.3x > **NOTE:** > > Trial capacity multiplier from treaty-scale pragmatic trial funding capacity vs. current global trial participation > > **Inputs**: > > - [Annual Global Clinical Trial Participants](#sec-current_trial_slots_available) 📊: 1.9 million patients/year (95% CI: 1.5 million patients/year - 2.3 million patients/year) > - [Patients Fundable Annually at Reference Funding](#sec-dfda_patients_fundable_annually) 🔢: 23.4 million patients/year > > $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Patients Fundable Annually at Reference Funding (patients/year) | 0.9866 | Strong driver | > | Annual Global Clinical Trial Participants (patients/year) | -0.1314 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 12.3x | > | Mean (expected value) | 20.5x | > | Median (50th percentile) | 16x | > | Standard Deviation | 16x | > | 90% Range (5th-95th percentile) | \[4.92x, 50.8x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 565 billion DALYs > **NOTE:** > > Total DALYs averted from the combined treatment timeline shift. Calculated as annual global DALY burden × eventually avoidable percentage × timeline shift years. Includes both fatal and non-fatal diseases (WHO GBD methodology). Undiscounted physical count of healthy years; cost-effectiveness ratios and dollar values use DFDA_TRIAL_CAPACITY_PLUS_EFFICACY_LAG_DALYS_DISCOUNTED so costs and health are discounted at the same rate. > > **Inputs**: > > - [Global Annual DALY Burden](#sec-global_annual_daly_burden) 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year) > - [Eventually Avoidable DALY Percentage](#sec-eventually_avoidable_daly_pct): 92.6% (95% CI: 50% - 98%) > - [Average Total Treatment Timeline Shift](#sec-dfda_trial_capacity_plus_efficacy_lag_years) 🔢: 212 years > > $$ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Average Total Treatment Timeline Shift (years) | 0.9320 | Strong driver | > | Eventually Avoidable DALY Percentage (percentage) | 0.3151 | Moderate driver | > | Global Annual DALY Burden (DALYs/year) | 0.1348 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (10,000 simulations) > > **Simulation Results Summary: Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 565 billion | > | Mean (expected value) | 635 billion | > | Median (50th percentile) | 600 billion | > | Standard Deviation | 237 billion | > | 90% Range (5th-95th percentile) | \[309 billion, 1.08 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput > > *This exceedance probability chart shows the likelihood that Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 50.2 billion DALYs > **NOTE:** > > Present value of the DALYs averted by the combined treatment timeline shift, discounted at the standard social discount rate (the same rate applied to costs). Standard cost-effectiveness practice (WHO-CHOICE, US Second Panel, ICER) discounts costs and health at the same rate; discounting costs but not health makes any program look better the longer it is delayed (Keeler-Cretin paradox). Also prices the rising uncertainty of benefits centuries out. Use for cost per DALY, ROI, and dollar values; use the undiscounted DFDA_TRIAL_CAPACITY_PLUS_EFFICACY_LAG_DALYS for counts of healthy years. A(T) = (1 - (1+r)^(-T)) / (T ln(1+r)) is the average discount factor over \[0, T\]. > > **Inputs**: > > - [Global Annual DALY Burden](#sec-global_annual_daly_burden) 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year) > - [Eventually Avoidable DALY Percentage](#sec-eventually_avoidable_daly_pct): 92.6% (95% CI: 50% - 98%) > - [Status Quo Therapeutic Space Exploration Time](#sec-status_quo_queue_clearance_years) 🔢: 443 years > - [Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity](#sec-dfda_queue_clearance_years) 🔢: 36 years > - [Regulatory Delay for Efficacy Testing Post-Safety Verification](#sec-efficacy_lag_years) 📊: 8.2 years (SE: ±2 years) > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > > $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity (years) | -0.8653 | Strong driver | > | Eventually Avoidable DALY Percentage (percentage) | 0.3629 | Moderate driver | > | Global Annual DALY Burden (DALYs/year) | 0.1587 | Weak driver | > | Status Quo Therapeutic Space Exploration Time (years) | 0.0498 | Minimal effect | > | Regulatory Delay for Efficacy Testing Post-Safety Verification (years) | 0.0139 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (10,000 simulations) > > **Simulation Results Summary: Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | 50.2 billion | > | Mean (expected value) | 52.1 billion | > | Median (50th percentile) | 53.8 billion | > | Standard Deviation | 17 billion | > | 90% Range (5th-95th percentile) | \[22 billion, 76.9 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput > > *This exceedance probability chart shows the likelihood that Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: \$7.53 quadrillion > **NOTE:** > > Present value of the combined treatment timeline shift: discounted DALYs valued at the standard economic rate per QALY. > > **Inputs**: > > - [Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_dalys_discounted) 🔢: 50.2 billion DALYs > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | 0.8594 | Strong driver | > | Standard Economic Value per QALY (USD/QALY) | 0.4828 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (10,000 simulations) > > **Simulation Results Summary: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$7.53 quadrillion | > | Mean (expected value) | \$7.8 quadrillion | > | Median (50th percentile) | \$7.74 quadrillion | > | Standard Deviation | \$2.94 quadrillion | > | 90% Range (5th-95th percentile) | \[\$3.08 quadrillion, \$12.8 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput > > *This exceedance probability chart shows the likelihood that Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 10.7 billion deaths > **NOTE:** > > Total eventually avoidable deaths from the combined treatment timeline shift. Represents deaths prevented when cures arrive earlier due to both increased trial capacity and eliminated efficacy lag. > > **Inputs**: > > - [Global Daily Deaths from Disease and Aging](#sec-global_disease_deaths_daily) 📊: 150 thousand deaths/day (SE: ±7,500 deaths/day) > - [Average Total Treatment Timeline Shift](#sec-dfda_trial_capacity_plus_efficacy_lag_years) 🔢: 212 years > > $$ \begin{gathered} Lives_{max} \\ = Deaths_{disease,daily} \times T_{accel,max} \times 338 \\ = 150{,}000 \times 212 \times 338 \\ = 10.7B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Average Total Treatment Timeline Shift (years) | 0.9886 | Strong driver | > | Global Daily Deaths from Disease and Aging (deaths/day) | 0.1418 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (10,000 simulations) > > **Simulation Results Summary: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 10.7 billion | > | Mean (expected value) | 12.1 billion | > | Median (50th percentile) | 11.5 billion | > | Standard Deviation | 4.28 billion | > | 90% Range (5th-95th percentile) | \[6.24 billion, 20.3 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput > > *This exceedance probability chart shows the likelihood that Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 1.93 quadrillion hours > **NOTE:** > > Hours of suffering eliminated from the combined treatment timeline shift. Calculated from YLD component of DALYs (39% of total DALYs × hours per year). One-time benefit, not annual recurring. > > **Inputs**: > > - [Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_dalys) 🔢: 565 billion DALYs > - [YLD Proportion of Total DALYs](#sec-global_yld_proportion_of_dalys) 📊: 0.39 proportion (SE: ±0.03 proportion) > > $$ \begin{gathered} Hours_{suffer,max} \\ = DALYs_{max} \times Pct_{YLD} \times 8760 \\ = 565B \times 0.39 \times 8760 \\ = 1930T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | 0.9776 | Strong driver | > | YLD Proportion of Total DALYs (proportion) | 0.2007 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (10,000 simulations) > > **Simulation Results Summary: Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput** > > | Statistic | Value | > |:--------------------------------|---------------------------------------:| > | Baseline (deterministic) | 1.93 quadrillion | > | Mean (expected value) | 2.17 quadrillion | > | Median (50th percentile) | 2.04 quadrillion | > | Standard Deviation | 828 trillion | > | 90% Range (5th-95th percentile) | \[1.04 quadrillion, 3.75 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput > > *This exceedance probability chart shows the likelihood that Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Average Total Treatment Timeline Shift: 212 years > **NOTE:** > > Average years earlier patients receive treatments from increased pragmatic trial capacity plus efficacy lag elimination for treatments already discovered. > > **Inputs**: > > - [Treatment Timeline Acceleration from Pragmatic Trial Capacity](#sec-dfda_trial_capacity_treatment_acceleration_years) 🔢: 204 years > - [Regulatory Delay for Efficacy Testing Post-Safety Verification](#sec-efficacy_lag_years) 📊: 8.2 years (SE: ±2 years) > > $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Average Total Treatment Timeline Shift** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treatment Timeline Acceleration from Pragmatic Trial Capacity (years) | 0.9998 | Strong driver | > | Regulatory Delay for Efficacy Testing Post-Safety Verification (years) | 0.0238 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Average Total Treatment Timeline Shift (10,000 simulations) > > **Simulation Results Summary: Average Total Treatment Timeline Shift** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 212 | > | Mean (expected value) | 239 | > | Median (50th percentile) | 227 | > | Standard Deviation | 83.4 | > | 90% Range (5th-95th percentile) | \[124, 398\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Average Total Treatment Timeline Shift; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Average Total Treatment Timeline Shift > > *This exceedance probability chart shows the likelihood that Average Total Treatment Timeline Shift will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treatment Timeline Acceleration from Pragmatic Trial Capacity: 204 years > **NOTE:** > > Years earlier the average first treatment arrives due to increased pragmatic trial capacity. Calculated as the status quo timeline reduced by the inverse of the capacity multiplier. Uses only trial capacity multiplier (not combined with valley of death rescue) because additional candidates do not directly speed therapeutic space exploration. > > **Inputs**: > > - [Status Quo Average Years to First Treatment](#sec-status_quo_avg_years_to_first_treatment) 🔢: 222 years > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > > $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treatment Timeline Acceleration from Pragmatic Trial Capacity** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Status Quo Average Years to First Treatment (years) | 0.9823 | Strong driver | > | Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (x) | 0.1163 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treatment Timeline Acceleration from Pragmatic Trial Capacity (10,000 simulations) > > **Simulation Results Summary: Treatment Timeline Acceleration from Pragmatic Trial Capacity** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 204 | > | Mean (expected value) | 231 | > | Median (50th percentile) | 219 | > | Standard Deviation | 83.4 | > | 90% Range (5th-95th percentile) | \[116, 390\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treatment Timeline Acceleration from Pragmatic Trial Capacity; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treatment Timeline Acceleration from Pragmatic Trial Capacity > > *This exceedance probability chart shows the likelihood that Treatment Timeline Acceleration from Pragmatic Trial Capacity will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Cost Reduction Factor: 44.1x > **NOTE:** > > Cost reduction factor projected for embedded pragmatic trials (traditional Phase 3 cost / pragmatic trial cost per patient) > > **Inputs**: > > - [Phase 3 Cost per Patient](#sec-traditional_phase3_cost_per_patient) 📊: \$41,000 (95% CI: \$20,000 - \$120,000) > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > > $$ \begin{gathered} k_{reduce} \\ = \frac{Cost_{P3,pt}}{Cost_{pragmatic,pt}} \\ = \frac{\$41K}{\$929} \\ = 44.1 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Cost Reduction Factor** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Phase 3 Cost per Patient (USD/patient) | 0.5310 | Strong driver | > | Pragmatic Trial Cost per Patient (USD/patient) | -0.4880 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Cost Reduction Factor (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Cost Reduction Factor** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 44.1x | > | Mean (expected value) | 73x | > | Median (50th percentile) | 49.1x | > | Standard Deviation | 78.5x | > | 90% Range (5th-95th percentile) | \[12.8x, 210x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Cost Reduction Factor; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Cost Reduction Factor > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Cost Reduction Factor will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pragmatic Trial Cost Reduction Percentage: 97.7% > **NOTE:** > > Trial cost reduction percentage: 1 - (pragmatic trial cost / traditional Phase 3 cost) > > **Inputs**: > > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > - [Phase 3 Cost per Patient](#sec-traditional_phase3_cost_per_patient) 📊: \$41,000 (95% CI: \$20,000 - \$120,000) > > $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Cost Reduction Percentage** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Cost per Patient (USD/patient) | -0.8031 | Strong driver | > | Phase 3 Cost per Patient (USD/patient) | 0.4142 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Cost Reduction Percentage (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Cost Reduction Percentage** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 97.7% | > | Mean (expected value) | 97.2% | > | Median (50th percentile) | 98% | > | Standard Deviation | 2.48% | > | 90% Range (5th-95th percentile) | \[92.2%, 99.5%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Cost Reduction Percentage; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Cost Reduction Percentage > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Cost Reduction Percentage will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Reference Annual Trial Subsidies: \$21.8 billion > **NOTE:** > > Annual patient-level pragmatic trial subsidies after operating costs at the reference funding level > > **Inputs**: > > - [Reference Annual Pragmatic Trial Funding](#sec-dfda_annual_trial_funding): \$21.8 billion > - [Total Annual Pragmatic Trial Platform Operational Costs](#sec-dfda_annual_opex) 🔢: \$40 million > > $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Reference Annual Trial Subsidies** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Pragmatic Trial Platform Operational Costs (USD/year) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Reference Annual Trial Subsidies (10,000 simulations) > > **Simulation Results Summary: Reference Annual Trial Subsidies** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$21.8 billion | > | Mean (expected value) | \$21.8 billion | > | Median (50th percentile) | \$21.8 billion | > | Standard Deviation | \$4.09 million | > | 90% Range (5th-95th percentile) | \[\$21.8 billion, \$21.8 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Reference Annual Trial Subsidies; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Reference Annual Trial Subsidies > > *This exceedance probability chart shows the likelihood that Reference Annual Trial Subsidies will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Valley of Death Rescue Multiplier: 1.4x > **NOTE:** > > Factor increase in drugs entering development when pragmatic trial subsidies remove the Phase 2/3 cost barrier. Valley-of-death attrition (40%) becomes new drugs, so 1 + 0.40 = 1.4× more drugs. > > **Inputs**: > > - [Valley of Death Attrition Rate](#sec-valley_of_death_attrition_pct) 📊: 40% (95% CI: 25% - 55%) > > $$ k_{rescue} = Attrition_{valley} + 1 = 40\% + 1 = 1.4 $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Valley of Death Rescue Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Valley of Death Attrition Rate (percentage) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Valley of Death Rescue Multiplier (10,000 simulations) > > **Simulation Results Summary: Valley of Death Rescue Multiplier** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 1.4x | > | Mean (expected value) | 1.4x | > | Median (50th percentile) | 1.4x | > | Standard Deviation | 0.0868x | > | 90% Range (5th-95th percentile) | \[1.26x, 1.54x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Valley of Death Rescue Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Valley of Death Rescue Multiplier > > *This exceedance probability chart shows the likelihood that Valley of Death Rescue Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### DIH Same-Budget Trial Patients Fundable: 1.67 million patients > **NOTE:** > > Trial participants the SAME NIH clinical-trials allocation funds each year at dFDA pragmatic-trial prices: identical budget, identical 3.3% split, different procurement. No treaty and no new money required. > > **Inputs**: > > - [NIH Annual Budget](#sec-nih_annual_budget) 📊: \$47 billion (95% CI: \$45 billion - \$50 billion) > - [NIH Clinical Trials Spending Percentage](#sec-nih_clinical_trials_spending_pct) 📊: 3.3% (95% CI: 2% - 5%) > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > > $$ \begin{gathered} Fundable \\ = \text{NIH\_BUDGET} \times \text{TRIALS\_PCT} / \text{PRAGMATIC\_COST\_PER\_PATIENT} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for DIH Same-Budget Trial Patients Fundable** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Cost per Patient (USD/patient) | -0.6452 | Strong driver | > | NIH Clinical Trials Spending Percentage (percentage) | 0.2793 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: DIH Same-Budget Trial Patients Fundable (10,000 simulations) > > **Simulation Results Summary: DIH Same-Budget Trial Patients Fundable** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 1.67 million | > | Mean (expected value) | 2.74 million | > | Median (50th percentile) | 2.1 million | > | Standard Deviation | 2.25 million | > | 90% Range (5th-95th percentile) | \[627 thousand, 7.13 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for DIH Same-Budget Trial Patients Fundable; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: DIH Same-Budget Trial Patients Fundable > > *This exceedance probability chart shows the likelihood that DIH Same-Budget Trial Patients Fundable will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Patients Fundable Annually Under the 1% Treaty: 23.4 million patients/year > **NOTE:** > > Number of patients fundable annually from 1% Treaty pragmatic trial subsidies at empirical pragmatic trial cost (RECOVERY to PCORnet range). > > **Inputs**: > > - [1% Treaty Annual Trial Subsidies](#sec-dih_treasury_trial_subsidies_annual) 🔢: \$21.7 billion > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > > $$ \begin{gathered} N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.7B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ where: $$ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Patients Fundable Annually Under the 1% Treaty** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Cost per Patient (USD/patient) | -0.6862 | Strong driver | > | 1% Treaty Annual Trial Subsidies (USD/year) | 0.0014 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Patients Fundable Annually Under the 1% Treaty (10,000 simulations) > > **Simulation Results Summary: Patients Fundable Annually Under the 1% Treaty** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 23.4 million | > | Mean (expected value) | 38.3 million | > | Median (50th percentile) | 29.9 million | > | Standard Deviation | 29.5 million | > | 90% Range (5th-95th percentile) | \[9.21 million, 93.7 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Patients Fundable Annually Under the 1% Treaty; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Patients Fundable Annually Under the 1% Treaty > > *This exceedance probability chart shows the likelihood that Patients Fundable Annually Under the 1% Treaty will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Medical Research Percentage of Treaty Funding: 80% > **NOTE:** > > Percentage of treaty funding allocated to medical research (after bond payouts and IAB incentives) > > **Inputs**: > > - [1% Treaty Pragmatic Trial Funding](#sec-dih_treasury_to_medical_research_annual) 🔢: \$21.8 billion > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > > $$ \begin{gathered} Pct_{treasury,RD} \\ = \frac{Treasury_{RD,ann}}{Funding_{treaty}} \\ = \frac{\$21.8B}{\$27.2B} \\ = 80\% \end{gathered} $$ where: $$ \begin{gathered} Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ where: $$ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Medical Research Percentage of Treaty Funding is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 80% | > | Mean (expected value) | 80% | > | Median (50th percentile) | 80% | > | Standard Deviation | 1.11e-14% | > | 90% Range (5th-95th percentile) | \[80%, 80%\] | > > #### Exceedance Probability > > **Exceedance note:** Medical Research Percentage of Treaty Funding collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **80%** ### 1% Treaty Pragmatic Trial Funding: \$21.8 billion > **NOTE:** > > Annual 1% Treaty funding available for pragmatic clinical trials after bond payouts and political incentive funding. > > **Inputs**: > > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > - [Annual VICTORY Incentive Alignment Bond Payout](#sec-victory_bond_annual_payout) 🔢: \$2.72 billion > - [Annual IAB Political Incentive Funding](#sec-iab_political_incentive_funding_annual) 🔢: \$2.72 billion > > $$ \begin{gathered} Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ where: $$ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** 1% Treaty Pragmatic Trial Funding is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.176e-02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$21.8 billion | > | Mean (expected value) | \$21.8 billion | > | Median (50th percentile) | \$21.8 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$21.8 billion, \$21.8 billion\] | > > #### Exceedance Probability > > **Exceedance note:** 1% Treaty Pragmatic Trial Funding collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.176e-02) > > Approximate deterministic value: **\$21.8 billion** ### 1% Treaty Annual Trial Subsidies: \$21.7 billion > **NOTE:** > > Annual 1% Treaty patient-level pragmatic trial subsidies after platform operating costs > > **Inputs**: > > - [Total Annual Pragmatic Trial Platform Operational Costs](#sec-dfda_annual_opex) 🔢: \$40 million > - [1% Treaty Pragmatic Trial Funding](#sec-dih_treasury_to_medical_research_annual) 🔢: \$21.8 billion > > $$ \begin{gathered} Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.7B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ where: $$ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for 1% Treaty Annual Trial Subsidies** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Pragmatic Trial Platform Operational Costs (USD/year) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: 1% Treaty Annual Trial Subsidies (10,000 simulations) > > **Simulation Results Summary: 1% Treaty Annual Trial Subsidies** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$21.7 billion | > | Mean (expected value) | \$21.7 billion | > | Median (50th percentile) | \$21.7 billion | > | Standard Deviation | \$4.09 million | > | 90% Range (5th-95th percentile) | \[\$21.7 billion, \$21.7 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for 1% Treaty Annual Trial Subsidies; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: 1% Treaty Annual Trial Subsidies > > *This exceedance probability chart shows the likelihood that 1% Treaty Annual Trial Subsidies will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Patient Trial Subsidies Percentage of Treaty Funding: 79.9% > **NOTE:** > > Percentage of treaty funding going directly to patient trial subsidies > > **Inputs**: > > - [1% Treaty Annual Trial Subsidies](#sec-dih_treasury_trial_subsidies_annual) 🔢: \$21.7 billion > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > > $$ \begin{gathered} Pct_{subsidies} \\ = \frac{Subsidies_{trial,ann}}{Funding_{treaty}} \\ = \frac{\$21.7B}{\$27.2B} \\ = 79.9\% \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.7B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ where: $$ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Patient Trial Subsidies Percentage of Treaty Funding** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | 1% Treaty Annual Trial Subsidies (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Patient Trial Subsidies Percentage of Treaty Funding (10,000 simulations) > > **Simulation Results Summary: Patient Trial Subsidies Percentage of Treaty Funding** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 79.9% | > | Mean (expected value) | 79.9% | > | Median (50th percentile) | 79.9% | > | Standard Deviation | 0.015% | > | 90% Range (5th-95th percentile) | \[79.8%, 79.9%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Patient Trial Subsidies Percentage of Treaty Funding; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Patient Trial Subsidies Percentage of Treaty Funding > > *This exceedance probability chart shows the likelihood that Patient Trial Subsidies Percentage of Treaty Funding will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Diseases Without Effective Treatment: 6,650 diseases > **NOTE:** > > Number of diseases without effective treatment. 95% of 7,000 rare diseases lack FDA-approved treatment (per Orphanet 2024). This represents the therapeutic search space that remains unexplored. > > **Inputs**: > > - [Total Number of Rare Diseases Globally](#sec-rare_diseases_count_global) 📊: 7,000 diseases (95% CI: 6,000 diseases - 10,000 diseases) > > $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ > > **Methodology**:^([4](../references.llms.md#ref-rare-disease-only-5pct-have-treatment)) > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Diseases Without Effective Treatment** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Number of Rare Diseases Globally (diseases) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Diseases Without Effective Treatment (10,000 simulations) > > **Simulation Results Summary: Diseases Without Effective Treatment** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 6,650 | > | Mean (expected value) | 6,718 | > | Median (50th percentile) | 6,629 | > | Standard Deviation | 827 | > | 90% Range (5th-95th percentile) | \[5,700, 8,232\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Diseases Without Effective Treatment; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Diseases Without Effective Treatment > > *This exceedance probability chart shows the likelihood that Diseases Without Effective Treatment will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths: 18.4 thousand:1 > **NOTE:** > > Ratio of annual disease deaths to 9/11 terrorism deaths > > **Inputs**: > > - [Annual Deaths from All Diseases and Aging Globally](#sec-global_annual_deaths_curable_diseases) 📊: 55 million deaths/year (SE: ±5 million deaths/year) > - [Deaths from 9/11 Terrorist Attacks](#sec-terrorism_deaths_911) 📊: 2,996 deaths > > $$ \begin{gathered} Ratio_{dis:terror} \\ = \frac{Deaths_{curable,ann}}{Deaths_{9/11}} \\ = \frac{55M}{3{,}000} \\ = 18{,}400 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Deaths from All Diseases and Aging Globally (deaths/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths (10,000 simulations) > > **Simulation Results Summary: Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | 18.4 thousand:1 | > | Mean (expected value) | 18.3 thousand:1 | > | Median (50th percentile) | 18.3 thousand:1 | > | Standard Deviation | 1,676:1 | > | 90% Range (5th-95th percentile) | \[15.6 thousand:1, 21.1 thousand:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths > > *This exceedance probability chart shows the likelihood that Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Ratio of Annual Disease Deaths to War Deaths: 225:1 > **NOTE:** > > Ratio of annual disease deaths to war deaths > > **Inputs**: > > - [Annual Deaths from All Diseases and Aging Globally](#sec-global_annual_deaths_curable_diseases) 📊: 55 million deaths/year (SE: ±5 million deaths/year) > - [Total Annual Conflict Deaths Globally](#sec-global_annual_conflict_deaths_total) 🔢: 245 thousand deaths/year > > $$ \begin{gathered} Ratio_{dis:war} \\ = \frac{Deaths_{curable,ann}}{Deaths_{conflict}} \\ = \frac{55M}{245{,}000} \\ = 225 \end{gathered} $$ where: $$ \begin{gathered} Deaths_{conflict} \\ = Deaths_{combat} + Deaths_{state} + Deaths_{terror} \\ = 234{,}000 + 2{,}700 + 8{,}300 \\ = 245{,}000 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Ratio of Annual Disease Deaths to War Deaths** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Conflict Deaths Globally (deaths/year) | -0.7790 | Strong driver | > | Annual Deaths from All Diseases and Aging Globally (deaths/year) | 0.6094 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Ratio of Annual Disease Deaths to War Deaths (10,000 simulations) > > **Simulation Results Summary: Ratio of Annual Disease Deaths to War Deaths** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 225:1 | > | Mean (expected value) | 228:1 | > | Median (50th percentile) | 225:1 | > | Standard Deviation | 34.3:1 | > | 90% Range (5th-95th percentile) | \[175:1, 288:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Ratio of Annual Disease Deaths to War Deaths; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Ratio of Annual Disease Deaths to War Deaths > > *This exceedance probability chart shows the likelihood that Ratio of Annual Disease Deaths to War Deaths will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Drugs Approved Since 1962: 3,100 drugs > **NOTE:** > > Estimated total drugs approved globally since 1962 (62 years × average approval rate). Conservative: uses current rate, actual historical rate was lower in 1960s-80s. > > **Inputs**: > > - [Average Annual New Drug Approvals Globally](#sec-current_drug_approvals_per_year) 📊: 50 drugs/year (95% CI: 45 drugs/year - 60 drugs/year) > > $$ \begin{gathered} N_{drugs,62} \\ = Drugs_{ann,curr} \times 62 \\ = 50 \times 62 \\ = 3{,}100 \end{gathered} $$ > > **Methodology**:^([5](../references.llms.md#ref-global-new-drug-approvals-50-annually)) > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Drugs Approved Since 1962** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Average Annual New Drug Approvals Globally (drugs/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Drugs Approved Since 1962 (10,000 simulations) > > **Simulation Results Summary: Total Drugs Approved Since 1962** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 3,100 | > | Mean (expected value) | 3,109 | > | Median (50th percentile) | 3,094 | > | Standard Deviation | 217 | > | 90% Range (5th-95th percentile) | \[2,790, 3,493\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Drugs Approved Since 1962; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Drugs Approved Since 1962 > > *This exceedance probability chart shows the likelihood that Total Drugs Approved Since 1962 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Drug Cost Increase: 1980s to Current: 13.4x > **NOTE:** > > Drug development cost increase from 1980s to current > > **Inputs**: > > - [Drug Development Cost (1980s)](#sec-drug_development_cost_1980s) 📊: \$194 million (95% CI: \$146 million - \$242 million) > - [Pharma Drug Development Cost (Current System)](#sec-pharma_drug_development_cost_current) 📊: \$2.6 billion (95% CI: \$1.5 billion - \$4 billion) > > $$ \begin{gathered} k_{cost,80s} \\ = \frac{Cost_{dev,curr}}{Cost_{dev,80s}} \\ = \frac{\$2.6B}{\$194M} \\ = 13.4 \end{gathered} $$ > > **Methodology**:^([6](../references.llms.md#ref-pre-1962-drug-costs-timeline)) > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Drug Cost Increase: 1980s to Current** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pharma Drug Development Cost (Current System) (USD) | 0.8327 | Strong driver | > | Drug Development Cost (1980s) (USD) | -0.5396 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Drug Cost Increase: 1980s to Current (10,000 simulations) > > **Simulation Results Summary: Drug Cost Increase: 1980s to Current** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 13.4x | > | Mean (expected value) | 13.6x | > | Median (50th percentile) | 13.3x | > | Standard Deviation | 3.06x | > | 90% Range (5th-95th percentile) | \[9.15x, 19.2x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Drug Cost Increase: 1980s to Current; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Drug Cost Increase: 1980s to Current > > *This exceedance probability chart shows the likelihood that Drug Cost Increase: 1980s to Current will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Drug Cost Increase: Pre-1962 to Current: 105x > **NOTE:** > > Drug development cost increase from pre-1962 to current > > **Inputs**: > > - [Pharma Drug Development Cost (Current System)](#sec-pharma_drug_development_cost_current) 📊: \$2.6 billion (95% CI: \$1.5 billion - \$4 billion) > - [Pre-1962 Drug Development Cost (2024 Dollars)](#sec-pre_1962_drug_development_cost_2024_usd) 📊: \$24.7 million (95% CI: \$19.5 million - \$30 million) > > $$ \begin{gathered} k_{cost,pre62} \\ = \frac{Cost_{dev,curr}}{Cost_{pre62,24}} \\ = \frac{\$2.6B}{\$24.7M} \\ = 105 \end{gathered} $$ > > **Methodology**:^([7](../references.llms.md#ref-pre-1962-drug-costs-baily-1972)) > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Drug Cost Increase: Pre-1962 to Current** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pharma Drug Development Cost (Current System) (USD) | 0.8709 | Strong driver | > | Pre-1962 Drug Development Cost (2024 Dollars) (USD) | -0.4736 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Drug Cost Increase: Pre-1962 to Current (10,000 simulations) > > **Simulation Results Summary: Drug Cost Increase: Pre-1962 to Current** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 105x | > | Mean (expected value) | 107x | > | Median (50th percentile) | 104x | > | Standard Deviation | 22.9x | > | 90% Range (5th-95th percentile) | \[72.8x, 149x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Drug Cost Increase: Pre-1962 to Current; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Drug Cost Increase: Pre-1962 to Current > > *This exceedance probability chart shows the likelihood that Drug Cost Increase: Pre-1962 to Current will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Possible Drug-Disease Combinations: 9.5 million combinations > **NOTE:** > > Total possible drug-disease combinations using existing safe compounds > > **Inputs**: > > - [Safe Compounds Available for Testing](#sec-safe_compounds_count): 9,500 compounds (95% CI: 7,000 compounds - 12,000 compounds) > - [Trial-Relevant Diseases](#sec-trial_relevant_diseases_count): 1,000 diseases (95% CI: 800 diseases - 1,200 diseases) > > $$ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Possible Drug-Disease Combinations** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Safe Compounds Available for Testing (compounds) | 0.7847 | Strong driver | > | Trial-Relevant Diseases (diseases) | 0.5990 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Possible Drug-Disease Combinations (10,000 simulations) > > **Simulation Results Summary: Possible Drug-Disease Combinations** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 9.5 million | > | Mean (expected value) | 9.48 million | > | Median (50th percentile) | 9.36 million | > | Standard Deviation | 1.83 million | > | 90% Range (5th-95th percentile) | \[6.68 million, 12.8 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Possible Drug-Disease Combinations; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Possible Drug-Disease Combinations > > *This exceedance probability chart shows the likelihood that Possible Drug-Disease Combinations will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Drug Treatment Administration Savings per Citizen per Year: \$269 > **NOTE:** > > Annual savings per citizen from ending the drug war and routing addiction into the treatment machinery. > > **Inputs**: > > - [Drug War Cost](#sec-us_gov_waste_drug_war) 📊: \$90 billion (95% CI: \$60 billion - \$150 billion) > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} Savings_{ann} \\ = \frac{W_{drugs}}{Pop_{US}} \\ = \frac{\$90B}{335M} \\ = \$269 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Drug Treatment Administration Savings per Citizen per Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-------------------------------|------------------------:|:---------------| > | Drug War Cost (USD) | 0.9995 | Strong driver | > | US Population in 2024 (people) | -0.0254 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Drug Treatment Administration Savings per Citizen per Year (10,000 simulations) > > **Simulation Results Summary: Drug Treatment Administration Savings per Citizen per Year** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | \$269 | > | Mean (expected value) | \$269 | > | Median (50th percentile) | \$255 | > | Standard Deviation | \$76 | > | 90% Range (5th-95th percentile) | \[\$179, \$434\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Drug Treatment Administration Savings per Citizen per Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Drug Treatment Administration Savings per Citizen per Year > > *This exceedance probability chart shows the likelihood that Drug Treatment Administration Savings per Citizen per Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Earth Optimization Point Value: \$6,671 > **NOTE:** > > Value of a single Earth Optimization Point based on the modeled prize pool size (investable assets × participation rate × horizon multiple). CI range reflects participation uncertainty (0.1%-10%). > > **Inputs**: > > - [Prize Pool Size](#sec-prize_pool_size) 🔢: \$27.5 trillion > - [Global Registered Voters](#sec-global_registered_voters) 📊: 4.13 billion of people > > $$ \begin{gathered} V_{vote} \\ = \frac{Pool}{N_{voters,global}} \\ = \frac{\$27.5T}{4.13B} \\ = \$6.67K \end{gathered} $$ where: $$ \begin{gathered} Pool \\ = Assets_{invest} \times R_{pool} \times M_{pool} \\ = \$305T \times 1\% \times 9.03 \\ = \$27.5T \end{gathered} $$ where: $$ M_{pool} = (1 + r_{pool})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} r_{pool} \\ = r_{VC,gross} + \Delta r_{scale} + \alpha_{crowd} \\ + \alpha_{home} \\ = 17\% + -2.5\% + 0.5\% + 0.8\% \\ = 15.8\% \end{gathered} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Earth Optimization Point Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------|------------------------:|:---------------| > | Prize Pool Size (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Earth Optimization Point Value (10,000 simulations) > > **Simulation Results Summary: Earth Optimization Point Value** > > | Statistic | Value | > |:--------------------------------|--------------------:| > | Baseline (deterministic) | \$6,671 | > | Mean (expected value) | \$6,510 | > | Median (50th percentile) | \$2,429 | > | Standard Deviation | \$11,576 | > | 90% Range (5th-95th percentile) | \[\$531, \$26,504\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Earth Optimization Point Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Earth Optimization Point Value > > *This exceedance probability chart shows the likelihood that Earth Optimization Point Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Earth Optimization Points Payout (2 Claims): \$13,341 > **NOTE:** > > Payout for a depositor who recruits 2 verified participants (earning 2 Earth Optimization Points). CI range reflects participation uncertainty. > > **Inputs**: > > - [Earth Optimization Point Value](#sec-earth_optimization_point_value) 🔢: \$6,671 > > $$ V_{2claims} = V_{vote} \times 2 = \$6.67K \times 2 = \$13.3K $$ where: $$ \begin{gathered} V_{vote} \\ = \frac{Pool}{N_{voters,global}} \\ = \frac{\$27.5T}{4.13B} \\ = \$6.67K \end{gathered} $$ where: $$ \begin{gathered} Pool \\ = Assets_{invest} \times R_{pool} \times M_{pool} \\ = \$305T \times 1\% \times 9.03 \\ = \$27.5T \end{gathered} $$ where: $$ M_{pool} = (1 + r_{pool})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} r_{pool} \\ = r_{VC,gross} + \Delta r_{scale} + \alpha_{crowd} \\ + \alpha_{home} \\ = 17\% + -2.5\% + 0.5\% + 0.8\% \\ = 15.8\% \end{gathered} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Earth Optimization Points Payout (2 Claims)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Earth Optimization Point Value (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Earth Optimization Points Payout (2 Claims) (10,000 simulations) > > **Simulation Results Summary: Earth Optimization Points Payout (2 Claims)** > > | Statistic | Value | > |:--------------------------------|----------------------:| > | Baseline (deterministic) | \$13,341 | > | Mean (expected value) | \$13,020 | > | Median (50th percentile) | \$4,858 | > | Standard Deviation | \$23,152 | > | 90% Range (5th-95th percentile) | \[\$1,061, \$53,009\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Earth Optimization Points Payout (2 Claims); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Earth Optimization Points Payout (2 Claims) > > *This exceedance probability chart shows the likelihood that Earth Optimization Points Payout (2 Claims) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cumulative Efficacy Testing Cost (1962-2024): \$4.84 trillion > **NOTE:** > > Cumulative Phase 2/3 efficacy testing cost since 1962. Uses direct Phase 2/3 cost per drug - this is a LOWER BOUND because it excludes opportunity cost of delays, compounds abandoned due to cost barrier, and regulatory overhead. > > **Inputs**: > > - [Pharma Phase 2/3 Cost Barrier Per Drug](#sec-pharma_phase_2_3_cost_barrier): \$1.56 billion (SE: ±\$200 million) > - [Total Drugs Approved Since 1962](#sec-drugs_approved_since_1962) 🔢: 3,100 drugs > > $$ \begin{gathered} Cost_{eff,cumul} \\ = Cost_{P2+P3} \times N_{drugs,62} \\ = \$1.56B \times 3{,}100 \\ = \$4.84T \end{gathered} $$ where: $$ \begin{gathered} N_{drugs,62} \\ = Drugs_{ann,curr} \times 62 \\ = 50 \times 62 \\ = 3{,}100 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cumulative Efficacy Testing Cost (1962-2024)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pharma Phase 2/3 Cost Barrier Per Drug (USD) | 0.8781 | Strong driver | > | Total Drugs Approved Since 1962 (drugs) | 0.4846 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cumulative Efficacy Testing Cost (1962-2024) (10,000 simulations) > > **Simulation Results Summary: Cumulative Efficacy Testing Cost (1962-2024)** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$4.84 trillion | > | Mean (expected value) | \$4.86 trillion | > | Median (50th percentile) | \$4.83 trillion | > | Standard Deviation | \$701 billion | > | 90% Range (5th-95th percentile) | \[\$3.75 trillion, \$6.05 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cumulative Efficacy Testing Cost (1962-2024); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cumulative Efficacy Testing Cost (1962-2024) > > *This exceedance probability chart shows the likelihood that Cumulative Efficacy Testing Cost (1962-2024) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Efficacy Lag Deaths (9/11 Equivalents): 34,132 9/11s > **NOTE:** > > Total deaths from efficacy lag expressed in 9/11 equivalents. Makes the mortality cost viscerally understandable: how many September 11ths worth of deaths did the 1962 efficacy requirements cause? > > **Inputs**: > > - [Total Deaths from Historical Progress Delays](#sec-existing_drugs_efficacy_lag_deaths_total) 🔢: 102 million deaths > - [September 11 Deaths](#sec-sept_11_deaths) 📊: 2,977 people > > $$ \begin{gathered} N_{9/11,equiv} \\ = \frac{Deaths_{lag,total}}{N_{9/11}} \\ = \frac{102M}{2{,}980} \\ = 34{,}100 \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Efficacy Lag Deaths (9/11 Equivalents)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Deaths from Historical Progress Delays (deaths) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Efficacy Lag Deaths (9/11 Equivalents) (10,000 simulations) > > **Simulation Results Summary: Efficacy Lag Deaths (9/11 Equivalents)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 34,132 | > | Mean (expected value) | 34,033 | > | Median (50th percentile) | 32,344 | > | Standard Deviation | 12,202 | > | 90% Range (5th-95th percentile) | \[17,055, 56,926\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Efficacy Lag Deaths (9/11 Equivalents); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Efficacy Lag Deaths (9/11 Equivalents) > > *This exceedance probability chart shows the likelihood that Efficacy Lag Deaths (9/11 Equivalents) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treatment Delay YLD - Annual: 2.01 billion DALYs > **NOTE:** > > Annual YLD from treatment delay: patients receiving chronic disease treatment would have collectively avoided this disability if treatments were available 8.2 years earlier. Represents morbidity burden for treatment beneficiaries (distinct from mortality burden). > > **Inputs**: > > - [Annual Chronic Disease Patients Treated](#sec-chronic_disease_treated_patients_annual) 🔢: 982 million people > - [Regulatory Delay for Efficacy Testing Post-Safety Verification](#sec-efficacy_lag_years) 📊: 8.2 years (SE: ±2 years) > - [Treatment Disability Reduction](#sec-treatment_disability_reduction) 📊: 0.25 weight (95% CI: 0.15 weight - 0.35 weight) > > $$ \begin{gathered} YLD_{treat\_delay} \\ = N_{treated} \times T_{lag} \times \Delta DW_{treat} \\ = 982M \times 8.2 \times 0.25 \\ = 2.01B \end{gathered} $$ where: $$ \begin{gathered} N_{treated} \\ = DOT_{chronic} \times 0.000767 \\ = 1.28T \times 0.000767 \\ = 982M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treatment Delay YLD - Annual** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Regulatory Delay for Efficacy Testing Post-Safety Verification (years) | 0.7122 | Strong driver | > | Treatment Disability Reduction (weight) | 0.5988 | Strong driver | > | Annual Chronic Disease Patients Treated (people) | 0.2936 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treatment Delay YLD - Annual (10,000 simulations) > > **Simulation Results Summary: Treatment Delay YLD - Annual** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 2.01 billion | > | Mean (expected value) | 2.02 billion | > | Median (50th percentile) | 1.95 billion | > | Standard Deviation | 682 million | > | 90% Range (5th-95th percentile) | \[1.02 billion, 3.24 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treatment Delay YLD - Annual; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treatment Delay YLD - Annual > > *This exceedance probability chart shows the likelihood that Treatment Delay YLD - Annual will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### EOS Equity Value (V): \$74.1 trillion > **NOTE:** > > Total addressable value for EOS equity: NPV of the fraction of political dysfunction tax EOS captures as portfolio appreciation. V in the share price formula: price = P(I) x V / total_shares. Calculated as dysfunction_tax x capture_pct / discount_rate. > > **Inputs**: > > - [Global Opportunity Cost Total](#sec-political_dysfunction_global_opportunity_cost_total) 🔢: \$101 trillion > - [EOS Social Value Capture Rate](#sec-eos_social_value_capture_pct) 📊: 2.2% (95% CI: 0.5% - 5%) > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > > $$ \begin{gathered} V_{EOS} \\ = O_{total} \times \frac{\phi_{capture}}{r_{discount}} \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for EOS Equity Value (V)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | EOS Social Value Capture Rate (percent) | 0.7820 | Strong driver | > | Global Opportunity Cost Total (USD) | 0.5615 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: EOS Equity Value (V) (10,000 simulations) > > **Simulation Results Summary: EOS Equity Value (V)** > > | Statistic | Value | > |:--------------------------------|------------------------------------:| > | Baseline (deterministic) | \$74.1 trillion | > | Mean (expected value) | \$73.4 trillion | > | Median (50th percentile) | \$63.7 trillion | > | Standard Deviation | \$40.8 trillion | > | 90% Range (5th-95th percentile) | \[\$27.2 trillion, \$153 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for EOS Equity Value (V); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: EOS Equity Value (V) > > *This exceedance probability chart shows the likelihood that EOS Equity Value (V) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923): 23.2% > **NOTE:** > > How much richer a diversified Ethyl Gasoline Corporation shareholder would have been by 1996 had the board switched to ethanol in 1923: the economy compounds faster without the lead-induced IQ loss, and diversified shareholder wealth scales with the economy. Central ~23%; the Monte Carlo range spans roughly ‘twenty to sixty percent richer’. Foregone TEL royalties are second-order against the economy-wide effect. > > **Inputs**: > > - [US Average IQ Loss from Leaded Gasoline](#sec-leaded_gasoline_us_avg_iq_loss_points) 📊: 2.6 IQ points (95% CI: 1.5 IQ points - 5.9 IQ points) > - [GDP Growth Effect per National IQ Point](#sec-national_iq_gdp_growth_effect_per_point) 📊: 0.11% (95% CI: 0.04% - 0.18%) > - [Leaded Gasoline Era Duration](#sec-leaded_gasoline_era_years): 73 years > > $$ \begin{gathered} \Delta W_{Ethyl} \\ = (1 + \Delta IQ_{lead} \times \beta_{IQ})^{T_{lead}} - 1 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | US Average IQ Loss from Leaded Gasoline (IQ points) | 0.7610 | Strong driver | > | GDP Growth Effect per National IQ Point (rate) | 0.5812 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923) (10,000 simulations) > > **Simulation Results Summary: Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 23.2% | > | Mean (expected value) | 24.1% | > | Median (50th percentile) | 20.5% | > | Standard Deviation | 14.3% | > | 90% Range (5th-95th percentile) | \[8.41%, 52.4%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923) > > *This exceedance probability chart shows the likelihood that Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Deaths from Historical Progress Delays: 102 million deaths > **NOTE:** > > Total deaths from delaying existing drugs over 8.2-year efficacy lag. One-time impact of eliminating Phase 2-4 testing delay for drugs already approved 1962-2024. Based on Lichtenberg (2019) estimate of 12M lives saved annually × 8.2 years efficacy lag. Excludes innovation acceleration effects. > > **Inputs**: > > - [Annual Lives Saved by Pharmaceuticals](#sec-pharma_lives_saved_annual) 🔢: 12.4 million deaths > - [Regulatory Delay for Efficacy Testing Post-Safety Verification](#sec-efficacy_lag_years) 📊: 8.2 years (SE: ±2 years) > > $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Deaths from Historical Progress Delays** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Lives Saved by Pharmaceuticals (deaths) | 0.7195 | Strong driver | > | Regulatory Delay for Efficacy Testing Post-Safety Verification (years) | 0.6685 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Deaths from Historical Progress Delays (10,000 simulations) > > **Simulation Results Summary: Total Deaths from Historical Progress Delays** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 102 million | > | Mean (expected value) | 101 million | > | Median (50th percentile) | 96.3 million | > | Standard Deviation | 36.3 million | > | 90% Range (5th-95th percentile) | \[50.8 million, 169 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Deaths from Historical Progress Delays; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Deaths from Historical Progress Delays > > *This exceedance probability chart shows the likelihood that Total Deaths from Historical Progress Delays will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Economic Loss from Historical Progress Delays: \$290 trillion > **NOTE:** > > Total economic loss from delaying existing drugs over 8.2-year efficacy lag. One-time benefit of eliminating Phase 2-4 delay. Excludes innovation acceleration effects. Years lost per death uses WHO conditional remaining life expectancy at 60 adjusted to the mean lag-death age (~19 years/death), not life-expectancy-at-birth minus age, which understated remaining years by ~40%. > > **Inputs**: > > - [Remaining Life Expectancy at Age 60 (Global)](#sec-global_life_expectancy_remaining_at_age_60) 📊: 21 years (95% CI: 19.6 years - 22 years) > - [Total Deaths from Historical Progress Delays](#sec-existing_drugs_efficacy_lag_deaths_total) 🔢: 102 million deaths > - [Mean Age of Preventable Death from Post-Safety Efficacy Delay](#sec-regulatory_delay_mean_age_of_death) 📊: 62 years (SE: ±3 years) > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} Loss_{lag} \\ = \text{DEATHS\_TOTAL} \times (REMAINING_LIFE_EXPECTANCY_AT_60 - (\text{MEAN\_AGE\_OF\_DEATH} - 60)) \times \text{VSLY} \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Economic Loss from Historical Progress Delays** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Deaths from Historical Progress Delays (deaths) | 0.8120 | Strong driver | > | Standard Economic Value per QALY (USD/QALY) | 0.4152 | Moderate driver | > | Mean Age of Preventable Death from Post-Safety Efficacy Delay (years) | -0.3576 | Moderate driver | > | Remaining Life Expectancy at Age 60 (Global) (years) | 0.0693 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Economic Loss from Historical Progress Delays (10,000 simulations) > > **Simulation Results Summary: Total Economic Loss from Historical Progress Delays** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$290 trillion | > | Mean (expected value) | \$288 trillion | > | Median (50th percentile) | \$265 trillion | > | Standard Deviation | \$127 trillion | > | 90% Range (5th-95th percentile) | \[\$123 trillion, \$527 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Economic Loss from Historical Progress Delays; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Economic Loss from Historical Progress Delays > > *This exceedance probability chart shows the likelihood that Total Economic Loss from Historical Progress Delays will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Therapeutic Frontier Exploration Ratio: 0.342% > **NOTE:** > > Fraction of possible drug-disease space actually tested (\<1%) > > **Inputs**: > > - [Tested Drug-Disease Relationships](#sec-tested_relationships_estimate): 32,500 relationships (95% CI: 15,000 relationships - 50,000 relationships) > - [Possible Drug-Disease Combinations](#sec-drug_disease_combinations_possible) 🔢: 9.5 million combinations > > $$ \begin{gathered} Ratio_{explore} \\ = \frac{N_{tested}}{N_{combos}} \\ = \frac{32{,}500}{9.5M} \\ = 0.342\% \end{gathered} $$ where: $$ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Therapeutic Frontier Exploration Ratio** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Tested Drug-Disease Relationships (relationships) | 0.7794 | Strong driver | > | Possible Drug-Disease Combinations (combinations) | -0.5916 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Therapeutic Frontier Exploration Ratio (10,000 simulations) > > **Simulation Results Summary: Therapeutic Frontier Exploration Ratio** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.342% | > | Mean (expected value) | 0.354% | > | Median (50th percentile) | 0.337% | > | Standard Deviation | 0.116% | > | 90% Range (5th-95th percentile) | \[0.197%, 0.569%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Therapeutic Frontier Exploration Ratio; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Therapeutic Frontier Exploration Ratio > > *This exceedance probability chart shows the likelihood that Therapeutic Frontier Exploration Ratio will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier: 32.8x > **NOTE:** > > Efficacy testing time vs Oxford RECOVERY trial (8.2 years ÷ 3 months = 32.8x slower). Compares efficacy lag only (post-safety Phase II/III) since RECOVERY was an efficacy trial. > > **Inputs**: > > - [Regulatory Delay for Efficacy Testing Post-Safety Verification](#sec-efficacy_lag_years) 📊: 8.2 years (SE: ±2 years) > - [Oxford RECOVERY Trial Duration](#sec-oxford_recovery_trial_duration_months) 📊: 3 months > > $$ \begin{gathered} k_{FDA:RECOVERY} \\ = T_{lag} \times \text{MONTHS\_PER\_YEAR} / T_{RECOVERY} \end{gathered} $$ > > **Methodology**:^([8](../references.llms.md#ref-recovery-trial-82x-cost-reduction)) > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Regulatory Delay for Efficacy Testing Post-Safety Verification (years) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier (10,000 simulations) > > **Simulation Results Summary: FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 32.8x | > | Mean (expected value) | 32.8x | > | Median (50th percentile) | 32.8x | > | Standard Deviation | 7.92x | > | 90% Range (5th-95th percentile) | \[19.4x, 45.9x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier > > *This exceedance probability chart shows the likelihood that FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Full Corporate Activist-Stake Cost (All Sectors): \$884 billion > **NOTE:** > > Realistic cost to take an activist (non-control) stake across EVERY government-controlling industry at once: military, pharma, tech, insurance, and oil and gas. This is the symmetric activist version of the takeover. Outright majority control of all of them is not even possible (founder and mutual control of Meta, Alphabet, Oracle, and the insurance mutuals), which is exactly why the activist tier, not a 50.1% buyout, is the operative model outside the defense primes. > > **Inputs**: > > - [US Military Primes Market Cap](#sec-defense_primes_market_cap_us) 📊: \$836 billion > - [Allied Military Primes Market Cap](#sec-defense_primes_market_cap_allied) 📊: \$132 billion > - [Government-Controlling Sectors Top-5 Market Cap](#sec-gov_controlling_sectors_top5_market_cap) 📊: \$16.7 trillion (95% CI: \$15 trillion - \$18 trillion) > - [Activist Stake Fraction](#sec-influence_activist_stake_fraction): 0.05:1 (95% CI: 0.01:1 - 0.1:1) > > $$ \begin{gathered} C_{corp,activist} \\ = (MarketCap_{US} + MarketCap_{allied} \\ + MarketCap_{sectors}) \times f_{activist} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** Full Corporate Activist-Stake Cost (All Sectors) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 8.839e-01) > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$884 billion | > | Mean (expected value) | \$884 billion | > | Median (50th percentile) | \$884 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$884 billion, \$884 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Full Corporate Activist-Stake Cost (All Sectors) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 8.839e-01) > > Approximate deterministic value: **\$884 billion** ### Full Corporate Activist-Stake Cost as Share of Global Investable Assets: 0.29% > **NOTE:** > > Activist stake across every government-controlling industry as a share of total global investable assets. > > **Inputs**: > > - [Full Corporate Activist-Stake Cost (All Sectors)](#sec-full_corporate_takeover_cost_activist) 🔢: \$884 billion > - [Global Investable Financial Assets](#sec-global_investable_assets) 📊: \$305 trillion > > $$ \begin{gathered} C_{corp,activist}/A_{investable} \\ = \frac{C_{corp,activist}}{Assets_{invest}} \\ = \frac{\$884B}{\$305T} \\ = 0.29\% \end{gathered} $$ where: $$ \begin{gathered} C_{corp,activist} \\ = (MarketCap_{US} + MarketCap_{allied} \\ + MarketCap_{sectors}) \times f_{activist} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** Full Corporate Activist-Stake Cost as Share of Global Investable Assets is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 0.29% | > | Mean (expected value) | 0.29% | > | Median (50th percentile) | 0.29% | > | Standard Deviation | 4.34e-17% | > | 90% Range (5th-95th percentile) | \[0.29%, 0.29%\] | > > #### Exceedance Probability > > **Exceedance note:** Full Corporate Activist-Stake Cost as Share of Global Investable Assets collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **0.29%** ### Total Annual Conflict Deaths Globally: 245 thousand deaths/year > **NOTE:** > > Total annual conflict deaths globally (sum of combat, terror, state violence) > > **Inputs**: > > - [Annual Deaths from Active Combat Worldwide](#sec-global_annual_conflict_deaths_active_combat) 📊: 234 thousand deaths/year (95% CI: 180 thousand deaths/year - 300 thousand deaths/year) > - [Annual Deaths from State Violence](#sec-global_annual_conflict_deaths_state_violence) 📊: 2,700 deaths/year (95% CI: 1,500 deaths/year - 5,000 deaths/year) > - [Annual Deaths from Terror Attacks Globally](#sec-global_annual_conflict_deaths_terror_attacks) 📊: 8,300 deaths/year (95% CI: 6,000 deaths/year - 12,000 deaths/year) > > $$ \begin{gathered} Deaths_{conflict} \\ = Deaths_{combat} + Deaths_{state} + Deaths_{terror} \\ = 234{,}000 + 2{,}700 + 8{,}300 \\ = 245{,}000 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Conflict Deaths Globally** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Deaths from Active Combat Worldwide (deaths/year) | 0.9979 | Strong driver | > | Annual Deaths from Terror Attacks Globally (deaths/year) | 0.0508 | Minimal effect | > | Annual Deaths from State Violence (deaths/year) | 0.0288 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Conflict Deaths Globally (10,000 simulations) > > **Simulation Results Summary: Total Annual Conflict Deaths Globally** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 245 thousand | > | Mean (expected value) | 245 thousand | > | Median (50th percentile) | 243 thousand | > | Standard Deviation | 29,010 | > | 90% Range (5th-95th percentile) | \[198 thousand, 297 thousand\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Conflict Deaths Globally; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Conflict Deaths Globally > > *This exceedance probability chart shows the likelihood that Total Annual Conflict Deaths Globally will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Annual Cost of War Worldwide: \$11.4 trillion > **NOTE:** > > Total annual cost of war worldwide (direct + indirect costs) > > **Inputs**: > > - [Total Annual Direct War Costs](#sec-global_annual_war_direct_costs_total) 🔢: \$7.66 trillion > - [Total Annual Indirect War Costs](#sec-global_annual_war_indirect_costs_total) 🔢: \$3.7 trillion > > $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Cost of War Worldwide** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Direct War Costs (USD/year) | 0.8460 | Strong driver | > | Total Annual Indirect War Costs (USD/year) | 0.5312 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Cost of War Worldwide (10,000 simulations) > > **Simulation Results Summary: Total Annual Cost of War Worldwide** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$11.4 trillion | > | Mean (expected value) | \$11.3 trillion | > | Median (50th percentile) | \$11.3 trillion | > | Standard Deviation | \$877 billion | > | 90% Range (5th-95th percentile) | \[\$9.96 trillion, \$12.9 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Cost of War Worldwide; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Cost of War Worldwide > > *This exceedance probability chart shows the likelihood that Total Annual Cost of War Worldwide will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Cost of Combat Deaths: \$2.34 trillion > **NOTE:** > > Annual cost of combat deaths (deaths × VSL) > > **Inputs**: > > - [Annual Deaths from Active Combat Worldwide](#sec-global_annual_conflict_deaths_active_combat) 📊: 234 thousand deaths/year (95% CI: 180 thousand deaths/year - 300 thousand deaths/year) > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Cost of Combat Deaths** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Value of Statistical Life (USD) | 0.9051 | Strong driver | > | Annual Deaths from Active Combat Worldwide (deaths/year) | 0.4089 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Cost of Combat Deaths (10,000 simulations) > > **Simulation Results Summary: Annual Cost of Combat Deaths** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$2.34 trillion | > | Mean (expected value) | \$2.31 trillion | > | Median (50th percentile) | \$2.23 trillion | > | Standard Deviation | \$699 billion | > | 90% Range (5th-95th percentile) | \[\$1.27 trillion, \$3.55 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Cost of Combat Deaths; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Cost of Combat Deaths > > *This exceedance probability chart shows the likelihood that Annual Cost of Combat Deaths will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Cost of State Violence Deaths: \$27 billion > **NOTE:** > > Annual cost of state violence deaths (deaths × VSL) > > **Inputs**: > > - [Annual Deaths from State Violence](#sec-global_annual_conflict_deaths_state_violence) 📊: 2,700 deaths/year (95% CI: 1,500 deaths/year - 5,000 deaths/year) > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Cost of State Violence Deaths** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Deaths from State Violence (deaths/year) | 0.7256 | Strong driver | > | Value of Statistical Life (USD) | 0.6501 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Cost of State Violence Deaths (10,000 simulations) > > **Simulation Results Summary: Annual Cost of State Violence Deaths** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$27 billion | > | Mean (expected value) | \$26.7 billion | > | Median (50th percentile) | \$24.5 billion | > | Standard Deviation | \$11.3 billion | > | 90% Range (5th-95th percentile) | \[\$12.2 billion, \$48.2 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Cost of State Violence Deaths; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Cost of State Violence Deaths > > *This exceedance probability chart shows the likelihood that Annual Cost of State Violence Deaths will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Cost of Terror Deaths: \$83 billion > **NOTE:** > > Annual cost of terror deaths (deaths × VSL) > > **Inputs**: > > - [Annual Deaths from Terror Attacks Globally](#sec-global_annual_conflict_deaths_terror_attacks) 📊: 8,300 deaths/year (95% CI: 6,000 deaths/year - 12,000 deaths/year) > - [Value of Statistical Life](#sec-value_of_statistical_life) 📊: \$10 million (95% CI: \$5 million - \$15 million) > > $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Cost of Terror Deaths** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Value of Statistical Life (USD) | 0.8343 | Strong driver | > | Annual Deaths from Terror Attacks Globally (deaths/year) | 0.5350 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Cost of Terror Deaths (10,000 simulations) > > **Simulation Results Summary: Annual Cost of Terror Deaths** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$83 billion | > | Mean (expected value) | \$82.2 billion | > | Median (50th percentile) | \$78.9 billion | > | Standard Deviation | \$27 billion | > | 90% Range (5th-95th percentile) | \[\$43.2 billion, \$132 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Cost of Terror Deaths; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Cost of Terror Deaths > > *This exceedance probability chart shows the likelihood that Annual Cost of Terror Deaths will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Annual Human Life Losses from Conflict: \$2.45 trillion > **NOTE:** > > Total annual human life losses from conflict (sum of combat, terror, state violence) > > **Inputs**: > > - [Annual Cost of Combat Deaths](#sec-global_annual_human_cost_active_combat) 🔢: \$2.34 trillion > - [Annual Cost of State Violence Deaths](#sec-global_annual_human_cost_state_violence) 🔢: \$27 billion > - [Annual Cost of Terror Deaths](#sec-global_annual_human_cost_terror_attacks) 🔢: \$83 billion > > $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Human Life Losses from Conflict** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Cost of Combat Deaths (USD/year) | 0.9622 | Strong driver | > | Annual Cost of Terror Deaths (USD/year) | 0.0372 | Minimal effect | > | Annual Cost of State Violence Deaths (USD/year) | 0.0156 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Human Life Losses from Conflict (10,000 simulations) > > **Simulation Results Summary: Total Annual Human Life Losses from Conflict** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$2.45 trillion | > | Mean (expected value) | \$2.41 trillion | > | Median (50th percentile) | \$2.34 trillion | > | Standard Deviation | \$727 billion | > | 90% Range (5th-95th percentile) | \[\$1.34 trillion, \$3.71 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Human Life Losses from Conflict; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Human Life Losses from Conflict > > *This exceedance probability chart shows the likelihood that Total Annual Human Life Losses from Conflict will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Annual Infrastructure Destruction: \$1.88 trillion > **NOTE:** > > Total annual infrastructure destruction (sum of transportation, energy, communications, water, education, healthcare) > > **Inputs**: > > - [Annual Infrastructure Damage to Communications from Conflict](#sec-global_annual_infrastructure_damage_communications_conflict) 📊: \$298 billion (95% CI: \$209 billion - \$418 billion) > - [Annual Infrastructure Damage to Education Facilities from Conflict](#sec-global_annual_infrastructure_damage_education_conflict) 📊: \$234 billion (95% CI: \$164 billion - \$328 billion) > - [Annual Infrastructure Damage to Energy Systems from Conflict](#sec-global_annual_infrastructure_damage_energy_conflict) 📊: \$422 billion (95% CI: \$295 billion - \$590 billion) > - [Annual Infrastructure Damage to Healthcare Facilities from Conflict](#sec-global_annual_infrastructure_damage_healthcare_conflict) 📊: \$166 billion (95% CI: \$116 billion - \$232 billion) > - [Annual Infrastructure Damage to Transportation from Conflict](#sec-global_annual_infrastructure_damage_transportation_conflict) 📊: \$487 billion (95% CI: \$340 billion - \$680 billion) > - [Annual Infrastructure Damage to Water Systems from Conflict](#sec-global_annual_infrastructure_damage_water_conflict) 📊: \$268 billion (95% CI: \$187 billion - \$375 billion) > > $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Infrastructure Destruction** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Infrastructure Damage to Transportation from Conflict (USD) | 0.6025 | Strong driver | > | Annual Infrastructure Damage to Energy Systems from Conflict (USD) | 0.5271 | Strong driver | > | Annual Infrastructure Damage to Communications from Conflict (USD) | 0.3730 | Moderate driver | > | Annual Infrastructure Damage to Water Systems from Conflict (USD) | 0.3317 | Moderate driver | > | Annual Infrastructure Damage to Education Facilities from Conflict (USD) | 0.2943 | Weak driver | > | Annual Infrastructure Damage to Healthcare Facilities from Conflict (USD) | 0.2074 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Infrastructure Destruction (10,000 simulations) > > **Simulation Results Summary: Total Annual Infrastructure Destruction** > > | Statistic | Value | > |:--------------------------------|------------------------------------:| > | Baseline (deterministic) | \$1.88 trillion | > | Mean (expected value) | \$1.87 trillion | > | Median (50th percentile) | \$1.87 trillion | > | Standard Deviation | \$136 billion | > | 90% Range (5th-95th percentile) | \[\$1.65 trillion, \$2.1 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Infrastructure Destruction; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Infrastructure Destruction > > *This exceedance probability chart shows the likelihood that Total Annual Infrastructure Destruction will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Annual Savings: \$31.1 trillion > **NOTE:** > > Global annual savings in USD (savings rate × GDP) > > **Inputs**: > > - [Global Gross Savings Rate](#sec-global_savings_rate_pct) 📊: 27% (95% CI: 24% - 30%) > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > > $$ \begin{gathered} S_{annual} \\ = s_{global} \times GDP_{global} \\ = 27\% \times \$115T \\ = \$31.1T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Annual Savings** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:------------------------------------|------------------------:|:---------------| > | Global Gross Savings Rate (percent) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Annual Savings (10,000 simulations) > > **Simulation Results Summary: Global Annual Savings** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$31.1 trillion | > | Mean (expected value) | \$31.1 trillion | > | Median (50th percentile) | \$31.1 trillion | > | Standard Deviation | \$1.68 trillion | > | 90% Range (5th-95th percentile) | \[\$28.2 trillion, \$34 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Annual Savings; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Annual Savings > > *This exceedance probability chart shows the likelihood that Global Annual Savings will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Annual Savings Per Capita: \$3,881 > **NOTE:** > > Global annual savings divided by global population. Useful as a rough average-person default for prize-contribution sizing. > > **Inputs**: > > - [Global Annual Savings](#sec-global_annual_savings) 🔢: \$31.1 trillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} S_{annual,pc} \\ = \frac{S_{annual}}{Pop_{global}} \\ = \frac{\$31.1T}{8B} \\ = \$3.88K \end{gathered} $$ where: $$ \begin{gathered} S_{annual} \\ = s_{global} \times GDP_{global} \\ = 27\% \times \$115T \\ = \$31.1T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Annual Savings Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Annual Savings (USD) | 0.9740 | Strong driver | > | Global Population in 2024 (of people) | -0.2192 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Annual Savings Per Capita (10,000 simulations) > > **Simulation Results Summary: Global Annual Savings Per Capita** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$3,881 | > | Mean (expected value) | \$3,885 | > | Median (50th percentile) | \$3,885 | > | Standard Deviation | \$215 | > | 90% Range (5th-95th percentile) | \[\$3,518, \$4,250\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Annual Savings Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Annual Savings Per Capita > > *This exceedance probability chart shows the likelihood that Global Annual Savings Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Annual Trade Disruption: \$616 billion > **NOTE:** > > Total annual trade disruption (sum of shipping, supply chain, energy prices, currency instability) > > **Inputs**: > > - [Annual Trade Disruption Costs from Currency Instability](#sec-global_annual_trade_disruption_currency_conflict) 📊: \$57.4 billion (95% CI: \$40 billion - \$80 billion) > - [Annual Trade Disruption Costs from Energy Price Volatility](#sec-global_annual_trade_disruption_energy_price_conflict) 📊: \$125 billion (95% CI: \$87 billion - \$175 billion) > - [Annual Trade Disruption Costs from Shipping Disruptions](#sec-global_annual_trade_disruption_shipping_conflict) 📊: \$247 billion (95% CI: \$173 billion - \$346 billion) > - [Annual Trade Disruption Costs from Supply Chain Disruptions](#sec-global_annual_trade_disruption_supply_chain_conflict) 📊: \$187 billion (95% CI: \$131 billion - \$262 billion) > > $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Trade Disruption** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Trade Disruption Costs from Shipping Disruptions (USD) | 0.7219 | Strong driver | > | Annual Trade Disruption Costs from Supply Chain Disruptions (USD) | 0.5511 | Strong driver | > | Annual Trade Disruption Costs from Energy Price Volatility (USD) | 0.3677 | Moderate driver | > | Annual Trade Disruption Costs from Currency Instability (USD) | 0.1676 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Trade Disruption (10,000 simulations) > > **Simulation Results Summary: Total Annual Trade Disruption** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$616 billion | > | Mean (expected value) | \$615 billion | > | Median (50th percentile) | \$613 billion | > | Standard Deviation | \$57.8 billion | > | 90% Range (5th-95th percentile) | \[\$526 billion, \$716 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Trade Disruption; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Trade Disruption > > *This exceedance probability chart shows the likelihood that Total Annual Trade Disruption will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Annual Direct War Costs: \$7.66 trillion > **NOTE:** > > Total annual direct war costs (military spending + infrastructure + human life + trade disruption) > > **Inputs**: > > - [Total Annual Human Life Losses from Conflict](#sec-global_annual_human_life_losses_conflict) 🔢: \$2.45 trillion > - [Total Annual Infrastructure Destruction](#sec-global_annual_infrastructure_destruction_conflict) 🔢: \$1.88 trillion > - [Total Annual Trade Disruption](#sec-global_annual_trade_disruption_conflict) 🔢: \$616 billion > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > > $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Direct War Costs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Human Life Losses from Conflict (USD/year) | 0.9800 | Strong driver | > | Total Annual Infrastructure Destruction (USD/year) | 0.1840 | Weak driver | > | Total Annual Trade Disruption (USD/year) | 0.0780 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Direct War Costs (10,000 simulations) > > **Simulation Results Summary: Total Annual Direct War Costs** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$7.66 trillion | > | Mean (expected value) | \$7.62 trillion | > | Median (50th percentile) | \$7.56 trillion | > | Standard Deviation | \$742 billion | > | 90% Range (5th-95th percentile) | \[\$6.52 trillion, \$8.95 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Direct War Costs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Direct War Costs > > *This exceedance probability chart shows the likelihood that Total Annual Direct War Costs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Annual Indirect War Costs: \$3.7 trillion > **NOTE:** > > Total annual indirect war costs (opportunity cost + veterans + refugees + environment + mental health + lost productivity) > > **Inputs**: > > - [Annual Environmental Damage and Restoration Costs from Conflict](#sec-global_annual_environmental_damage_conflict) 📊: \$100 billion (95% CI: \$70 billion - \$140 billion) > - [Annual Lost Economic Growth from Military Spending Opportunity Cost](#sec-global_annual_lost_economic_growth_military_spending) 📊: \$2.72 trillion (95% CI: \$1.9 trillion - \$3.8 trillion) > - [Annual Lost Productivity from Conflict Casualties](#sec-global_annual_lost_human_capital_conflict) 📊: \$300 billion (95% CI: \$210 billion - \$420 billion) > - [Annual PTSD and Mental Health Costs from Conflict](#sec-global_annual_psychological_impact_costs_conflict) 📊: \$232 billion (95% CI: \$162 billion - \$325 billion) > - [Annual Refugee Support Costs](#sec-global_annual_refugee_support_costs) 📊: \$150 billion (95% CI: \$105 billion - \$210 billion) > - [Annual Veteran Healthcare Costs](#sec-global_annual_veteran_healthcare_costs) 📊: \$200 billion (95% CI: \$140 billion - \$280 billion) > > $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Indirect War Costs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Lost Economic Growth from Military Spending Opportunity Cost (USD) | 0.9810 | Strong driver | > | Annual Lost Productivity from Conflict Casualties (USD) | 0.1093 | Weak driver | > | Annual PTSD and Mental Health Costs from Conflict (USD) | 0.0841 | Minimal effect | > | Annual Veteran Healthcare Costs (USD) | 0.0737 | Minimal effect | > | Annual Refugee Support Costs (USD) | 0.0543 | Minimal effect | > | Annual Environmental Damage and Restoration Costs from Conflict (USD) | 0.0366 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Indirect War Costs (10,000 simulations) > > **Simulation Results Summary: Total Annual Indirect War Costs** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$3.7 trillion | > | Mean (expected value) | \$3.7 trillion | > | Median (50th percentile) | \$3.66 trillion | > | Standard Deviation | \$466 billion | > | 90% Range (5th-95th percentile) | \[\$2.98 trillion, \$4.56 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Indirect War Costs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Indirect War Costs > > *This exceedance probability chart shows the likelihood that Total Annual Indirect War Costs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Average Hourly Income: \$7.19 > **NOTE:** > > Global average hourly income derived from GDP per capita. Uses average (not median), which overestimates the cost of sharing, making the payoff ratio conservative. > > **Inputs**: > > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > - [Annual Working Hours](#sec-annual_working_hours): 2,000 hours/year > > $$ \begin{gathered} \bar{w}_{hour} \\ = \frac{\bar{y}_{0}}{H_{work}} \\ = \frac{\$14.4K}{2{,}000} \\ = \$7.19 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Average Hourly Income** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Average Income (2025 Baseline) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Average Hourly Income (10,000 simulations) > > **Simulation Results Summary: Global Average Hourly Income** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | \$7.19 | > | Mean (expected value) | \$7.19 | > | Median (50th percentile) | \$7.19 | > | Standard Deviation | \$0.087 | > | 90% Range (5th-95th percentile) | \[\$7.04, \$7.34\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Average Hourly Income; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Average Hourly Income > > *This exceedance probability chart shows the likelihood that Global Average Hourly Income will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Average Income (2025 Baseline): \$14,375 > **NOTE:** > > Global average income (GDP per capita) in 2025 baseline. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Average Income (2025 Baseline)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Population in 2024 (of people) | -0.9999 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Average Income (2025 Baseline) (10,000 simulations) > > **Simulation Results Summary: Global Average Income (2025 Baseline)** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$14,375 | > | Mean (expected value) | \$14,376 | > | Median (50th percentile) | \$14,372 | > | Standard Deviation | \$175 | > | 90% Range (5th-95th percentile) | \[\$14,081, \$14,682\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Average Income (2025 Baseline); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Average Income (2025 Baseline) > > *This exceedance probability chart shows the likelihood that Global Average Income (2025 Baseline) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Average Remaining Years (Median Person): 42.9 years > **NOTE:** > > Average remaining lifespan for the median-age person. Conservative: uses life expectancy at birth minus median age, which underestimates remaining years because survivors to age 30 have higher conditional life expectancy. > > **Inputs**: > > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > - [Global Median Age (2024)](#sec-global_median_age_2024) 📊: 30.5 years > > $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Average Remaining Years (Median Person)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Life Expectancy (2024) (years) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Average Remaining Years (Median Person) (10,000 simulations) > > **Simulation Results Summary: Average Remaining Years (Median Person)** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 42.9 | > | Mean (expected value) | 42.9 | > | Median (50th percentile) | 42.9 | > | Standard Deviation | 1.91 | > | 90% Range (5th-95th percentile) | \[39.6, 46.1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Average Remaining Years (Median Person); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Average Remaining Years (Median Person) > > *This exceedance probability chart shows the likelihood that Average Remaining Years (Median Person) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Bullets Purchasable with Global Military Budget: 6.8 trillion rounds > **NOTE:** > > Number of 5.56mm NATO rounds purchasable with the entire global military budget at bulk procurement prices. Pure purchasing power calculation, not a combat efficiency estimate. > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Cost per 5.56mm NATO Round (Bulk)](#sec-bullet_cost_556_nato) 📊: \$0.4 (95% CI: \$0.25 - \$0.6) > > $$ \begin{gathered} N_{bullets,yr} \\ = \frac{Spending_{mil}}{c_{bullet}} \\ = \frac{\$2.72T}{\$0.4} \\ = 6.8T \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Bullets Purchasable with Global Military Budget** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Cost per 5.56mm NATO Round (Bulk) (USD) | -0.9746 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Bullets Purchasable with Global Military Budget (10,000 simulations) > > **Simulation Results Summary: Bullets Purchasable with Global Military Budget** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | 6.8 trillion | > | Mean (expected value) | 6.79 trillion | > | Median (50th percentile) | 6.39 trillion | > | Standard Deviation | 1.74 trillion | > | 90% Range (5th-95th percentile) | \[4.66 trillion, 10.2 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Bullets Purchasable with Global Military Budget; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Bullets Purchasable with Global Military Budget > > *This exceedance probability chart shows the likelihood that Bullets Purchasable with Global Military Budget will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Coordination Activation Budget: \$30.8 billion > **NOTE:** > > Canonical institutional activation threshold: capital required to make participation by the majority-of-humanity coordination target credible through direct referral incentives, verification, payment rails, and global launch operations. This is the main institutional ask, not the prize pool seed benchmark. > > **Inputs**: > > - [Global Registered Voters](#sec-global_registered_voters) 📊: 4.13 billion of people > - [Activation Cost per Participant](#sec-global_coordination_activation_cost_per_participant) 🔢: \$6.5 > - [Global Coordination Platform and Operations Cost](#sec-global_coordination_platform_and_operations_cost): \$4 billion (95% CI: \$2 billion - \$8 billion) > > $$ \begin{gathered} B_{activate} \\ = C_{ops} + N_{voters,global} \times C_{activate,pp} \\ = \$4B + 4.13B \times \$6.5 \\ = \$30.8B \end{gathered} $$ where: $$ \begin{gathered} C_{activate,pp} \\ = R_{activate} + C_{verify,pp} \\ = \$5 + \$1.5 \\ = \$6.5 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Coordination Activation Budget** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Activation Cost per Participant (USD) | 0.9798 | Strong driver | > | Global Coordination Platform and Operations Cost (USD) | 0.2111 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Coordination Activation Budget (10,000 simulations) > > **Simulation Results Summary: Global Coordination Activation Budget** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$30.8 billion | > | Mean (expected value) | \$31 billion | > | Median (50th percentile) | \$31 billion | > | Standard Deviation | \$6.48 billion | > | 90% Range (5th-95th percentile) | \[\$20.4 billion, \$41.9 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Coordination Activation Budget; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Coordination Activation Budget > > *This exceedance probability chart shows the likelihood that Global Coordination Activation Budget will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Activation Cost per Participant: \$6.5 > **NOTE:** > > Blended variable activation cost per successful verified participant: direct incentive plus verification and payment operations. > > **Inputs**: > > - [Activation Reward per Verified Participant](#sec-global_coordination_activation_reward_per_verified_participant): \$5 (95% CI: \$2 - \$10) > - [Verification and Payment Cost per Participant](#sec-global_coordination_verification_and_payment_cost_per_participant): \$1.5 (95% CI: \$1 - \$3) > > $$ \begin{gathered} C_{activate,pp} \\ = R_{activate} + C_{verify,pp} \\ = \$5 + \$1.5 \\ = \$6.5 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Activation Cost per Participant** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Activation Reward per Verified Participant (USD) | 0.9623 | Strong driver | > | Verification and Payment Cost per Participant (USD) | 0.2810 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Activation Cost per Participant (10,000 simulations) > > **Simulation Results Summary: Activation Cost per Participant** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | \$6.5 | > | Mean (expected value) | \$6.54 | > | Median (50th percentile) | \$6.52 | > | Standard Deviation | \$1.54 | > | 90% Range (5th-95th percentile) | \[\$4, \$9.14\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Activation Cost per Participant; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Activation Cost per Participant > > *This exceedance probability chart shows the likelihood that Activation Cost per Participant will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Majority of Humanity Coordination Target: 51.6% > **NOTE:** > > Majority-of-humanity public coordination target as a share of global population, using global registered voters as the current verified-human headcount proxy. > > **Inputs**: > > - [Global Registered Voters](#sec-global_registered_voters) 📊: 4.13 billion of people > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} R_{humanity,majority} \\ = \frac{N_{voters,global}}{Pop_{global}} \\ = \frac{4.13B}{8B} \\ = 51.6\% \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Majority of Humanity Coordination Target** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Population in 2024 (of people) | -0.9999 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Majority of Humanity Coordination Target (10,000 simulations) > > **Simulation Results Summary: Majority of Humanity Coordination Target** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 51.6% | > | Mean (expected value) | 51.6% | > | Median (50th percentile) | 51.6% | > | Standard Deviation | 0.627% | > | 90% Range (5th-95th percentile) | \[50.5%, 52.7%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Majority of Humanity Coordination Target; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Majority of Humanity Coordination Target > > *This exceedance probability chart shows the likelihood that Majority of Humanity Coordination Target will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Destructive Economy (2025): \$13.2 trillion > **NOTE:** > > Combined annual cost of military spending and cybercrime. The ‘destructive economy’ that competes with the productive economy. > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Global Cybercrime Costs (2025)](#sec-global_cybercrime_cost_annual_2025) 📊: \$10.5 trillion > > $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Global Destructive Economy (2025) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.322e+01) > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$13.2 trillion | > | Mean (expected value) | \$13.2 trillion | > | Median (50th percentile) | \$13.2 trillion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$13.2 trillion, \$13.2 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** Global Destructive Economy (2025) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.322e+01) > > Approximate deterministic value: **\$13.2 trillion** ### Destructive Economy as % of GDP: 11.5% > **NOTE:** > > Destructive economy (military + cybercrime) as percentage of global GDP. > > **Inputs**: > > - [Global Destructive Economy (2025)](#sec-global_destructive_economy_annual_2025) 🔢: \$13.2 trillion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > > $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Destructive Economy as % of GDP is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 11.5% | > | Mean (expected value) | 11.5% | > | Median (50th percentile) | 11.5% | > | Standard Deviation | 0% | > | 90% Range (5th-95th percentile) | \[11.5%, 11.5%\] | > > #### Exceedance Probability > > **Exceedance note:** Destructive Economy as % of GDP collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **11.5%** ### Global Deaths per Minute from Disease: 104 deaths/minute > **NOTE:** > > Global deaths per minute from all disease and aging > > **Inputs**: > > - [Global Daily Deaths from Disease and Aging](#sec-global_disease_deaths_daily) 📊: 150 thousand deaths/day (SE: ±7,500 deaths/day) > > $$ \begin{gathered} Deaths_{disease,min} \\ = Deaths_{disease,daily} \times 0.000694 \\ = 150{,}000 \times 0.000694 \\ = 104 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Deaths per Minute from Disease** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Daily Deaths from Disease and Aging (deaths/day) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Deaths per Minute from Disease (10,000 simulations) > > **Simulation Results Summary: Global Deaths per Minute from Disease** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 104 | > | Mean (expected value) | 104 | > | Median (50th percentile) | 104 | > | Standard Deviation | 5.21 | > | 90% Range (5th-95th percentile) | \[95.4, 113\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Deaths per Minute from Disease; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Deaths per Minute from Disease > > *This exceedance probability chart shows the likelihood that Global Deaths per Minute from Disease will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Welfare Cost of Avoidable Disease: \$400 trillion > **NOTE:** > > Annual welfare cost of avoidable disease globally. Calculated as global DALY burden × eventually avoidable percentage × standard QALY value (\$150K). Uses consistent QALY valuation matching all other health impact calculations. Medical costs and productivity losses are NOT added separately to avoid double-counting (QALY valuation already captures these welfare components). > > **Inputs**: > > - [Global Annual DALY Burden](#sec-global_annual_daly_burden) 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year) > - [Eventually Avoidable DALY Percentage](#sec-eventually_avoidable_daly_pct): 92.6% (95% CI: 50% - 98%) > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} Burden_{disease} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times Value_{QALY} \\ = 2.88B \times 92.6\% \times \$150K \\ = \$400T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Welfare Cost of Avoidable Disease** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Standard Economic Value per QALY (USD/QALY) | 0.8093 | Strong driver | > | Eventually Avoidable DALY Percentage (percentage) | 0.5200 | Strong driver | > | Global Annual DALY Burden (DALYs/year) | 0.2294 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Welfare Cost of Avoidable Disease (10,000 simulations) > > **Simulation Results Summary: Annual Welfare Cost of Avoidable Disease** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$400 trillion | > | Mean (expected value) | \$397 trillion | > | Median (50th percentile) | \$397 trillion | > | Standard Deviation | \$89.5 trillion | > | 90% Range (5th-95th percentile) | \[\$252 trillion, \$544 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Welfare Cost of Avoidable Disease; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Welfare Cost of Avoidable Disease > > *This exceedance probability chart shows the likelihood that Annual Welfare Cost of Avoidable Disease will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Annual Total Market Cost of Disease: \$14.9 trillion > **NOTE:** > > Total annual market cost of disease globally: direct medical costs (\$9.9T) plus lost productivity from people too sick to work (\$5T). This is the cash-cost sum a payer or economy actually bears, distinct from the DALY-based welfare burden, and is deliberately NOT added to that burden to avoid double-counting. > > **Inputs**: > > - [Global Annual Direct Medical Costs of Disease](#sec-global_disease_direct_medical_cost_annual) 📊: \$9.9 trillion (95% CI: \$7 trillion - \$14 trillion) > - [Global Annual Productivity Loss from Disease](#sec-global_disease_productivity_loss_annual) 📊: \$5 trillion (95% CI: \$3.5 trillion - \$7 trillion) > > $$ \begin{gathered} Cost_{disease,market} \\ = Cost_{medical,direct} + Loss_{productivity} \\ = \$9.9T + \$5T \\ = \$14.9T \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Annual Total Market Cost of Disease** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Annual Direct Medical Costs of Disease (USD/year) | 0.8885 | Strong driver | > | Global Annual Productivity Loss from Disease (USD/year) | 0.4427 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Annual Total Market Cost of Disease (10,000 simulations) > > **Simulation Results Summary: Global Annual Total Market Cost of Disease** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$14.9 trillion | > | Mean (expected value) | \$14.9 trillion | > | Median (50th percentile) | \$14.8 trillion | > | Standard Deviation | \$1.92 trillion | > | 90% Range (5th-95th percentile) | \[\$11.9 trillion, \$18.3 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Annual Total Market Cost of Disease; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Annual Total Market Cost of Disease > > *This exceedance probability chart shows the likelihood that Global Annual Total Market Cost of Disease will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Eventually Avoidable Deaths per Day: 139 thousand deaths/day > **NOTE:** > > Daily global deaths that are eventually avoidable with sufficient biomedical research. Each day the disease-eradication date slips adds roughly this many deaths to the total schedule cost. > > **Inputs**: > > - [Global Daily Deaths from Disease and Aging](#sec-global_disease_deaths_daily) 📊: 150 thousand deaths/day (SE: ±7,500 deaths/day) > - [Eventually Avoidable Death Percentage](#sec-eventually_avoidable_death_pct): 92.6% (95% CI: 50% - 98%) > > $$ \begin{gathered} Deaths_{avoid,daily} \\ = Deaths_{disease,daily} \times Pct_{avoid,death} \\ = 150{,}000 \times 92.6\% \\ = 139{,}000 \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Eventually Avoidable Deaths per Day** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Eventually Avoidable Death Percentage (percentage) | 0.9246 | Strong driver | > | Global Daily Deaths from Disease and Aging (deaths/day) | 0.3805 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Eventually Avoidable Deaths per Day (10,000 simulations) > > **Simulation Results Summary: Eventually Avoidable Deaths per Day** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | 139 thousand | > | Mean (expected value) | 138 thousand | > | Median (50th percentile) | 143 thousand | > | Standard Deviation | 18,098 | > | 90% Range (5th-95th percentile) | \[95,673, 157 thousand\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Eventually Avoidable Deaths per Day; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Eventually Avoidable Deaths per Day > > *This exceedance probability chart shows the likelihood that Eventually Avoidable Deaths per Day will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Global Government Expense: \$36.5 trillion > **NOTE:** > > Approximate annual global government expenditure, computed as global GDP times the World Bank general-government expense share of GDP. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Global Government Expense Share of GDP](#sec-global_government_expense_pct_gdp) 📊: 31.8% > > $$ \begin{gathered} Expense_{gov,global} \\ = GDP_{global} \times p_{gov,expense} \\ = \$115T \times 31.8\% \\ = \$36.5T \end{gathered} $$ > > *~ Medium confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Annual Global Government Expense is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 3.655e+01) > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$36.5 trillion | > | Mean (expected value) | \$36.5 trillion | > | Median (50th percentile) | \$36.5 trillion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$36.5 trillion, \$36.5 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** Annual Global Government Expense collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 3.655e+01) > > Approximate deterministic value: **\$36.5 trillion** ### Life Expectancy to HALE Gap: 10.1 years > **NOTE:** > > Gap between life expectancy and healthy life expectancy. Represents years lived with disability or disease that could be recovered by curing diseases. > > **Inputs**: > > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > - [Global Healthy Life Expectancy (HALE)](#sec-global_hale_current) 📊: 63.3 years (SE: ±1.5 years) > > $$ \Delta_{HALE} = LE_{global} - HALE_{0} = 73.4 - 63.3 = 10.1 $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Life Expectancy to HALE Gap** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Life Expectancy (2024) (years) | 0.7903 | Strong driver | > | Global Healthy Life Expectancy (HALE) (years) | -0.6237 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Life Expectancy to HALE Gap (10,000 simulations) > > **Simulation Results Summary: Life Expectancy to HALE Gap** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 10.1 | > | Mean (expected value) | 10.1 | > | Median (50th percentile) | 10.1 | > | Standard Deviation | 2.42 | > | 90% Range (5th-95th percentile) | \[6.16, 14.2\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Life Expectancy to HALE Gap; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Life Expectancy to HALE Gap > > *This exceedance probability chart shows the likelihood that Life Expectancy to HALE Gap will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Median After-Tax Consumable Income (2025): \$2,138 > **NOTE:** > > Median after-tax consumable income today: mean income x (1 - military share) x median-to-mean ratio x (1 - tax). Because the ratio is derived from the Gallup anchor, this equals Gallup’s measured median with the military slice and taxes removed. The baseline the three scenario trajectories grow from. > > **Inputs**: > > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > - [Military Share of Global GDP](#sec-global_military_share_of_gdp) 🔢: 2.37% > - [Global Median-to-Mean Income Ratio](#sec-global_median_to_mean_income_ratio) 🔢: 0.203:1 > - [Effective Tax Rate on Median Earner](#sec-global_effective_tax_rate_median): 25% > > $$ \begin{gathered} \tilde{m}_{0} \\ = \bar{y}_{0} \times (1 - s_{mil}) \times \rho_{med} \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ where: $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Median After-Tax Consumable Income (2025)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Median-to-Mean Income Ratio (ratio) | 1.0051 | Strong driver | > | Global Average Income (2025 Baseline) (USD) | 0.1050 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Median After-Tax Consumable Income (2025) (10,000 simulations) > > **Simulation Results Summary: Global Median After-Tax Consumable Income (2025)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$2,138 | > | Mean (expected value) | \$2,139 | > | Median (50th percentile) | \$2,135 | > | Standard Deviation | \$248 | > | 90% Range (5th-95th percentile) | \[\$1,710, \$2,566\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Median After-Tax Consumable Income (2025); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Median After-Tax Consumable Income (2025) > > *This exceedance probability chart shows the likelihood that Global Median After-Tax Consumable Income (2025) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Median-to-Mean Income Ratio: 0.203:1 > **NOTE:** > > Global median-to-mean income ratio, DERIVED from the Gallup anchor against mean income (GDP per capita): the median human receives about twenty-one cents of the average dollar. Pre-tax basis on both sides. The derivation replaces a hand-chosen 0.30 ‘from Gallup’s range’, which made the model’s agreement with Gallup circular. This single number is why GDP per capita overstates what a typical person earns by roughly 5x. > > **Inputs**: > > - [Gallup Global Median Per-Capita Income (2013, PPP)](#sec-gallup_global_median_income_per_capita) 📊: \$2,920 (95% CI: \$2,300 - \$3,700) > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > > $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Median-to-Mean Income Ratio** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Gallup Global Median Per-Capita Income (2013, PPP) (USD) | 0.9948 | Strong driver | > | Global Average Income (2025 Baseline) (USD) | -0.1044 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Median-to-Mean Income Ratio (10,000 simulations) > > **Simulation Results Summary: Global Median-to-Mean Income Ratio** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | 0.203:1 | > | Mean (expected value) | 0.203:1 | > | Median (50th percentile) | 0.203:1 | > | Standard Deviation | 0.0236:1 | > | 90% Range (5th-95th percentile) | \[0.163:1, 0.244:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Median-to-Mean Income Ratio; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Median-to-Mean Income Ratio > > *This exceedance probability chart shows the likelihood that Global Median-to-Mean Income Ratio will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Military Share of Global GDP: 2.37% > **NOTE:** > > Military spending as a share of global GDP (SIPRI spending over IMF output). The regrettables deduction in the median consumable-income track: output that is made and counted but cannot be eaten. Military ONLY, by design: it is hard data and the book’s actual subject. Cybercrime was removed from this deduction (v2): loss estimates count transfers and double-counted indirect costs, not uneaten output. > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > > $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Military Share of Global GDP is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 2.37% | > | Mean (expected value) | 2.37% | > | Median (50th percentile) | 2.37% | > | Standard Deviation | 3.47e-16% | > | 90% Range (5th-95th percentile) | \[2.37%, 2.37%\] | > > #### Exceedance Probability > > **Exceedance note:** Military Share of Global GDP collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **2.37%** ### Per Capita Military Spending Globally: \$340 > **NOTE:** > > Per capita military spending globally > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} Spending_{mil,pc} \\ = \frac{Spending_{mil}}{Pop_{global}} \\ = \frac{\$2.72T}{8B} \\ = \$340 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Per Capita Military Spending Globally** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Population in 2024 (of people) | -0.9999 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Per Capita Military Spending Globally (10,000 simulations) > > **Simulation Results Summary: Per Capita Military Spending Globally** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | \$340 | > | Mean (expected value) | \$340 | > | Median (50th percentile) | \$340 | > | Standard Deviation | \$4.13 | > | 90% Range (5th-95th percentile) | \[\$333, \$347\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Per Capita Military Spending Globally; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Per Capita Military Spending Globally > > *This exceedance probability chart shows the likelihood that Per Capita Military Spending Globally will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Military Spending After 1% Treaty Reduction: \$2.69 trillion > **NOTE:** > > Global military spending after 1% treaty reduction > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > > $$ \begin{gathered} Spending_{mil,post} \\ = Spending_{mil} \times (1 - Reduce_{treaty}) \\ = \$2.72T \times (1 - 1\%) \\ = \$2.69T \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Global Military Spending After 1% Treaty Reduction is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.693e+00) > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$2.69 trillion | > | Mean (expected value) | \$2.69 trillion | > | Median (50th percentile) | \$2.69 trillion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$2.69 trillion, \$2.69 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** Global Military Spending After 1% Treaty Reduction collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.693e+00) > > Approximate deterministic value: **\$2.69 trillion** ### Military Spending Real CAGR (20-Year): 2.76% > **NOTE:** > > Real compound annual growth rate of global military spending over the last 20 years (2005-2024), computed from SIPRI World totals in constant 2023 USD. This window deliberately includes the 2011-2014 drawdown, so it cannot be attacked as trough-to-peak cherry picking. The 20-year rate (~2.75%) is actually lower than the 10-year rate (3.4%) precisely because the drawdown pulls the average down. > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Global Military Spending in 2005 (Constant 2023 USD)](#sec-global_military_spending_annual_2005) 📊: \$1.62 trillion > > $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Military Spending Real CAGR (20-Year) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 2.76% | > | Mean (expected value) | 2.76% | > | Median (50th percentile) | 2.76% | > | Standard Deviation | 3.47e-16% | > | 90% Range (5th-95th percentile) | \[2.76%, 2.76%\] | > > #### Exceedance Probability > > **Exceedance note:** Military Spending Real CAGR (20-Year) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **2.76%** ### Global Political Reform Investment: \$128 billion > **NOTE:** > > Estimated global advocacy investment for policy reform. Calculated as US costs × global ratio (based on discretionary spending). Upper bound representing full democratic engagement at scale. > > **Inputs**: > > - [US Political Reform Investment (Total)](#sec-us_political_reform_investment_total) 🔢: \$25.5 billion > - [Global-to-US Political Cost Ratio](#sec-global_to_us_political_cost_ratio): 5:1 (95% CI: 3:1 - 8:1) > > $$ \begin{gathered} Cost_{global,reform} \\ = Cost_{US,total} \times \rho_{global/US} \\ = \$25.5B \times 5 \\ = \$128B \end{gathered} $$ where: $$ \begin{gathered} Cost_{US,total} \\ = (Cost_{campaign} \\ + Cost_{lobby} \times 2) \times \mu_{effort} + Cost_{career} \end{gathered} $$ where: $$ \begin{gathered} Cost_{US,congress} \\ = N_{congress} \times V_{post-office} \\ = 535 \times \$10M \\ = \$5.35B \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Political Reform Investment** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global-to-US Political Cost Ratio (ratio) | 0.7419 | Strong driver | > | US Political Reform Investment (Total) (USD) | 0.6649 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Political Reform Investment (10,000 simulations) > > **Simulation Results Summary: Global Political Reform Investment** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$128 billion | > | Mean (expected value) | \$127 billion | > | Median (50th percentile) | \$120 billion | > | Standard Deviation | \$41.2 billion | > | 90% Range (5th-95th percentile) | \[\$71.7 billion, \$206 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Political Reform Investment; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Political Reform Investment > > *This exceedance probability chart shows the likelihood that Global Political Reform Investment will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Annual Cost of War and Disease: \$412 trillion > **NOTE:** > > Total annual welfare cost of war and disease. Disease burden uses DALY-based welfare valuation; war costs use direct + indirect economic costs. Symptomatic treatment costs NOT added separately (already captured in QALY valuation). > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [Annual Welfare Cost of Avoidable Disease](#sec-global_disease_economic_burden_annual) 🔢: \$400 trillion > > $$ \begin{gathered} Cost_{health+war} \\ = Cost_{war,total} + Burden_{disease} \\ = \$11.4T + \$400T \\ = \$412T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} Burden_{disease} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times Value_{QALY} \\ = 2.88B \times 92.6\% \times \$150K \\ = \$400T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Annual Cost of War and Disease** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Welfare Cost of Avoidable Disease (USD/year) | 1.0002 | Strong driver | > | Total Annual Cost of War Worldwide (USD/year) | 0.0098 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Annual Cost of War and Disease (10,000 simulations) > > **Simulation Results Summary: Total Annual Cost of War and Disease** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$412 trillion | > | Mean (expected value) | \$408 trillion | > | Median (50th percentile) | \$408 trillion | > | Standard Deviation | \$89.5 trillion | > | 90% Range (5th-95th percentile) | \[\$264 trillion, \$556 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Annual Cost of War and Disease; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Annual Cost of War and Disease > > *This exceedance probability chart shows the likelihood that Total Annual Cost of War and Disease will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cumulative 80-Year War Cost (Baseline Trajectory): \$3.22 quadrillion > **NOTE:** > > Cumulative global war cost over 80 years (one human lifespan) on the baseline trajectory, where war costs keep compounding at SIPRI’s 20-year real CAGR (2.76%). This is what the world pays if nothing changes. > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [Military Spending Real CAGR (20-Year)](#sec-global_military_spending_real_cagr_20yr) 🔢: 2.76% > > $$ Cost_{war,cum,baseline} = C \times ((1 + g)^{80} - 1) / g $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cumulative 80-Year War Cost (Baseline Trajectory)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cumulative 80-Year War Cost (Baseline Trajectory) (10,000 simulations) > > **Simulation Results Summary: Cumulative 80-Year War Cost (Baseline Trajectory)** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$3.22 quadrillion | > | Mean (expected value) | \$3.21 quadrillion | > | Median (50th percentile) | \$3.19 quadrillion | > | Standard Deviation | \$249 trillion | > | 90% Range (5th-95th percentile) | \[\$2.82 quadrillion, \$3.65 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cumulative 80-Year War Cost (Baseline Trajectory); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cumulative 80-Year War Cost (Baseline Trajectory) > > *This exceedance probability chart shows the likelihood that Cumulative 80-Year War Cost (Baseline Trajectory) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cumulative 80-Year War Cost (Treaty Trajectory): \$899 trillion > **NOTE:** > > Cumulative global war cost over 80 years under the treaty trajectory, where costs drop 1% immediately and then hold flat (no growth). This is what the world pays after the treaty passes. > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > > $$ \begin{gathered} Cost_{war,cum,treaty} \\ = C \times (1 - Reduce_{treaty}) \times 80 \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cumulative 80-Year War Cost (Treaty Trajectory)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cumulative 80-Year War Cost (Treaty Trajectory) (10,000 simulations) > > **Simulation Results Summary: Cumulative 80-Year War Cost (Treaty Trajectory)** > > | Statistic | Value | > |:--------------------------------|---------------------------------------:| > | Baseline (deterministic) | \$899 trillion | > | Mean (expected value) | \$896 trillion | > | Median (50th percentile) | \$892 trillion | > | Standard Deviation | \$69.4 trillion | > | 90% Range (5th-95th percentile) | \[\$789 trillion, \$1.02 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cumulative 80-Year War Cost (Treaty Trajectory); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cumulative 80-Year War Cost (Treaty Trajectory) > > *This exceedance probability chart shows the likelihood that Cumulative 80-Year War Cost (Treaty Trajectory) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years: \$244 million > **NOTE:** > > Per-person future value at year 80 of the full baseline war cost stream, compounded at the illustrative long-term real return rate (13%, Nasdaq-100 historical). This is the hypothetical opportunity cost per person of running the baseline SIPRI trajectory rather than abolishing war entirely and redirecting every dollar. Serves as the ceiling on the peace dividend ladder: the treaty gets you a large fraction of this number, full abolition gets you all of it. > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [Military Spending Real CAGR (20-Year)](#sec-global_military_spending_real_cagr_20yr) 🔢: 2.76% > - [Assumed Long-Term Real Return for Investment Compounding](#sec-peace_dividend_investment_real_return_rate): 13% > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} FV_{pp,abolition} \\ = [Σ_{t=0..79} C(1+g)^t \times (1+r)^{79-t}] / Pop_{global} \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 0.9895 | Strong driver | > | Global Population in 2024 (of people) | -0.1552 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years (10,000 simulations) > > **Simulation Results Summary: Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$244 million | > | Mean (expected value) | \$243 million | > | Median (50th percentile) | \$242 million | > | Standard Deviation | \$19.1 million | > | 90% Range (5th-95th percentile) | \[\$214 million, \$277 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years > > *This exceedance probability chart shows the likelihood that Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Per-Person 80-Year War Cost (Baseline Trajectory): \$402,488 > **NOTE:** > > Per-person 80-year lifetime tab for global war costs on the SIPRI baseline trajectory (2.76% real growth). About 3.5x the flat-assumption figure the chapter opens with, because war costs have been compounding while nobody updated the invoice. > > **Inputs**: > > - [Cumulative 80-Year War Cost (Baseline Trajectory)](#sec-global_war_cost_lifetime_cumulative_baseline) 🔢: \$3.22 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} Cost_{war,pp,baseline} \\ = \frac{Cost_{war,cum,baseline}}{Pop_{global}} \\ = \frac{\$3220T}{8B} \\ = \$402K \end{gathered} $$ where: $$ Cost_{war,cum,baseline} = C \times ((1 + g)^{80} - 1) / g $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Per-Person 80-Year War Cost (Baseline Trajectory)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Cumulative 80-Year War Cost (Baseline Trajectory) (USD) | 0.9895 | Strong driver | > | Global Population in 2024 (of people) | -0.1552 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Per-Person 80-Year War Cost (Baseline Trajectory) (10,000 simulations) > > **Simulation Results Summary: Per-Person 80-Year War Cost (Baseline Trajectory)** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | \$402,488 | > | Mean (expected value) | \$401,168 | > | Median (50th percentile) | \$399,095 | > | Standard Deviation | \$31,400 | > | 90% Range (5th-95th percentile) | \[\$352,459, \$456,139\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Per-Person 80-Year War Cost (Baseline Trajectory); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Per-Person 80-Year War Cost (Baseline Trajectory) > > *This exceedance probability chart shows the likelihood that Per-Person 80-Year War Cost (Baseline Trajectory) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Per-Person 80-Year War Cost (Flat Assumption): \$113,571 > **NOTE:** > > Per-person 80-year lifetime tab for global war costs, assuming costs stay flat at today’s level. This is the conservative floor the chapter opens with. The actual figure is higher because war costs have been compounding in real terms. > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} Cost_{war,pp,flat} \\ = Cost_{war,total} \times \frac{80}{Pop_{global}} \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Per-Person 80-Year War Cost (Flat Assumption)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 0.9895 | Strong driver | > | Global Population in 2024 (of people) | -0.1552 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Per-Person 80-Year War Cost (Flat Assumption) (10,000 simulations) > > **Simulation Results Summary: Per-Person 80-Year War Cost (Flat Assumption)** > > | Statistic | Value | > |:--------------------------------|------------------------:| > | Baseline (deterministic) | \$113,571 | > | Mean (expected value) | \$113,199 | > | Median (50th percentile) | \$112,614 | > | Standard Deviation | \$8,860 | > | 90% Range (5th-95th percentile) | \[\$99,454, \$128,710\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Per-Person 80-Year War Cost (Flat Assumption); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Per-Person 80-Year War Cost (Flat Assumption) > > *This exceedance probability chart shows the likelihood that Per-Person 80-Year War Cost (Flat Assumption) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Years Until Baseline War Cost Exceeds Current World GDP: 85.1 years > **NOTE:** > > Years until annual war cost exceeds current world GDP at the SIPRI 20-year real growth rate. At ~2.76% real growth and \$11.4T current war cost, this happens in under a century. The baseline trajectory is therefore a countdown, not a plan. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [Military Spending Real CAGR (20-Year)](#sec-global_military_spending_real_cagr_20yr) 🔢: 2.76% > > $$ \begin{gathered} T_{GDP} \\ = log\left(\frac{GDP_{global}}{Cost_{war,total}}\right) / log(1 \\ + g_{mil,20yr}) \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Years Until Baseline War Cost Exceeds Current World GDP** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | -0.9987 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Years Until Baseline War Cost Exceeds Current World GDP (10,000 simulations) > > **Simulation Results Summary: Years Until Baseline War Cost Exceeds Current World GDP** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 85.1 | > | Mean (expected value) | 85.3 | > | Median (50th percentile) | 85.4 | > | Standard Deviation | 2.83 | > | 90% Range (5th-95th percentile) | \[80.5, 89.9\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Years Until Baseline War Cost Exceeds Current World GDP; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Years Until Baseline War Cost Exceeds Current World GDP > > *This exceedance probability chart shows the likelihood that Years Until Baseline War Cost Exceeds Current World GDP will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual War Cost in Year 80 (Baseline Trajectory): \$100 trillion > **NOTE:** > > Projected annual war cost in year 80 under the baseline trajectory, assuming the SIPRI 20-year real CAGR continues. At ~2.76% real growth, this approaches current world GDP (which is why the baseline trajectory breaks math around year 85). > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [Military Spending Real CAGR (20-Year)](#sec-global_military_spending_real_cagr_20yr) 🔢: 2.76% > > $$ \begin{gathered} Cost_{war,yr80} \\ = Cost_{war,total} \times (1 + g_{mil,20yr})^{80} \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual War Cost in Year 80 (Baseline Trajectory)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual War Cost in Year 80 (Baseline Trajectory) (10,000 simulations) > > **Simulation Results Summary: Annual War Cost in Year 80 (Baseline Trajectory)** > > | Statistic | Value | > |:--------------------------------|------------------------------------:| > | Baseline (deterministic) | \$100 trillion | > | Mean (expected value) | \$99.9 trillion | > | Median (50th percentile) | \$99.4 trillion | > | Standard Deviation | \$7.74 trillion | > | 90% Range (5th-95th percentile) | \[\$87.8 trillion, \$114 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual War Cost in Year 80 (Baseline Trajectory); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual War Cost in Year 80 (Baseline Trajectory) > > *This exceedance probability chart shows the likelihood that Annual War Cost in Year 80 (Baseline Trajectory) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Government Replacement Suite Annual Operating Cost: \$300 million > **NOTE:** > > Combined steady-state annual operating cost of the priced government-replacement modules (revenue service, security administration, election commission, census sensor array, monetary authority, securities commission). Excludes the revenue-positive and included-elsewhere modules. > > **Inputs**: > > - [Automated Revenue Service Annual Operating Cost](#sec-automated_revenue_service_annual_opex) 🔢: \$150 million > - [Universal Security Administration Annual Operating Cost](#sec-universal_security_admin_annual_opex) 🔢: \$100 million > - [Aligned Election Commission Annual Operating Cost](#sec-aligned_election_commission_annual_opex): \$7 million (95% CI: \$2 million - \$20 million) > - [Decentralized Census Bureau Annual Operating Cost](#sec-decentralized_census_bureau_annual_opex): \$15 million (95% CI: \$5 million - \$40 million) > - [Algorithmic Monetary Authority Annual Operating Cost](#sec-algorithmic_monetary_authority_annual_opex): \$7.5 million (95% CI: \$3 million - \$20 million) > - [Transparent Securities Commission Annual Operating Cost](#sec-transparent_securities_commission_annual_opex): \$20 million (95% CI: \$8 million - \$50 million) > > $$ \begin{gathered} Cost_{opex,ann} \\ = Cost_{opex,ann} + Cost_{opex,ann} + Cost_{opex,ann} \\ + Cost_{opex,ann} + Cost_{net,ann} + Cost_{opex,ann} \\ = \$150M + \$100M + \$7M + \$15M + \$7.5M + \$20M \\ = \$300M \end{gathered} $$ where: $$ \begin{gathered} Cost_{opex,ann} \\ = Automated_{annual} \times Cost_{all} \\ = 500B \times \$0.0003 \\ = \$150M \end{gathered} $$ where: $$ \begin{gathered} Cost_{opex,ann} \\ = Pop_{US} \times Cost_{ann} \\ = 335M \times \$0.3 \\ = \$100M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Government Replacement Suite Annual Operating Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Automated Revenue Service Annual Operating Cost (USD) | 0.8233 | Strong driver | > | Universal Security Administration Annual Operating Cost (USD) | 0.5593 | Strong driver | > | Transparent Securities Commission Annual Operating Cost (USD) | 0.0806 | Minimal effect | > | Decentralized Census Bureau Annual Operating Cost (USD) | 0.0659 | Minimal effect | > | Aligned Election Commission Annual Operating Cost (USD) | 0.0339 | Minimal effect | > | Algorithmic Monetary Authority Annual Operating Cost (USD) | 0.0325 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Government Replacement Suite Annual Operating Cost (10,000 simulations) > > **Simulation Results Summary: Government Replacement Suite Annual Operating Cost** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$300 million | > | Mean (expected value) | \$300 million | > | Median (50th percentile) | \$273 million | > | Standard Deviation | \$121 million | > | 90% Range (5th-95th percentile) | \[\$152 million, \$543 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Government Replacement Suite Annual Operating Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Government Replacement Suite Annual Operating Cost > > *This exceedance probability chart shows the likelihood that Government Replacement Suite Annual Operating Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Replacement Suite Operating Cost per Citizen per Year: \$0.896 > **NOTE:** > > The whole priced replacement suite’s operating cost per citizen per year: roughly one dollar, versus five figures of dysfunction. > > **Inputs**: > > - [Government Replacement Suite Annual Operating Cost](#sec-gov_replacement_suite_annual_opex) 🔢: \$300 million > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} Cost_{opex,ann} \\ = \frac{Cost_{opex,ann}}{Pop_{US}} \\ = \frac{\$300M}{335M} \\ = \$0.896 \end{gathered} $$ where: $$ \begin{gathered} Cost_{opex,ann} \\ = Cost_{opex,ann} + Cost_{opex,ann} + Cost_{opex,ann} \\ + Cost_{opex,ann} + Cost_{net,ann} + Cost_{opex,ann} \\ = \$150M + \$100M + \$7M + \$15M + \$7.5M + \$20M \\ = \$300M \end{gathered} $$ where: $$ \begin{gathered} Cost_{opex,ann} \\ = Automated_{annual} \times Cost_{all} \\ = 500B \times \$0.0003 \\ = \$150M \end{gathered} $$ where: $$ \begin{gathered} Cost_{opex,ann} \\ = Pop_{US} \times Cost_{ann} \\ = 335M \times \$0.3 \\ = \$100M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Replacement Suite Operating Cost per Citizen per Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Government Replacement Suite Annual Operating Cost (USD) | 1.0000 | Strong driver | > | US Population in 2024 (people) | -0.0178 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Replacement Suite Operating Cost per Citizen per Year (10,000 simulations) > > **Simulation Results Summary: Replacement Suite Operating Cost per Citizen per Year** > > | Statistic | Value | > |:--------------------------------|--------------------:| > | Baseline (deterministic) | \$0.896 | > | Mean (expected value) | \$0.894 | > | Median (50th percentile) | \$0.815 | > | Standard Deviation | \$0.362 | > | 90% Range (5th-95th percentile) | \[\$0.452, \$1.62\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Replacement Suite Operating Cost per Citizen per Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Replacement Suite Operating Cost per Citizen per Year > > *This exceedance probability chart shows the likelihood that Replacement Suite Operating Cost per Citizen per Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Healthcare vs Military Multiplier Ratio: 7.17x > **NOTE:** > > Ratio of healthcare to military fiscal multipliers. Healthcare investment generates 7× more economic activity per dollar than military spending. > > **Inputs**: > > - [Economic Multiplier for Healthcare Investment](#sec-economic_multiplier_healthcare_investment) 📊: 4.3x (95% CI: 3x - 6x) > - [Economic Multiplier for Military Spending](#sec-economic_multiplier_military_spending) 📊: 0.6x (95% CI: 0.4x - 0.9x) > > $$ \begin{gathered} r_{health/mil} \\ = \frac{k_{health}}{k_{mil}} \\ = \frac{4.3}{0.6} \\ = 7.17 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Healthcare vs Military Multiplier Ratio** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Economic Multiplier for Military Spending (x) | -0.7391 | Strong driver | > | Economic Multiplier for Healthcare Investment (x) | 0.6443 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Healthcare vs Military Multiplier Ratio (10,000 simulations) > > **Simulation Results Summary: Healthcare vs Military Multiplier Ratio** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 7.17x | > | Mean (expected value) | 7.48x | > | Median (50th percentile) | 7.24x | > | Standard Deviation | 1.97x | > | 90% Range (5th-95th percentile) | \[4.67x, 11.1x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Healthcare vs Military Multiplier Ratio; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Healthcare vs Military Multiplier Ratio > > *This exceedance probability chart shows the likelihood that Healthcare vs Military Multiplier Ratio will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Human Laughs per Healthy Life-Year: 6,205 laughs > **NOTE:** > > Laughs occurring across one healthy life-year, computed as the adult daily laughter rate multiplied by days in a year. The conversion factor between DALYs averted (healthy life-years restored) and total laughs preserved. > > **Inputs**: > > - [Human Laughs per Day (Average Adult)](#sec-human_laughs_per_day_average): 17 laughs (95% CI: 5 laughs - 50 laughs) > > $$ L_{year} = L_{day} \times 365 = 17 \times 365 = 6{,}200 $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Human Laughs per Healthy Life-Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Human Laughs per Day (Average Adult) (laughs) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Human Laughs per Healthy Life-Year (10,000 simulations) > > **Simulation Results Summary: Human Laughs per Healthy Life-Year** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 6,205 | > | Mean (expected value) | 6,135 | > | Median (50th percentile) | 5,123 | > | Standard Deviation | 3,746 | > | 90% Range (5th-95th percentile) | \[1,848, 14,225\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Human Laughs per Healthy Life-Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Human Laughs per Healthy Life-Year > > *This exceedance probability chart shows the likelihood that Human Laughs per Healthy Life-Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### IAB Mechanism Benefit-Cost Ratio: 205:1 > **NOTE:** > > Benefit-Cost Ratio of the IAB mechanism itself > > **Inputs**: > > - [1% treaty Basic Annual Benefits (Peace + R&D Savings)](#sec-treaty_peace_plus_rd_annual_benefits) 🔢: \$154 billion > - [IAB Mechanism Annual Cost (High Estimate)](#sec-iab_mechanism_annual_cost): \$750 million (95% CI: \$160 million - \$750 million) > > $$ \begin{gathered} BCR_{IAB} \\ = \frac{Benefit_{peace+RD}}{Cost_{IAB,ann}} \\ = \frac{\$154B}{\$750M} \\ = 205 \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$40.5B \\ = \$154B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ **Methodology**: [https://iab.warondisease.org##welfare-analysis](https://iab.warondisease.org##welfare-analysis) > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for IAB Mechanism Benefit-Cost Ratio** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | 1% treaty Basic Annual Benefits (Peace + R&D Savings) (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: IAB Mechanism Benefit-Cost Ratio (10,000 simulations) > > **Simulation Results Summary: IAB Mechanism Benefit-Cost Ratio** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 205:1 | > | Mean (expected value) | 205:1 | > | Median (50th percentile) | 204:1 | > | Standard Deviation | 14.3:1 | > | 90% Range (5th-95th percentile) | \[182:1, 229:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for IAB Mechanism Benefit-Cost Ratio; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: IAB Mechanism Benefit-Cost Ratio > > *This exceedance probability chart shows the likelihood that IAB Mechanism Benefit-Cost Ratio will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual IAB Political Incentive Funding: \$2.72 billion > **NOTE:** > > Annual funding for IAB political incentive mechanism (independent expenditures supporting high-scoring politicians, post-office fellowship endowments, Public Good Score infrastructure) > > **Inputs**: > > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > - [IAB Political Incentive Funding Percentage](#sec-iab_political_incentive_funding_pct): 10% > > $$ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Annual IAB Political Incentive Funding is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.720e-03) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$2.72 billion | > | Mean (expected value) | \$2.72 billion | > | Median (50th percentile) | \$2.72 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$2.72 billion, \$2.72 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Annual IAB Political Incentive Funding collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.720e-03) > > Approximate deterministic value: **\$2.72 billion** ### IAB vs Defense Lobbying Ratio at 1% Treaty: 13.7x > **NOTE:** > > Ratio of IAB political incentive funding to defense industry lobbying at 1% treaty level. At just 1%, the health lobby already outguns the defense lobby by this factor. > > **Inputs**: > > - [Annual IAB Political Incentive Funding](#sec-iab_political_incentive_funding_annual) 🔢: \$2.72 billion > - [Annual Military Sector Lobbying](#sec-defense_lobbying_annual) 📊: \$198 million (95% CI: \$190 million - \$210 million) > > $$ \begin{gathered} k_{IAB:defense} \\ = \frac{Funding_{political,ann}}{Lobby_{def,ann}} \\ = \frac{\$2.72B}{\$198M} \\ = 13.7 \end{gathered} $$ where: $$ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** IAB vs Defense Lobbying Ratio at 1% Treaty is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.374e-11) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 13.7x | > | Mean (expected value) | 13.7x | > | Median (50th percentile) | 13.7x | > | Standard Deviation | 3.55e-15x | > | 90% Range (5th-95th percentile) | \[13.7x, 13.7x\] | > > #### Exceedance Probability > > **Exceedance note:** IAB vs Defense Lobbying Ratio at 1% Treaty collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.374e-11) > > Approximate deterministic value: **13.7x** ### Immigration Dividend per Citizen per Year: \$310 > **NOTE:** > > Annual per-citizen dividend from priced entry: entry-fee revenue redirected from smugglers to the Treasury, plus the annualized fiscal surplus the CBO measured from a surge of comparable volume. A floor: excludes the surtax stream and the retired enforcement budget. > > **Inputs**: > > - [Priced-Entry Annual Fee Revenue](#sec-immigration_entry_revenue_annual) 🔢: \$13.9 billion > - [CBO Immigration Surge Deficit Reduction, 2024-2034](#sec-cbo_immigration_surge_deficit_reduction_2024_2034) 📊: \$900 billion > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} Dividend_{ann} \\ = (\text{ENTRY\_FEES} \\ + \text{SURGE\_FISCAL\_SURPLUS} / 10 years) / \text{US\_POPULATION} \end{gathered} $$ where: $$ \begin{gathered} Ratio_{ann} \\ = Ratio_{ann} \times US \\ = 2M \times \$6.94K \\ = \$13.9B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Immigration Dividend per Citizen per Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Priced-Entry Annual Fee Revenue (USD) | 0.9967 | Strong driver | > | US Population in 2024 (people) | -0.0908 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Immigration Dividend per Citizen per Year (10,000 simulations) > > **Simulation Results Summary: Immigration Dividend per Citizen per Year** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | \$310 | > | Mean (expected value) | \$310 | > | Median (50th percentile) | \$304 | > | Standard Deviation | \$25 | > | 90% Range (5th-95th percentile) | \[\$284, \$359\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Immigration Dividend per Citizen per Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Immigration Dividend per Citizen per Year > > *This exceedance probability chart shows the likelihood that Immigration Dividend per Citizen per Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Priced-Entry Annual Fee Revenue: \$13.9 billion > **NOTE:** > > Annual entry-fee revenue under priced entry at the smuggler-revealed price: money migrants already pay, redirected from cartels to the Treasury. Excludes the ongoing surtax stream, so it is a floor. > > **Inputs**: > > - [Priced-Entry Annual Volume](#sec-immigration_priced_entry_annual_volume): 2 million entrants (95% CI: 1 million entrants - 5 million entrants) > - [Average Smuggler Fee per US Border Crossing](#sec-us_smuggler_fee_avg) 📊: \$6,937 (95% CI: \$4,000 - \$14,000) > > $$ \begin{gathered} Ratio_{ann} \\ = Ratio_{ann} \times US \\ = 2M \times \$6.94K \\ = \$13.9B \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Priced-Entry Annual Fee Revenue** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Priced-Entry Annual Volume (entrants) | 0.7787 | Strong driver | > | Average Smuggler Fee per US Border Crossing (USD) | 0.5754 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Priced-Entry Annual Fee Revenue (10,000 simulations) > > **Simulation Results Summary: Priced-Entry Annual Fee Revenue** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$13.9 billion | > | Mean (expected value) | \$13.9 billion | > | Median (50th percentile) | \$11.7 billion | > | Standard Deviation | \$8.21 billion | > | 90% Range (5th-95th percentile) | \[\$5.16 billion, \$30.2 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Priced-Entry Annual Fee Revenue; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Priced-Entry Annual Fee Revenue > > *This exceedance probability chart shows the likelihood that Priced-Entry Annual Fee Revenue will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Ratio of Industry to Government Clinical Trials Spending: 12.3:1 > **NOTE:** > > Ratio of Industry to Government spending on clinical trials (approx 90/10 split) > > **Inputs**: > > - [Annual Global Spending on Clinical Trials](#sec-global_clinical_trials_spending_annual) 📊: \$60 billion (95% CI: \$50 billion - \$75 billion) > - [Annual Global Government Spending on Clinical Trials](#sec-global_government_clinical_trials_spending_annual) 📊: \$4.5 billion (95% CI: \$3 billion - \$6 billion) > > $$ \begin{gathered} Ratio_{ind:gov} \\ = \frac{Spending_{trials}}{Spending_{trials,gov}} - 1 \\ = \frac{\$60B}{\$4.5B} - 1 \\ = 12.3 \end{gathered} $$ > > **Methodology**:^([9](../references.llms.md#ref-industry-vs-government-trial-spending-split)) > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Ratio of Industry to Government Clinical Trials Spending** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Government Spending on Clinical Trials (USD) | -0.8087 | Strong driver | > | Annual Global Spending on Clinical Trials (USD) | 0.5560 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Ratio of Industry to Government Clinical Trials Spending (10,000 simulations) > > **Simulation Results Summary: Ratio of Industry to Government Clinical Trials Spending** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 12.3:1 | > | Mean (expected value) | 13:1 | > | Median (50th percentile) | 12.6:1 | > | Standard Deviation | 3.36:1 | > | 90% Range (5th-95th percentile) | \[8.23:1, 19.3:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Ratio of Industry to Government Clinical Trials Spending; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Ratio of Industry to Government Clinical Trials Spending > > *This exceedance probability chart shows the likelihood that Ratio of Industry to Government Clinical Trials Spending will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort: \$23.4 million > **NOTE:** > > Representative full-life cohort exposure: lifetime lost earnings under the 8-channel compound peace economy counterfactual, undiscounted, over WHO global life expectancy at birth. NOT a uniform per-individual award. A 5-year-old, 45-year-old, and 85-year-old plaintiff would each have a different remaining-life horizon; this number is the representative full-cohort exposure used as the single-theory headline. Implicitly captures war deaths, property destruction, and medical opportunity cost via their drag on compound growth, so cannot be added to the body-count ledger. > > **Inputs**: > > - [Annual Lost GDP per Capita from War](#sec-war_counterfactual_lost_gdp_per_capita) 🔢: \$319,261 > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > > $$ \begin{gathered} D_{prosperity,life,pc} \\ = GDP_{pc,lost} \times LE_{global} \\ = \$319K \times 73.4 \\ = \$23.4M \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,lost} \\ = GDP_{pc,peace} - \bar{y}_{0} \\ = \$334K - \$14.4K \\ = \$319K \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Lost GDP per Capita from War (USD/person/year) | 0.9994 | Strong driver | > | Global Life Expectancy (2024) (years) | 0.0400 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort (10,000 simulations) > > **Simulation Results Summary: Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$23.4 million | > | Mean (expected value) | \$28.8 million | > | Median (50th percentile) | \$23.1 million | > | Standard Deviation | \$18.8 million | > | 90% Range (5th-95th percentile) | \[\$7.81 million, \$66.5 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort > > *This exceedance probability chart shows the likelihood that Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon): \$187 quadrillion > **NOTE:** > > Global lifetime lost prosperity damages, undiscounted, WHO global life expectancy horizon. Single coherent damages theory under corporate-liability lost-profits doctrine: the integral of the productive economy that didn’t happen because of the destructive economy. Cannot be added to the body-count ledger; replaces it as a single-theory pleading. > > **Inputs**: > > - [Annual Lost GDP Global from War](#sec-war_counterfactual_lost_gdp_global) 🔢: \$2.55 quadrillion > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > > $$ \begin{gathered} D_{prosperity,life} \\ = GDP_{lost,total} \times LE_{global} \\ = \$2550T \times 73.4 \\ = \$187000T \end{gathered} $$ where: $$ \begin{gathered} GDP_{lost,total} \\ = GDP_{pc,lost} \times Pop_{global} \\ = \$319K \times 8B \\ = \$2550T \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,lost} \\ = GDP_{pc,peace} - \bar{y}_{0} \\ = \$334K - \$14.4K \\ = \$319K \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Lost GDP Global from War (USD/year) | 0.9994 | Strong driver | > | Global Life Expectancy (2024) (years) | 0.0400 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon) (10,000 simulations) > > **Simulation Results Summary: Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon)** > > | Statistic | Value | > |:--------------------------------|------------------------------------------:| > | Baseline (deterministic) | \$187 quadrillion | > | Mean (expected value) | \$230 quadrillion | > | Median (50th percentile) | \$184 quadrillion | > | Standard Deviation | \$151 quadrillion | > | 90% Range (5th-95th percentile) | \[\$62.5 quadrillion, \$532 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon) > > *This exceedance probability chart shows the likelihood that Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline): \$10.6 million > **NOTE:** > > Net present value of perpetual annual lost-income flow per representative living human, at the standard 3% social discount rate, under a no-cure/no-convergence baseline. Sensitivity exposure for the corporate-liability lost-profits theory. Treats the lost-prosperity flow as continuing indefinitely; a finite-horizon convergence assumption would shrink this slightly (~5% reduction at 100 years). Single-theory pleading; cannot be added to the body-count ledger. > > **Inputs**: > > - [Annual Lost GDP per Capita from War](#sec-war_counterfactual_lost_gdp_per_capita) 🔢: \$319,261 > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > > $$ \begin{gathered} D_{prosperity,NPV,pc} \\ = \frac{GDP_{pc,lost}}{r_{discount}} \\ = \frac{\$319K}{3\%} \\ = \$10.6M \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,lost} \\ = GDP_{pc,peace} - \bar{y}_{0} \\ = \$334K - \$14.4K \\ = \$319K \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Lost GDP per Capita from War (USD/person/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline) (10,000 simulations) > > **Simulation Results Summary: Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$10.6 million | > | Mean (expected value) | \$13.1 million | > | Median (50th percentile) | \$10.5 million | > | Standard Deviation | \$8.55 million | > | 90% Range (5th-95th percentile) | \[\$3.54 million, \$30.2 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline) > > *This exceedance probability chart shows the likelihood that Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline): \$85.1 quadrillion > **NOTE:** > > Net present value of perpetual lost-income flow globally, at the standard 3% social discount rate, under a no-cure/no-convergence baseline. Sensitivity exposure under corporate-liability lost-profits doctrine. Cannot be added to the body-count ledger. > > **Inputs**: > > - [Annual Lost GDP Global from War](#sec-war_counterfactual_lost_gdp_global) 🔢: \$2.55 quadrillion > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > > $$ \begin{gathered} D_{prosperity,NPV} \\ = \frac{GDP_{lost,total}}{r_{discount}} \\ = \frac{\$2550T}{3\%} \\ = \$85100T \end{gathered} $$ where: $$ \begin{gathered} GDP_{lost,total} \\ = GDP_{pc,lost} \times Pop_{global} \\ = \$319K \times 8B \\ = \$2550T \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,lost} \\ = GDP_{pc,peace} - \bar{y}_{0} \\ = \$334K - \$14.4K \\ = \$319K \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Lost GDP Global from War (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline) (10,000 simulations) > > **Simulation Results Summary: Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline)** > > | Statistic | Value | > |:--------------------------------|------------------------------------------:| > | Baseline (deterministic) | \$85.1 quadrillion | > | Mean (expected value) | \$105 quadrillion | > | Median (50th percentile) | \$83.9 quadrillion | > | Standard Deviation | \$68.4 quadrillion | > | 90% Range (5th-95th percentile) | \[\$28.4 quadrillion, \$241 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline) > > *This exceedance probability chart shows the likelihood that Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Court of Humanity Expected Social Value: \$11.4 billion > **NOTE:** > > Expected annual social value from the Court of Humanity: P(success) \* annual peace dividend. Expenditure (\$30M). > > **Inputs**: > > - [P(Success \| Court of Humanity Funded)](#sec-mechanism_court_of_humanity_p_success): 0.1:1 (95% CI: 0.03:1 - 0.25:1) > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > > $$ \begin{gathered} EV_{court} \\ = P_{court} \times Benefit_{peace,soc} \\ = 0.1 \times \$114B \\ = \$11.4B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Court of Humanity Expected Social Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Court of Humanity Expected Social Value (10,000 simulations) > > **Simulation Results Summary: Court of Humanity Expected Social Value** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$11.4 billion | > | Mean (expected value) | \$11.3 billion | > | Median (50th percentile) | \$11.3 billion | > | Standard Deviation | \$877 million | > | 90% Range (5th-95th percentile) | \[\$9.96 billion, \$12.9 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Court of Humanity Expected Social Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Court of Humanity Expected Social Value > > *This exceedance probability chart shows the likelihood that Court of Humanity Expected Social Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### dFDA Expected Social Value: \$79.5 billion > **NOTE:** > > Expected annual social value from the dFDA: P(success) \* annual peace dividend. Platform build is expenditure (\$500M). > > **Inputs**: > > - [P(Success \| dFDA Funded)](#sec-mechanism_dfda_p_success): 0.7:1 (95% CI: 0.4:1 - 0.9:1) > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > > $$ \begin{gathered} EV_{dFDA} \\ = P_{dFDA} \times Benefit_{peace,soc} \\ = 0.7 \times \$114B \\ = \$79.5B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for dFDA Expected Social Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: dFDA Expected Social Value (10,000 simulations) > > **Simulation Results Summary: dFDA Expected Social Value** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$79.5 billion | > | Mean (expected value) | \$79.2 billion | > | Median (50th percentile) | \$78.8 billion | > | Standard Deviation | \$6.14 billion | > | 90% Range (5th-95th percentile) | \[\$69.7 billion, \$90.1 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for dFDA Expected Social Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: dFDA Expected Social Value > > *This exceedance probability chart shows the likelihood that dFDA Expected Social Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### IAB Expected Social Value: \$68.1 billion > **NOTE:** > > Expected annual social value from IABs: P(success) \* annual peace dividend. Investor capital is returned with interest, not spent. > > **Inputs**: > > - [P(Success \| IABs Funded)](#sec-mechanism_iab_p_success): 0.6:1 (95% CI: 0.3:1 - 0.85:1) > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > > $$ \begin{gathered} EV_{IAB} \\ = P_{IAB} \times Benefit_{peace,soc} \\ = 0.6 \times \$114B \\ = \$68.1B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for IAB Expected Social Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: IAB Expected Social Value (10,000 simulations) > > **Simulation Results Summary: IAB Expected Social Value** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$68.1 billion | > | Mean (expected value) | \$67.9 billion | > | Median (50th percentile) | \$67.6 billion | > | Standard Deviation | \$5.26 billion | > | 90% Range (5th-95th percentile) | \[\$59.7 billion, \$77.2 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for IAB Expected Social Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: IAB Expected Social Value > > *This exceedance probability chart shows the likelihood that IAB Expected Social Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### IAB Net Cost (Annual): \$50 million > **NOTE:** > > Net annual cost of IABs to EOS: administration only (investor capital is returned, not spent) > > **Inputs**: > > - [Total 1% Treaty Campaign Cost](#sec-treaty_campaign_total_cost) 🔢: \$1 billion > - [IAB Admin Cost Rate](#sec-mechanism_iab_admin_rate): 0.05:1 > > $$ \begin{gathered} C_{IAB,net} \\ = Cost_{campaign} \times r_{IAB,admin} \\ = \$1B \times 0.05 \\ = \$50M \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for IAB Net Cost (Annual)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:------------------------------------|------------------------:|:---------------| > | Total 1% Treaty Campaign Cost (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: IAB Net Cost (Annual) (10,000 simulations) > > **Simulation Results Summary: IAB Net Cost (Annual)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$50 million | > | Mean (expected value) | \$49.9 million | > | Median (50th percentile) | \$47.8 million | > | Standard Deviation | \$12.6 million | > | 90% Range (5th-95th percentile) | \[\$33.6 million, \$73.8 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for IAB Net Cost (Annual); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: IAB Net Cost (Annual) > > *This exceedance probability chart shows the likelihood that IAB Net Cost (Annual) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Loving Takeover Expected Social Value: \$108 billion > **NOTE:** > > Expected annual social value from the Loving Takeover: P(success) \* annual peace dividend. Capital is deployed into shares (retained and appreciating), not spent. > > **Inputs**: > > - [P(Success \| Loving Takeover Funded)](#sec-mechanism_loving_takeover_p_success): 0.95:1 (95% CI: 0.8:1 - 0.99:1) > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > > $$ \begin{gathered} EV_{takeover} \\ = P_{takeover} \times Benefit_{peace,soc} \\ = 0.95 \times \$114B \\ = \$108B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Loving Takeover Expected Social Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Loving Takeover Expected Social Value (10,000 simulations) > > **Simulation Results Summary: Loving Takeover Expected Social Value** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$108 billion | > | Mean (expected value) | \$108 billion | > | Median (50th percentile) | \$107 billion | > | Standard Deviation | \$8.33 billion | > | 90% Range (5th-95th percentile) | \[\$94.6 billion, \$122 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Loving Takeover Expected Social Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Loving Takeover Expected Social Value > > *This exceedance probability chart shows the likelihood that Loving Takeover Expected Social Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Loving Takeover Net Cost (Annual): \$17.5 billion > **NOTE:** > > Net annual cost of the Loving Takeover: opportunity cost only (capital deployed into shares is retained, not spent). At 2% annual opportunity cost rate. > > **Inputs**: > > - [Military Takeover Cost (Outright-Control Ceiling)](#sec-defense_takeover_cost_total) 🔢: \$873 billion > - [Mechanism Opportunity Cost Rate](#sec-mechanism_opportunity_cost_rate): 0.02:1 (95% CI: 0:1 - 0.05:1) > > $$ \begin{gathered} C_{takeover,net} \\ = C_{takeover} \times r_{opp} \\ = \$873B \times 0.02 \\ = \$17.5B \end{gathered} $$ where: $$ \begin{gathered} C_{takeover} \\ = (MarketCap_{US} \\ + MarketCap_{allied}) \times f_{control} \times m_{premium} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** Loving Takeover Net Cost (Annual) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.746e-02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$17.5 billion | > | Mean (expected value) | \$17.5 billion | > | Median (50th percentile) | \$17.5 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$17.5 billion, \$17.5 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Loving Takeover Net Cost (Annual) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.746e-02) > > Approximate deterministic value: **\$17.5 billion** ### Referendum Expected Social Value: \$34.1 billion > **NOTE:** > > Expected annual social value from the global referendum: P(success) \* annual peace dividend. Expenditure (\$250M). > > **Inputs**: > > - [P(Success \| Referendum Funded)](#sec-mechanism_referendum_p_success): 0.3:1 (95% CI: 0.1:1 - 0.55:1) > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > > $$ \begin{gathered} EV_{referendum} \\ = P_{referendum} \times Benefit_{peace,soc} \\ = 0.3 \times \$114B \\ = \$34.1B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Referendum Expected Social Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Referendum Expected Social Value (10,000 simulations) > > **Simulation Results Summary: Referendum Expected Social Value** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$34.1 billion | > | Mean (expected value) | \$34 billion | > | Median (50th percentile) | \$33.8 billion | > | Standard Deviation | \$2.63 billion | > | 90% Range (5th-95th percentile) | \[\$29.9 billion, \$38.6 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Referendum Expected Social Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Referendum Expected Social Value > > *This exceedance probability chart shows the likelihood that Referendum Expected Social Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Campaign Expected Social Value: \$45.4 billion > **NOTE:** > > Expected annual social value from the treaty campaign: P(success) \* annual peace dividend. Lobbying + referendum is expenditure (\$1B). > > **Inputs**: > > - [P(Success \| Treaty Campaign Funded)](#sec-mechanism_treaty_campaign_p_success): 0.4:1 (95% CI: 0.15:1 - 0.7:1) > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > > $$ \begin{gathered} EV_{campaign} \\ = P_{campaign} \times Benefit_{peace,soc} \\ = 0.4 \times \$114B \\ = \$45.4B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Campaign Expected Social Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Campaign Expected Social Value (10,000 simulations) > > **Simulation Results Summary: Treaty Campaign Expected Social Value** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$45.4 billion | > | Mean (expected value) | \$45.3 billion | > | Median (50th percentile) | \$45 billion | > | Standard Deviation | \$3.51 billion | > | 90% Range (5th-95th percentile) | \[\$39.8 billion, \$51.5 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Campaign Expected Social Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Campaign Expected Social Value > > *This exceedance probability chart shows the likelihood that Treaty Campaign Expected Social Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Medical Research Spending as Percentage of Total Disease Burden: 0.0164% > **NOTE:** > > Medical research spending as percentage of total disease burden > > **Inputs**: > > - [Global Government Medical Research Spending](#sec-global_med_research_spending) 📊: \$67.5 billion (95% CI: \$54 billion - \$81 billion) > - [Total Annual Cost of War and Disease](#sec-global_total_health_and_war_cost_annual) 🔢: \$412 trillion > > $$ \begin{gathered} Pct_{RD:burden} \\ = \frac{Spending_{RD}}{Cost_{health+war}} \\ = \frac{\$67.5B}{\$412T} \\ = 0.0164\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{health+war} \\ = Cost_{war,total} + Burden_{disease} \\ = \$11.4T + \$400T \\ = \$412T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} Burden_{disease} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times Value_{QALY} \\ = 2.88B \times 92.6\% \times \$150K \\ = \$400T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Medical Research Spending as Percentage of Total Disease Burden** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War and Disease (USD/year) | -0.8815 | Strong driver | > | Global Government Medical Research Spending (USD) | 0.3507 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Medical Research Spending as Percentage of Total Disease Burden (10,000 simulations) > > **Simulation Results Summary: Medical Research Spending as Percentage of Total Disease Burden** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | 0.0164% | > | Mean (expected value) | 0.0175% | > | Median (50th percentile) | 0.0165% | > | Standard Deviation | 0.00483% | > | 90% Range (5th-95th percentile) | \[0.0116%, 0.0264%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Medical Research Spending as Percentage of Total Disease Burden; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Medical Research Spending as Percentage of Total Disease Burden > > *This exceedance probability chart shows the likelihood that Medical Research Spending as Percentage of Total Disease Burden will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Observed Medical Toolchain Anchor Costs: \$58.6 billion > **NOTE:** > > Sum of observed anchor costs for major medical toolchain programs. This is anchor evidence for the prosecutor reserve, not a claim that these programs alone cure disease. > > **Inputs**: > > - [Medical Toolchain Human Genome Project Cost](#sec-medical_toolchain_hgp_cost_usd) 📊: \$2.7 billion > - [Medical Toolchain NIH CRISPR Funding, FY2011-FY2018](#sec-medical_toolchain_crispr_nih_funding_2011_2018) 📊: \$3.1 billion > - [Medical Toolchain BRAIN Initiative Planned Budget](#sec-medical_toolchain_brain_initiative_planned_budget) 📊: \$4.5 billion > - [Medical Toolchain PCORnet Infrastructure Funding Anchor](#sec-medical_toolchain_pcornet_infrastructure_funding) 📊: \$325 million > - [Medical Toolchain HITECH EHR Incentive Estimated Spending](#sec-medical_toolchain_hitech_ehr_incentive_estimated_spending) 📊: \$30 billion > - [Medical Toolchain Operation Warp Speed Potential Vaccine Awards](#sec-medical_toolchain_ows_potential_vaccine_awards) 📊: \$18 billion > > $$ \begin{gathered} C_{tool,anchors} \\ = C_{tool,HGP} + C_{tool,CRISPR} + C_{tool,BRAIN} \\ + C_{tool,PCORnet} + C_{tool,EHR} + C_{tool,OWS} \\ = \$2.7B + \$3.1B + \$4.5B + \$325M + \$30B + \$18B \\ = \$58.6B \end{gathered} $$ > > *~ Medium confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Observed Medical Toolchain Anchor Costs is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 5.862e-02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$58.6 billion | > | Mean (expected value) | \$58.6 billion | > | Median (50th percentile) | \$58.6 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$58.6 billion, \$58.6 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Observed Medical Toolchain Anchor Costs collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 5.862e-02) > > Approximate deterministic value: **\$58.6 billion** ### Ratio of Military to Clinical Trials Spending: 45.3:1 > **NOTE:** > > Ratio of global military spending to all clinical trials spending (government + industry + nonprofit) > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Annual Global Spending on Clinical Trials](#sec-global_clinical_trials_spending_annual) 📊: \$60 billion (95% CI: \$50 billion - \$75 billion) > > $$ \begin{gathered} Ratio_{mil:trials} \\ = \frac{Spending_{mil}}{Spending_{trials}} \\ = \frac{\$2.72T}{\$60B} \\ = 45.3 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Ratio of Military to Clinical Trials Spending** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Spending on Clinical Trials (USD) | -0.9930 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Ratio of Military to Clinical Trials Spending (10,000 simulations) > > **Simulation Results Summary: Ratio of Military to Clinical Trials Spending** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 45.3:1 | > | Mean (expected value) | 46:1 | > | Median (50th percentile) | 45.9:1 | > | Standard Deviation | 5.98:1 | > | 90% Range (5th-95th percentile) | \[36.3:1, 54.4:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Ratio of Military to Clinical Trials Spending; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Ratio of Military to Clinical Trials Spending > > *This exceedance probability chart shows the likelihood that Ratio of Military to Clinical Trials Spending will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Ratio of Military to Government Clinical Trials Spending: 604:1 > **NOTE:** > > Ratio of global military spending to government clinical trials spending > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Annual Global Government Spending on Clinical Trials](#sec-global_government_clinical_trials_spending_annual) 📊: \$4.5 billion (95% CI: \$3 billion - \$6 billion) > > $$ \begin{gathered} Ratio_{mil:gov} \\ = \frac{Spending_{mil}}{Spending_{trials,gov}} \\ = \frac{\$2.72T}{\$4.5B} \\ = 604 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Ratio of Military to Government Clinical Trials Spending** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Government Spending on Clinical Trials (USD) | -0.9789 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Ratio of Military to Government Clinical Trials Spending (10,000 simulations) > > **Simulation Results Summary: Ratio of Military to Government Clinical Trials Spending** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 604:1 | > | Mean (expected value) | 635:1 | > | Median (50th percentile) | 621:1 | > | Standard Deviation | 126:1 | > | 90% Range (5th-95th percentile) | \[453:1, 888:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Ratio of Military to Government Clinical Trials Spending; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Ratio of Military to Government Clinical Trials Spending > > *This exceedance probability chart shows the likelihood that Ratio of Military to Government Clinical Trials Spending will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### NIH Traditional Trial Maximum Efficiency vs Pragmatic (%): 2.27% > **NOTE:** > > Maximum efficiency of NIH traditional Phase 3 trials relative to pragmatic trials, expressed as a percentage. Calculated as pragmatic cost / traditional cost. This is a CEILING on NIH trial efficiency because: (1) only 3.3% of NIH budget goes to clinical trials at all, and (2) the other 96.7% funds basic research with far lower marginal value when thousands of safe compounds already await testing. > > **Inputs**: > > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > - [Phase 3 Cost per Patient](#sec-traditional_phase3_cost_per_patient) 📊: \$41,000 (95% CI: \$20,000 - \$120,000) > > $$ \begin{gathered} \eta_{NIH,max} \\ = \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = \frac{\$929}{\$41K} \\ = 2.27\% \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for NIH Traditional Trial Maximum Efficiency vs Pragmatic (%)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Cost per Patient (USD/patient) | 0.8031 | Strong driver | > | Phase 3 Cost per Patient (USD/patient) | -0.4142 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: NIH Traditional Trial Maximum Efficiency vs Pragmatic (%) (10,000 simulations) > > **Simulation Results Summary: NIH Traditional Trial Maximum Efficiency vs Pragmatic (%)** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 2.27% | > | Mean (expected value) | 2.79% | > | Median (50th percentile) | 2.04% | > | Standard Deviation | 2.48% | > | 90% Range (5th-95th percentile) | \[0.476%, 7.84%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for NIH Traditional Trial Maximum Efficiency vs Pragmatic (%); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: NIH Traditional Trial Maximum Efficiency vs Pragmatic (%) > > *This exceedance probability chart shows the likelihood that NIH Traditional Trial Maximum Efficiency vs Pragmatic (%) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### NIH Trial Patients Fundable (Status Quo): 37,829 patients > **NOTE:** > > Trial participants the NIH’s current clinical-trials allocation funds each year at traditional per-patient costs: the status quo output of the existing budget. > > **Inputs**: > > - [NIH Annual Budget](#sec-nih_annual_budget) 📊: \$47 billion (95% CI: \$45 billion - \$50 billion) > - [NIH Clinical Trials Spending Percentage](#sec-nih_clinical_trials_spending_pct) 📊: 3.3% (95% CI: 2% - 5%) > - [Phase 3 Cost per Patient](#sec-traditional_phase3_cost_per_patient) 📊: \$41,000 (95% CI: \$20,000 - \$120,000) > > $$ \begin{gathered} Fundable \\ = \text{NIH\_BUDGET} \times \text{TRIALS\_PCT} / \text{TRADITIONAL\_COST\_PER\_PATIENT} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for NIH Trial Patients Fundable (Status Quo)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Phase 3 Cost per Patient (USD/patient) | -0.7605 | Strong driver | > | NIH Clinical Trials Spending Percentage (percentage) | 0.4534 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: NIH Trial Patients Fundable (Status Quo) (10,000 simulations) > > **Simulation Results Summary: NIH Trial Patients Fundable (Status Quo)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 37,829 | > | Mean (expected value) | 47,615 | > | Median (50th percentile) | 43,777 | > | Standard Deviation | 23,373 | > | 90% Range (5th-95th percentile) | \[16,066, 91,339\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for NIH Trial Patients Fundable (Status Quo); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: NIH Trial Patients Fundable (Status Quo) > > *This exceedance probability chart shows the likelihood that NIH Trial Patients Fundable (Status Quo) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Nuclear Winter Overkill Factor: 122x > **NOTE:** > > How many times the global nuclear arsenal exceeds the threshold for civilizational collapse via regional-scale nuclear winter (~100 warheads, ~5 Tg soot, ~2 billion famine deaths, global food system collapse). The arsenal-based overkill factor against the apocalypse the median human experiences. > > **Inputs**: > > - [Global Nuclear Warhead Count](#sec-global_warhead_count) 📊: 12,241 warheads > - [Nuclear Winter Warhead Threshold](#sec-nuclear_winter_warhead_threshold) 📊: 100 warheads (95% CI: 50 warheads - 300 warheads) > > $$ \begin{gathered} Overkill_{winter} \\ = \frac{W_{global}}{W_{winter}} \\ = \frac{12{,}200}{100} \\ = 122 \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Nuclear Winter Overkill Factor** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Nuclear Winter Warhead Threshold (warheads) | -0.9008 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Nuclear Winter Overkill Factor (10,000 simulations) > > **Simulation Results Summary: Nuclear Winter Overkill Factor** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 122x | > | Mean (expected value) | 88.2x | > | Median (50th percentile) | 70.4x | > | Standard Deviation | 48.3x | > | 90% Range (5th-95th percentile) | \[42.6x, 198x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Nuclear Winter Overkill Factor; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Nuclear Winter Overkill Factor > > *This exceedance probability chart shows the likelihood that Nuclear Winter Overkill Factor will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Spare Apocalypses (Overkill Factor Minus One): 121 apocalypses > **NOTE:** > > Spare apocalypses: how many civilization-ending nuclear winters the global arsenal can trigger beyond the one that would already end civilization. The nuclear winter overkill factor minus one. Used wherever the manual jokes about the surplus (the apocalypses kept in case the first does not take). > > **Inputs**: > > - [Nuclear Winter Overkill Factor](#sec-nuclear_winter_overkill_factor) 🔢: 122x > > $$ Overkill_{spare} = Overkill_{winter} - 1 = 122 - 1 = 121 $$ where: $$ \begin{gathered} Overkill_{winter} \\ = \frac{W_{global}}{W_{winter}} \\ = \frac{12{,}200}{100} \\ = 122 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Spare Apocalypses (Overkill Factor Minus One)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-----------------------------------|------------------------:|:---------------| > | Nuclear Winter Overkill Factor (x) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Spare Apocalypses (Overkill Factor Minus One) (10,000 simulations) > > **Simulation Results Summary: Spare Apocalypses (Overkill Factor Minus One)** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 121 | > | Mean (expected value) | 87.2 | > | Median (50th percentile) | 69.4 | > | Standard Deviation | 48.3 | > | 90% Range (5th-95th percentile) | \[41.6, 197\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Spare Apocalypses (Overkill Factor Minus One); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Spare Apocalypses (Overkill Factor Minus One) > > *This exceedance probability chart shows the likelihood that Spare Apocalypses (Overkill Factor Minus One) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Peace Dividend from 1% Reduction in Total War Costs: \$114 billion > **NOTE:** > > Annual peace dividend from 1% reduction in total war costs (theoretical maximum at ε=1.0) > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > > $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Peace Dividend from 1% Reduction in Total War Costs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Peace Dividend from 1% Reduction in Total War Costs (10,000 simulations) > > **Simulation Results Summary: Annual Peace Dividend from 1% Reduction in Total War Costs** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$114 billion | > | Mean (expected value) | \$113 billion | > | Median (50th percentile) | \$113 billion | > | Standard Deviation | \$8.77 billion | > | 90% Range (5th-95th percentile) | \[\$99.6 billion, \$129 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Peace Dividend from 1% Reduction in Total War Costs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Peace Dividend from 1% Reduction in Total War Costs > > *This exceedance probability chart shows the likelihood that Annual Peace Dividend from 1% Reduction in Total War Costs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Conflict Reduction Benefits from 1% Less Military Spending: \$86.4 billion > **NOTE:** > > Conflict reduction benefits from 1% less military spending (lower confidence - assumes proportional relationship) > > **Inputs**: > > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > > $$ \begin{gathered} Savings_{conflict} \\ = Benefit_{peace,soc} - Funding_{treaty} \\ = \$114B - \$27.2B \\ = \$86.4B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ **Methodology**: [Direct Calculation](#calculated) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Conflict Reduction Benefits from 1% Less Military Spending** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Conflict Reduction Benefits from 1% Less Military Spending (10,000 simulations) > > **Simulation Results Summary: Conflict Reduction Benefits from 1% Less Military Spending** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$86.4 billion | > | Mean (expected value) | \$86 billion | > | Median (50th percentile) | \$85.4 billion | > | Standard Deviation | \$8.77 billion | > | 90% Range (5th-95th percentile) | \[\$72.4 billion, \$101 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Conflict Reduction Benefits from 1% Less Military Spending; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Conflict Reduction Benefits from 1% Less Military Spending > > *This exceedance probability chart shows the likelihood that Conflict Reduction Benefits from 1% Less Military Spending will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Savings from 1% Reduction in Direct War Costs: \$76.6 billion > **NOTE:** > > Annual savings from 1% reduction in direct war costs > > **Inputs**: > > - [Total Annual Direct War Costs](#sec-global_annual_war_direct_costs_total) 🔢: \$7.66 trillion > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > > $$ \begin{gathered} Savings_{direct} \\ = Cost_{war,direct} \times Reduce_{treaty} \\ = \$7.66T \times 1\% \\ = \$76.6B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Savings from 1% Reduction in Direct War Costs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Direct War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Savings from 1% Reduction in Direct War Costs (10,000 simulations) > > **Simulation Results Summary: Annual Savings from 1% Reduction in Direct War Costs** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$76.6 billion | > | Mean (expected value) | \$76.2 billion | > | Median (50th percentile) | \$75.6 billion | > | Standard Deviation | \$7.42 billion | > | 90% Range (5th-95th percentile) | \[\$65.2 billion, \$89.5 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Savings from 1% Reduction in Direct War Costs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Savings from 1% Reduction in Direct War Costs > > *This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Direct War Costs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Savings from 1% Reduction in Indirect War Costs: \$37 billion > **NOTE:** > > Annual savings from 1% reduction in indirect war costs > > **Inputs**: > > - [Total Annual Indirect War Costs](#sec-global_annual_war_indirect_costs_total) 🔢: \$3.7 trillion > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > > $$ \begin{gathered} Savings_{indirect} \\ = Cost_{war,indirect} \times Reduce_{treaty} \\ = \$3.7T \times 1\% \\ = \$37B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Savings from 1% Reduction in Indirect War Costs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Indirect War Costs (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Savings from 1% Reduction in Indirect War Costs (10,000 simulations) > > **Simulation Results Summary: Annual Savings from 1% Reduction in Indirect War Costs** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$37 billion | > | Mean (expected value) | \$37 billion | > | Median (50th percentile) | \$36.6 billion | > | Standard Deviation | \$4.66 billion | > | 90% Range (5th-95th percentile) | \[\$29.8 billion, \$45.6 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Savings from 1% Reduction in Indirect War Costs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Savings from 1% Reduction in Indirect War Costs > > *This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Indirect War Costs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Per-Person 80-Year Peace Dividend: \$290,052 > **NOTE:** > > Per-person share of the 80-year cumulative peace dividend, averaged across the global population. Not literally a check in the reader’s pocket: most of it arrives as infrastructure not destroyed, wages not taxed to rebuild things that should not have been destroyed, and conflicts that never happen. Per-capita division hides that the poorest bear far more than the average today. > > **Inputs**: > > - [Cumulative Peace Dividend Over 80 Years](#sec-peace_dividend_lifetime_total) 🔢: \$2.32 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} Savings_{pp,LT} \\ = \frac{Savings_{LT}}{Pop_{global}} \\ = \frac{\$2320T}{8B} \\ = \$290K \end{gathered} $$ where: $$ \begin{gathered} Savings_{LT} \\ = Cost_{war,cum,baseline} - Cost_{war,cum,treaty} \\ = \$3220T - \$899T \\ = \$2320T \end{gathered} $$ where: $$ Cost_{war,cum,baseline} = C \times ((1 + g)^{80} - 1) / g $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,cum,treaty} \\ = C \times (1 - Reduce_{treaty}) \times 80 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Per-Person 80-Year Peace Dividend** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Cumulative Peace Dividend Over 80 Years (USD) | 0.9895 | Strong driver | > | Global Population in 2024 (of people) | -0.1552 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Per-Person 80-Year Peace Dividend (10,000 simulations) > > **Simulation Results Summary: Per-Person 80-Year Peace Dividend** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | \$290,052 | > | Mean (expected value) | \$289,102 | > | Median (50th percentile) | \$287,607 | > | Standard Deviation | \$22,628 | > | 90% Range (5th-95th percentile) | \[\$253,999, \$328,716\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Per-Person 80-Year Peace Dividend; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Per-Person 80-Year Peace Dividend > > *This exceedance probability chart shows the likelihood that Per-Person 80-Year Peace Dividend will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Per-Person Peace Dividend Compounded at 13% over 80 Years: \$53.7 million > **NOTE:** > > Per-person future value at year 80 of the treaty peace dividend stream, compounded at the illustrative long-term real return rate (13%, Nasdaq-100 historical). Each year’s savings are invested at the end of that year and compound until year 80. This is an opportunity-cost framing, not a promise of cash in the reader’s pocket: the ‘return’ is the real productive use of capital that weapons spending displaced, not a literal brokerage account. > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > - [Military Spending Real CAGR (20-Year)](#sec-global_military_spending_real_cagr_20yr) 🔢: 2.76% > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > - [Assumed Long-Term Real Return for Investment Compounding](#sec-peace_dividend_investment_real_return_rate): 13% > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} FV_{pp,treaty} \\ = [Σ_{t=0..79} (C(1 + g)^t - C(1-p)) \times (1 \\ + r)^{79-t}] / Pop_{global} \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Per-Person Peace Dividend Compounded at 13% over 80 Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 0.9895 | Strong driver | > | Global Population in 2024 (of people) | -0.1552 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Per-Person Peace Dividend Compounded at 13% over 80 Years (10,000 simulations) > > **Simulation Results Summary: Per-Person Peace Dividend Compounded at 13% over 80 Years** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$53.7 million | > | Mean (expected value) | \$53.5 million | > | Median (50th percentile) | \$53.2 million | > | Standard Deviation | \$4.19 million | > | 90% Range (5th-95th percentile) | \[\$47 million, \$60.8 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Per-Person Peace Dividend Compounded at 13% over 80 Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Per-Person Peace Dividend Compounded at 13% over 80 Years > > *This exceedance probability chart shows the likelihood that Per-Person Peace Dividend Compounded at 13% over 80 Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cumulative Peace Dividend Over 80 Years: \$2.32 quadrillion > **NOTE:** > > Cumulative peace dividend over 80 years (one human lifespan): the baseline 80-year war cost (SIPRI trajectory) minus the treaty 80-year war cost (flat at 99% of today). Assumes elasticity of 1.0 between military spending and war costs, which is almost certainly conservative because the political act of passing the treaty itself would reflect and reinforce a ‘war is stupid’ consensus that reduces externalities super-proportionally. > > **Inputs**: > > - [Cumulative 80-Year War Cost (Baseline Trajectory)](#sec-global_war_cost_lifetime_cumulative_baseline) 🔢: \$3.22 quadrillion > - [Cumulative 80-Year War Cost (Treaty Trajectory)](#sec-global_war_cost_lifetime_cumulative_treaty) 🔢: \$899 trillion > > $$ \begin{gathered} Savings_{LT} \\ = Cost_{war,cum,baseline} - Cost_{war,cum,treaty} \\ = \$3220T - \$899T \\ = \$2320T \end{gathered} $$ where: $$ Cost_{war,cum,baseline} = C \times ((1 + g)^{80} - 1) / g $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,cum,treaty} \\ = C \times (1 - Reduce_{treaty}) \times 80 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cumulative Peace Dividend Over 80 Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Cumulative 80-Year War Cost (Treaty Trajectory) (USD) | 1.0000 | Strong driver | > | Cumulative 80-Year War Cost (Baseline Trajectory) (USD) | 0.0000 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cumulative Peace Dividend Over 80 Years (10,000 simulations) > > **Simulation Results Summary: Cumulative Peace Dividend Over 80 Years** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$2.32 quadrillion | > | Mean (expected value) | \$2.31 quadrillion | > | Median (50th percentile) | \$2.3 quadrillion | > | Standard Deviation | \$179 trillion | > | 90% Range (5th-95th percentile) | \[\$2.03 quadrillion, \$2.63 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cumulative Peace Dividend Over 80 Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cumulative Peace Dividend Over 80 Years > > *This exceedance probability chart shows the likelihood that Cumulative Peace Dividend Over 80 Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Share of 80-Year War Cost Avoided by Treaty: 72.1% > **NOTE:** > > Fraction of the baseline 80-year war cost avoided by the treaty. Because the baseline compounds exponentially while the treaty holds spending flat, cutting 1% today and halting the growth trajectory avoids about 72% of the cumulative 80-year war cost. Most of the savings come from the halted growth, not the headline 1% cut. > > **Inputs**: > > - [Cumulative Peace Dividend Over 80 Years](#sec-peace_dividend_lifetime_total) 🔢: \$2.32 quadrillion > - [Cumulative 80-Year War Cost (Baseline Trajectory)](#sec-global_war_cost_lifetime_cumulative_baseline) 🔢: \$3.22 quadrillion > > $$ \begin{gathered} \phi_{avoided} \\ = \frac{Savings_{LT}}{Cost_{war,cum,baseline}} \\ = \frac{\$2320T}{\$3220T} \\ = 72.1\% \end{gathered} $$ where: $$ \begin{gathered} Savings_{LT} \\ = Cost_{war,cum,baseline} - Cost_{war,cum,treaty} \\ = \$3220T - \$899T \\ = \$2320T \end{gathered} $$ where: $$ Cost_{war,cum,baseline} = C \times ((1 + g)^{80} - 1) / g $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{mil,20yr} \\ = \left(\frac{Spending_{mil}}{Spending_{mil,2005}}\right)^{\frac{1}{19}} - 1 \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,cum,treaty} \\ = C \times (1 - Reduce_{treaty}) \times 80 \end{gathered} $$ \#### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Share of 80-Year War Cost Avoided by Treaty is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 2.220e-16 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 72.1% | > | Mean (expected value) | 72.1% | > | Median (50th percentile) | 72.1% | > | Standard Deviation | 1.35e-14% | > | 90% Range (5th-95th percentile) | \[72.1%, 72.1%\] | > > #### Exceedance Probability > > **Exceedance note:** Share of 80-Year War Cost Avoided by Treaty collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 2.220e-16 \<= tolerance 1.000e-12) > > Approximate deterministic value: **72.1%** ### Pentagon Unaccounted Funds in Clinical Trial Years: 547 years > **NOTE:** > > Number of years of clinical trial funding at current government spending levels that the Pentagon’s unaccounted funds could have provided > > **Inputs**: > > - [Pentagon Unaccounted Funds](#sec-pentagon_unaccounted_funds) 📊: \$2.46 trillion > - [Annual Global Government Spending on Clinical Trials](#sec-global_government_clinical_trials_spending_annual) 📊: \$4.5 billion (95% CI: \$3 billion - \$6 billion) > > $$ \begin{gathered} Years_{pentagon,trials} \\ = \frac{Funds_{pentagon,unaccounted}}{Spending_{trials,gov}} \\ = \frac{\$2.46T}{\$4.5B} \\ = 547 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pentagon Unaccounted Funds in Clinical Trial Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Government Spending on Clinical Trials (USD) | -0.9789 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pentagon Unaccounted Funds in Clinical Trial Years (10,000 simulations) > > **Simulation Results Summary: Pentagon Unaccounted Funds in Clinical Trial Years** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 547 | > | Mean (expected value) | 574 | > | Median (50th percentile) | 562 | > | Standard Deviation | 114 | > | 90% Range (5th-95th percentile) | \[410, 803\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pentagon Unaccounted Funds in Clinical Trial Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pentagon Unaccounted Funds in Clinical Trial Years > > *This exceedance probability chart shows the likelihood that Pentagon Unaccounted Funds in Clinical Trial Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pentagon Unaccounted Funds False Claims Analog Exposure: \$7.38 trillion > **NOTE:** > > False Claims Act-style treble-damages exposure on Pentagon unaccounted funds, used as a corporate-defendant audit analogy rather than a literal claim under existing sovereign law. > > **Inputs**: > > - [Pentagon Unaccounted Funds](#sec-pentagon_unaccounted_funds) 📊: \$2.46 trillion > - [Corporate Analog False Claims Act Treble Multiplier](#sec-corporate_analog_false_claims_treble_multiplier) 📊: 3x > > $$ \begin{gathered} Exposure_{pentagon,FCA} \\ = Funds_{pentagon,unaccounted} \times m_{FCA} \\ = \$2.46T \times 3 \\ = \$7.38T \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Pentagon Unaccounted Funds False Claims Analog Exposure is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 7.380e+00) > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$7.38 trillion | > | Mean (expected value) | \$7.38 trillion | > | Median (50th percentile) | \$7.38 trillion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$7.38 trillion, \$7.38 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** Pentagon Unaccounted Funds False Claims Analog Exposure collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 7.380e+00) > > Approximate deterministic value: **\$7.38 trillion** ### Annual Lives Saved by Pharmaceuticals: 12.4 million deaths > **NOTE:** > > Annual lives saved by pharmaceutical interventions globally. Derived from Lichtenberg (2019) finding of 148.7M life-years saved, divided by assumed 12-year average life extension per beneficiary. Note: Life-years is the primary metric; lives is an approximation for intuitive communication. > > **Inputs**: > > - [Annual Life-Years Saved by Pharmaceuticals](#sec-pharma_life_years_saved_annual) 📊: 149 million life-years (95% CI: 79.4 million life-years - 240 million life-years) > - [Average Life Extension per Beneficiary](#sec-avg_life_extension_per_beneficiary): 12 years (95% CI: 8 years - 18 years) > > $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ > > **Methodology**:^([10](../references.llms.md#ref-lichtenberg-life-years-saved-2019)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Lives Saved by Pharmaceuticals** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Life-Years Saved by Pharmaceuticals (life-years) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Lives Saved by Pharmaceuticals (10,000 simulations) > > **Simulation Results Summary: Annual Lives Saved by Pharmaceuticals** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 12.4 million | > | Mean (expected value) | 12.3 million | > | Median (50th percentile) | 11.9 million | > | Standard Deviation | 3.17 million | > | 90% Range (5th-95th percentile) | \[7.72 million, 18.6 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Lives Saved by Pharmaceuticals; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Lives Saved by Pharmaceuticals > > *This exceedance probability chart shows the likelihood that Annual Lives Saved by Pharmaceuticals will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pharma-to-Defense Net Profit Margin Ratio: 3.72x > **NOTE:** > > Ratio of pharmaceutical to defense net profit margins > > **Inputs**: > > - [Pharmaceutical Net Profit Margin](#sec-pharma_net_profit_margin) 📊: 18.5% (95% CI: 15% - 22%) > - [Aerospace/Defense Net Profit Margin](#sec-defense_net_profit_margin) 📊: 4.99% (95% CI: 4% - 6%) > > $$ \begin{gathered} \frac{Margin_{pharma}}{Margin_{def}} \\ = \frac{Margin_{pharma}}{Margin_{def}} \\ = \frac{18.5\%}{4.99\%} \\ = 3.72 \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** Pharma-to-Defense Net Profit Margin Ratio is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 3.715e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 3.72x | > | Mean (expected value) | 3.72x | > | Median (50th percentile) | 3.72x | > | Standard Deviation | 4.44e-16x | > | 90% Range (5th-95th percentile) | \[3.72x, 3.72x\] | > > #### Exceedance Probability > > **Exceedance note:** Pharma-to-Defense Net Profit Margin Ratio collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 3.715e-12) > > Approximate deterministic value: **3.72x** ### Global Governance Efficiency Score: 51.8% > **NOTE:** > > Global Governance Efficiency Score from Political Dysfunction Tax paper. E = Adjusted W_real / W_max, where W_real = GDP - waste, W_max = W_real + opportunity cost. Paper calculates 30-52% efficiency (using \$110.9T adjusted / \$211.9T maximum). This means civilization operates at roughly half its technological potential. > > **Inputs**: > > - [Adjusted Realized Welfare](#sec-political_dysfunction_global_realized_welfare_adjusted) 🔢: \$109 trillion > - [Theoretical Maximum Welfare (Conservative)](#sec-political_dysfunction_global_theoretical_max_welfare) 🔢: \$210 trillion > > $$ \begin{gathered} E_{gov} \\ = \frac{W_{real}}{W_{max}} \\ = \frac{\$109T}{\$210T} \\ = 51.8\% \end{gathered} $$ where: $$ \begin{gathered} W_{real} \\ = GDP_{global} - W_{waste} \\ = \$115T - \$6.28T \\ = \$109T \end{gathered} $$ where: $$ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.98T + \$1.3T \\ = \$6.28T \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ where: $$ W_{max} = W_{real} + O_{total} = \$109T + \$101T = \$210T $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ **Methodology**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Governance Efficiency Score** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Theoretical Maximum Welfare (Conservative) (USD) | -0.9806 | Strong driver | > | Adjusted Realized Welfare (USD) | 0.0219 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Governance Efficiency Score (10,000 simulations) > > **Simulation Results Summary: Global Governance Efficiency Score** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 51.8% | > | Mean (expected value) | 53% | > | Median (50th percentile) | 53.4% | > | Standard Deviation | 7.31% | > | 90% Range (5th-95th percentile) | \[40.3%, 64.6%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Governance Efficiency Score; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Governance Efficiency Score > > *This exceedance probability chart shows the likelihood that Global Governance Efficiency Score will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Opportunity Cost as % of GDP: 87.8% > **NOTE:** > > Global opportunity cost as percentage of global GDP. \$101T / \$115T = ~88% of current GDP in unrealized potential. This represents the ‘buried multipliers’ of the global economy. > > **Inputs**: > > - [Global Opportunity Cost Total](#sec-political_dysfunction_global_opportunity_cost_total) 🔢: \$101 trillion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > > $$ \begin{gathered} O_{\%GDP} \\ = \frac{O_{total}}{GDP_{global}} \\ = \frac{\$101T}{\$115T} \\ = 87.8\% \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ **Methodology**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Opportunity Cost as % of GDP** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:------------------------------------|------------------------:|:---------------| > | Global Opportunity Cost Total (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Opportunity Cost as % of GDP (10,000 simulations) > > **Simulation Results Summary: Global Opportunity Cost as % of GDP** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 87.8% | > | Mean (expected value) | 87.5% | > | Median (50th percentile) | 82.6% | > | Standard Deviation | 27.5% | > | 90% Range (5th-95th percentile) | \[51.8%, 140%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Opportunity Cost as % of GDP; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Opportunity Cost as % of GDP > > *This exceedance probability chart shows the likelihood that Global Opportunity Cost as % of GDP will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Opportunity Cost Total: \$101 trillion > **NOTE:** > > Total global opportunity cost from governance failures: health innovation delays (\$34T), outcome-blind R&D allocation (\$4T), lead poisoning (\$6T), migration restrictions (\$57T). Sum: \$101T annually in unrealized potential. > > **Inputs**: > > - [Global Health Opportunity Cost](#sec-political_dysfunction_global_health_opportunity_cost) 📊: \$34 trillion (95% CI: \$20 trillion - \$80 trillion) > - [Global R&D Misallocation Cost](#sec-political_dysfunction_global_science_opportunity_cost) 📊: \$4 trillion (95% CI: \$2 trillion - \$10 trillion) > - [Global Lead Poisoning Cost](#sec-political_dysfunction_global_lead_opportunity_cost) 📊: \$6 trillion (95% CI: \$4 trillion - \$8 trillion) > - [Global Migration Opportunity Cost](#sec-political_dysfunction_global_migration_opportunity_cost) 📊: \$57 trillion (95% CI: \$5 trillion - \$170 trillion) > > $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ > > **Methodology**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Opportunity Cost Total** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Migration Opportunity Cost (USD) | 0.8920 | Strong driver | > | Global Health Opportunity Cost (USD) | 0.4367 | Moderate driver | > | Global R&D Misallocation Cost (USD) | 0.0578 | Minimal effect | > | Global Lead Poisoning Cost (USD) | 0.0300 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Opportunity Cost Total (10,000 simulations) > > **Simulation Results Summary: Global Opportunity Cost Total** > > | Statistic | Value | > |:--------------------------------|------------------------------------:| > | Baseline (deterministic) | \$101 trillion | > | Mean (expected value) | \$101 trillion | > | Median (50th percentile) | \$95 trillion | > | Standard Deviation | \$31.6 trillion | > | 90% Range (5th-95th percentile) | \[\$59.6 trillion, \$161 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Opportunity Cost Total; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Opportunity Cost Total > > *This exceedance probability chart shows the likelihood that Global Opportunity Cost Total will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Adjusted Realized Welfare: \$109 trillion > **NOTE:** > > Adjusted realized welfare after subtracting measured governance waste from global GDP. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Global Waste Total (Efficiency Accounting)](#sec-political_dysfunction_global_waste_total) 🔢: \$6.28 trillion > > $$ \begin{gathered} W_{real} \\ = GDP_{global} - W_{waste} \\ = \$115T - \$6.28T \\ = \$109T \end{gathered} $$ where: $$ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.98T + \$1.3T \\ = \$6.28T \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Adjusted Realized Welfare** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Waste Total (Efficiency Accounting) (USD) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Adjusted Realized Welfare (10,000 simulations) > > **Simulation Results Summary: Adjusted Realized Welfare** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$109 trillion | > | Mean (expected value) | \$109 trillion | > | Median (50th percentile) | \$109 trillion | > | Standard Deviation | \$384 billion | > | 90% Range (5th-95th percentile) | \[\$108 trillion, \$109 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Adjusted Realized Welfare; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Adjusted Realized Welfare > > *This exceedance probability chart shows the likelihood that Adjusted Realized Welfare will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Theoretical Maximum Welfare (Conservative): \$210 trillion > **NOTE:** > > Conservative theoretical maximum welfare under opportunity-cost recapture assumptions. > > **Inputs**: > > - [Adjusted Realized Welfare](#sec-political_dysfunction_global_realized_welfare_adjusted) 🔢: \$109 trillion > - [Global Opportunity Cost Total](#sec-political_dysfunction_global_opportunity_cost_total) 🔢: \$101 trillion > > $$ W_{max} = W_{real} + O_{total} = \$109T + \$101T = \$210T $$ where: $$ \begin{gathered} W_{real} \\ = GDP_{global} - W_{waste} \\ = \$115T - \$6.28T \\ = \$109T \end{gathered} $$ where: $$ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.98T + \$1.3T \\ = \$6.28T \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Theoretical Maximum Welfare (Conservative)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:------------------------------------|------------------------:|:---------------| > | Global Opportunity Cost Total (USD) | 0.9998 | Strong driver | > | Adjusted Realized Welfare (USD) | 0.0121 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Theoretical Maximum Welfare (Conservative) (10,000 simulations) > > **Simulation Results Summary: Theoretical Maximum Welfare (Conservative)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$210 trillion | > | Mean (expected value) | \$209 trillion | > | Median (50th percentile) | \$204 trillion | > | Standard Deviation | \$31.6 trillion | > | 90% Range (5th-95th percentile) | \[\$168 trillion, \$270 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Theoretical Maximum Welfare (Conservative); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Theoretical Maximum Welfare (Conservative) > > *This exceedance probability chart shows the likelihood that Theoretical Maximum Welfare (Conservative) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Waste Total (Efficiency Accounting): \$6.28 trillion > **NOTE:** > > Global waste deduction used in Political Dysfunction Tax efficiency accounting. Combines US governance waste estimate with global explicit fossil-fuel subsidies. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > - [Global Fossil Fuel Subsidies](#sec-political_dysfunction_global_fossil_fuel_subsidies) 📊: \$1.3 trillion (95% CI: \$1.1 trillion - \$1.5 trillion) > > $$ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.98T + \$1.3T \\ = \$6.28T \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Waste Total (Efficiency Accounting)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | 0.9665 | Strong driver | > | Global Fossil Fuel Subsidies (USD) | 0.2482 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Waste Total (Efficiency Accounting) (10,000 simulations) > > **Simulation Results Summary: Global Waste Total (Efficiency Accounting)** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$6.28 trillion | > | Mean (expected value) | \$6.28 trillion | > | Median (50th percentile) | \$6.26 trillion | > | Standard Deviation | \$384 billion | > | 90% Range (5th-95th percentile) | \[\$5.67 trillion, \$6.93 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Waste Total (Efficiency Accounting); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Waste Total (Efficiency Accounting) > > *This exceedance probability chart shows the likelihood that Global Waste Total (Efficiency Accounting) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Political Dysfunction Tax per Household of Four (Annual): \$50,500 > **NOTE:** > > Annual household burden for a 4-person household implied by global Political Dysfunction Tax. > > **Inputs**: > > - [Political Dysfunction Tax per Person (Annual)](#sec-political_dysfunction_tax_per_person_annual) 🔢: \$12,625 > > $$ T_{pd,hh4} = T_{pd,pc} \times 4 = \$12.6K \times 4 = \$50.5K $$ where: $$ \begin{gathered} T_{pd,pc} \\ = \frac{O_{total}}{Pop_{global}} \\ = \frac{\$101T}{8B} \\ = \$12.6K \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Political Dysfunction Tax per Household of Four (Annual)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Political Dysfunction Tax per Person (Annual) (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Political Dysfunction Tax per Household of Four (Annual) (10,000 simulations) > > **Simulation Results Summary: Political Dysfunction Tax per Household of Four (Annual)** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$50,500 | > | Mean (expected value) | \$50,290 | > | Median (50th percentile) | \$47,521 | > | Standard Deviation | \$15,820 | > | 90% Range (5th-95th percentile) | \[\$29,798, \$80,308\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Political Dysfunction Tax per Household of Four (Annual); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Political Dysfunction Tax per Household of Four (Annual) > > *This exceedance probability chart shows the likelihood that Political Dysfunction Tax per Household of Four (Annual) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Political Dysfunction Tax per Person (Annual): \$12,625 > **NOTE:** > > Annual per-person burden implied by global Political Dysfunction Tax opportunity costs. > > **Inputs**: > > - [Global Opportunity Cost Total](#sec-political_dysfunction_global_opportunity_cost_total) 🔢: \$101 trillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} T_{pd,pc} \\ = \frac{O_{total}}{Pop_{global}} \\ = \frac{\$101T}{8B} \\ = \$12.6K \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Political Dysfunction Tax per Person (Annual)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Opportunity Cost Total (USD) | 0.9996 | Strong driver | > | Global Population in 2024 (of people) | -0.0386 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Political Dysfunction Tax per Person (Annual) (10,000 simulations) > > **Simulation Results Summary: Political Dysfunction Tax per Person (Annual)** > > | Statistic | Value | > |:--------------------------------|----------------------:| > | Baseline (deterministic) | \$12,625 | > | Mean (expected value) | \$12,572 | > | Median (50th percentile) | \$11,880 | > | Standard Deviation | \$3,955 | > | 90% Range (5th-95th percentile) | \[\$7,450, \$20,077\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Political Dysfunction Tax per Person (Annual); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Political Dysfunction Tax per Person (Annual) > > *This exceedance probability chart shows the likelihood that Political Dysfunction Tax per Person (Annual) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Percentage Military Spending Cut After WW2: 87.6% > **NOTE:** > > Percentage US military spending cut after WW2 (1945-1947, inflation-adjusted: \$1,420B to \$176B in constant 2024 dollars) > > **Inputs**: > > - [US Military Spending in 1947 (Constant 2024 Dollars)](#sec-us_military_spending_1947_annual_2024usd) 📊: \$176 billion > - [US Military Spending at WW2 Peak (Constant 2024 Dollars)](#sec-us_military_spending_1945_peak_annual_2024usd) 📊: \$1.42 trillion > > $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Percentage Military Spending Cut After WW2 is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 87.6% | > | Mean (expected value) | 87.6% | > | Median (50th percentile) | 87.6% | > | Standard Deviation | 0% | > | 90% Range (5th-95th percentile) | \[87.6%, 87.6%\] | > > #### Exceedance Probability > > **Exceedance note:** Percentage Military Spending Cut After WW2 collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **87.6%** ### Pragmatic Trial Cost per QALY (RECOVERY): \$4 > **NOTE:** > > Cost per QALY for pragmatic platform trials, calculated from RECOVERY trial data. Uses global impact methodology: trial cost divided by total QALYs from downstream adoption. This measures research efficiency (discovery value), not clinical intervention ICER. > > **Inputs**: > > - [RECOVERY Trial Total Cost](#sec-recovery_trial_total_cost) 📊: \$20 million (95% CI: \$15 million - \$25 million) > - [RECOVERY Trial Total QALYs Generated](#sec-recovery_trial_total_qalys_generated) 🔢: 5 million QALYs > > $$ \begin{gathered} Cost_{pragmatic,QALY} \\ = \frac{Cost_{RECOVERY}}{QALY_{RECOVERY}} \\ = \frac{\$20M}{5M} \\ = \$4 \end{gathered} $$ where: $$ \begin{gathered} QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} $$ **Methodology**:^([8](../references.llms.md#ref-recovery-trial-82x-cost-reduction)) > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Pragmatic Trial Cost per QALY (RECOVERY)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | RECOVERY Trial Total QALYs Generated (QALYs) | -0.7945 | Strong driver | > | RECOVERY Trial Total Cost (USD) | 0.2519 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Pragmatic Trial Cost per QALY (RECOVERY) (10,000 simulations) > > **Simulation Results Summary: Pragmatic Trial Cost per QALY (RECOVERY)** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | \$4 | > | Mean (expected value) | \$5.03 | > | Median (50th percentile) | \$4.54 | > | Standard Deviation | \$2.47 | > | 90% Range (5th-95th percentile) | \[\$1.91, \$9.8\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Cost per QALY (RECOVERY); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Pragmatic Trial Cost per QALY (RECOVERY) > > *This exceedance probability chart shows the likelihood that Pragmatic Trial Cost per QALY (RECOVERY) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Price of Apocalypse (Minimum Viable Apocalypse): \$752 million > **NOTE:** > > The Price of Apocalypse: the annual cost of maintaining enough nuclear warheads to trigger a civilization-ending nuclear winter (~100 warheads, ~5 Tg soot, ~2 billion famine deaths, global food system collapse). Calculated as global nuclear spending divided by the nuclear winter overkill factor. > > **Inputs**: > > - [Global Nuclear Weapons Spending](#sec-global_nuclear_weapons_spending) 📊: \$92 billion > - [Nuclear Winter Overkill Factor](#sec-nuclear_winter_overkill_factor) 🔢: 122x > > $$ \begin{gathered} P_{apocalypse} \\ = \frac{S_{nuke}}{Overkill_{winter}} \\ = \frac{\$92B}{122} \\ = \$752M \end{gathered} $$ where: $$ \begin{gathered} Overkill_{winter} \\ = \frac{W_{global}}{W_{winter}} \\ = \frac{12{,}200}{100} \\ = 122 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Price of Apocalypse (Minimum Viable Apocalypse)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-----------------------------------|------------------------:|:---------------| > | Nuclear Winter Overkill Factor (x) | -0.9008 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Price of Apocalypse (Minimum Viable Apocalypse) (10,000 simulations) > > **Simulation Results Summary: Price of Apocalypse (Minimum Viable Apocalypse)** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$752 million | > | Mean (expected value) | \$1.31 billion | > | Median (50th percentile) | \$1.31 billion | > | Standard Deviation | \$544 million | > | 90% Range (5th-95th percentile) | \[\$464 million, \$2.16 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Price of Apocalypse (Minimum Viable Apocalypse); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Price of Apocalypse (Minimum Viable Apocalypse) > > *This exceedance probability chart shows the likelihood that Price of Apocalypse (Minimum Viable Apocalypse) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### PRIZE Pool Annual Return: 15.8% > **NOTE:** > > Canonical annual return used for prize pool growth. Venture gross return + scale compression + crowd allocation alpha + home bias elimination. This is the structural pool return before contingent macro feedback loops. > > **Inputs**: > > - [Venture Capital Gross Return](#sec-venture_gross_return) 📊: 17% (95% CI: 13% - 22%) > - [Scale Compression Factor](#sec-scale_compression_factor): -2.5% (95% CI: -5% - -1%) > - [Wishocratic Crowd Allocation Alpha](#sec-wishocratic_crowd_alpha): 0.5% (95% CI: 0% - 1.5%) > - [Home Bias Return Drag](#sec-home_bias_alpha) 📊: 0.8% (95% CI: 0.3% - 1.5%) > > $$ \begin{gathered} r_{pool} \\ = r_{VC,gross} + \Delta r_{scale} + \alpha_{crowd} \\ + \alpha_{home} \\ = 17\% + -2.5\% + 0.5\% + 0.8\% \\ = 15.8\% \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for PRIZE Pool Annual Return** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Venture Capital Gross Return (percent) | 0.9039 | Strong driver | > | Scale Compression Factor (percent) | 0.3932 | Moderate driver | > | Wishocratic Crowd Allocation Alpha (percent) | 0.1453 | Weak driver | > | Home Bias Return Drag (percent) | 0.1197 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: PRIZE Pool Annual Return (10,000 simulations) > > **Simulation Results Summary: PRIZE Pool Annual Return** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 15.8% | > | Mean (expected value) | 15.8% | > | Median (50th percentile) | 15.8% | > | Standard Deviation | 2.41% | > | 90% Range (5th-95th percentile) | \[11.8%, 19.8%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for PRIZE Pool Annual Return; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: PRIZE Pool Annual Return > > *This exceedance probability chart shows the likelihood that PRIZE Pool Annual Return will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### PRIZE Pool Horizon Multiple: 9.03x > **NOTE:** > > Compound multiple for prize pool growth over the resolution horizon (tied to the destructive economy 50% threshold year). > > **Inputs**: > > - [PRIZE Pool Annual Return](#sec-prize_pool_annual_return) 🔢: 15.8% > - [Year Destructive Economy Reaches 50% of GDP](#sec-destructive_economy_50pct_year) 🔢: 2040 > - [Destructive Economy Base Year](#sec-destructive_economy_base_year): 2025 > > $$ M_{pool} = (1 + r_{pool})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} r_{pool} \\ = r_{VC,gross} + \Delta r_{scale} + \alpha_{crowd} \\ + \alpha_{home} \\ = 17\% + -2.5\% + 0.5\% + 0.8\% \\ = 15.8\% \end{gathered} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for PRIZE Pool Horizon Multiple** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-----------------------------------|------------------------:|:---------------| > | PRIZE Pool Annual Return (percent) | 0.9831 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: PRIZE Pool Horizon Multiple (10,000 simulations) > > **Simulation Results Summary: PRIZE Pool Horizon Multiple** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 9.03x | > | Mean (expected value) | 9.49x | > | Median (50th percentile) | 9.04x | > | Standard Deviation | 2.99x | > | 90% Range (5th-95th percentile) | \[5.36x, 15.1x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for PRIZE Pool Horizon Multiple; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: PRIZE Pool Horizon Multiple > > *This exceedance probability chart shows the likelihood that PRIZE Pool Horizon Multiple will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### PRIZE Pool Retirement-Equivalent Principal: \$2.28 trillion > **NOTE:** > > Secondary PRIZE seed benchmark: initial principal required so that the pool can make two referred votes retirement-equivalent on success at the majority-of-humanity coordination target. This is a stronger-incentive visible-pool benchmark, not the minimum capital required to make majority-of-humanity participation credible. > > **Inputs**: > > - [Global Registered Voters](#sec-global_registered_voters) 📊: 4.13 billion of people > - [Retirement-Equivalent Claim Value Target](#sec-retirement_equivalent_claim_value_target) 🔢: \$4,991 > - [PRIZE Pool Horizon Multiple](#sec-prize_pool_horizon_multiple) 🔢: 9.03x > > $$ \begin{gathered} P_{retire-eq} \\ = N_{voters,global} \times \frac{V_{claim,target}}{M_{pool}} \end{gathered} $$ where: $$ \begin{gathered} V_{claim,target} \\ = V_{2claims,target} \times 0.5 \\ = \$9.98K \times 0.5 \\ = \$4.99K \end{gathered} $$ where: $$ \begin{gathered} V_{2claims,target} \\ = S_{annual,pc} \times M_{retire} \\ = \$3.88K \times 2.57 \\ = \$9.98K \end{gathered} $$ where: $$ \begin{gathered} S_{annual,pc} \\ = \frac{S_{annual}}{Pop_{global}} \\ = \frac{\$31.1T}{8B} \\ = \$3.88K \end{gathered} $$ where: $$ \begin{gathered} S_{annual} \\ = s_{global} \times GDP_{global} \\ = 27\% \times \$115T \\ = \$31.1T \end{gathered} $$ where: $$ M_{retire} = (1 + r_{retire})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ where: $$ M_{pool} = (1 + r_{pool})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} r_{pool} \\ = r_{VC,gross} + \Delta r_{scale} + \alpha_{crowd} \\ + \alpha_{home} \\ = 17\% + -2.5\% + 0.5\% + 0.8\% \\ = 15.8\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for PRIZE Pool Retirement-Equivalent Principal** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | PRIZE Pool Horizon Multiple (x) | -0.8658 | Strong driver | > | Retirement-Equivalent Claim Value Target (USD) | 0.3438 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: PRIZE Pool Retirement-Equivalent Principal (10,000 simulations) > > **Simulation Results Summary: PRIZE Pool Retirement-Equivalent Principal** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$2.28 trillion | > | Mean (expected value) | \$2.41 trillion | > | Median (50th percentile) | \$2.27 trillion | > | Standard Deviation | \$817 billion | > | 90% Range (5th-95th percentile) | \[\$1.32 trillion, \$3.96 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for PRIZE Pool Retirement-Equivalent Principal; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: PRIZE Pool Retirement-Equivalent Principal > > *This exceedance probability chart shows the likelihood that PRIZE Pool Retirement-Equivalent Principal will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Prize Pool Size: \$27.5 trillion > **NOTE:** > > Terminal prize pool size: global investable assets × participation rate × compound multiple over the resolution horizon. > > **Inputs**: > > - [Global Investable Financial Assets](#sec-global_investable_assets) 📊: \$305 trillion > - [Prize Pool Participation Rate](#sec-prize_pool_participation_rate): 1% (95% CI: 0.1% - 10%) > - [PRIZE Pool Horizon Multiple](#sec-prize_pool_horizon_multiple) 🔢: 9.03x > > $$ \begin{gathered} Pool \\ = Assets_{invest} \times R_{pool} \times M_{pool} \\ = \$305T \times 1\% \times 9.03 \\ = \$27.5T \end{gathered} $$ where: $$ M_{pool} = (1 + r_{pool})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} r_{pool} \\ = r_{VC,gross} + \Delta r_{scale} + \alpha_{crowd} \\ + \alpha_{home} \\ = 17\% + -2.5\% + 0.5\% + 0.8\% \\ = 15.8\% \end{gathered} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Prize Pool Size** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Prize Pool Participation Rate (percent) | 0.9395 | Strong driver | > | PRIZE Pool Horizon Multiple (x) | 0.1773 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Prize Pool Size (10,000 simulations) > > **Simulation Results Summary: Prize Pool Size** > > | Statistic | Value | > |:--------------------------------|------------------------------------:| > | Baseline (deterministic) | \$27.5 trillion | > | Mean (expected value) | \$26.9 trillion | > | Median (50th percentile) | \$10 trillion | > | Standard Deviation | \$47.8 trillion | > | 90% Range (5th-95th percentile) | \[\$2.19 trillion, \$109 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Prize Pool Size; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Prize Pool Size > > *This exceedance probability chart shows the likelihood that Prize Pool Size will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Prize Settlement Target: Global HALE (Year 15): 79.4 years > **NOTE:** > > The Earth Optimization Prize settlement target for global HALE at year 15. Set to the Treaty-trajectory projection (the achievable floor). The terminal general-welfare metric oracle compares measured global HALE against this value. > > **Inputs**: > > - [Treaty Projected HALE at Year 15](#sec-treaty_projected_hale_year_15) 🔢: 79.4 years > > $$ HALE^{*}_{15} = HALE_{treaty,15} = 79.4 = 79.4 $$ where: $$ \begin{gathered} HALE_{treaty,15} \\ = HALE_{0} + \Delta HALE_{treaty,15} \\ = 63.3 + 16.1 \\ = 79.4 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \Delta_{HALE} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Prize Settlement Target: Global HALE (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Projected HALE at Year 15 (years) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Prize Settlement Target: Global HALE (Year 15) (10,000 simulations) > > **Simulation Results Summary: Prize Settlement Target: Global HALE (Year 15)** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 79.4 | > | Mean (expected value) | 78.3 | > | Median (50th percentile) | 77 | > | Standard Deviation | 6.97 | > | 90% Range (5th-95th percentile) | \[70.2, 90.7\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Prize Settlement Target: Global HALE (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Prize Settlement Target: Global HALE (Year 15) > > *This exceedance probability chart shows the likelihood that Prize Settlement Target: Global HALE (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Prize Settlement Target: Median Income (Year 15): \$4,381 > **NOTE:** > > MODEL PROJECTION that informs the Prize settlement target for global median income at year 15 (Treaty-trajectory median). NOT the binding trigger: per the Target Lock clause in the protocol spec, a pool freezes its targets as literal constants in a signed Settlement Schedule before its first deposit, denominated as a multiple of the Referee’s baseline MEASURED value so target and measurement share identical units. This parameter is the rationale for the locked number, never its definition. > > **Inputs**: > > - [Median After-Tax Consumable Income, Treaty (Year 15)](#sec-treaty_trajectory_median_after_tax_income_year_15) 🔢: \$4,381 > > $$ \begin{gathered} \tilde{m}^{*}_{15} \\ = \tilde{m}_{treaty,15} \\ = \$4.38K \\ = \$4.38K \end{gathered} $$ where: $$ \begin{gathered} \tilde{m}_{treaty,15} \\ = \bar{y}_{treaty,15} \times (1 - s_{mil} \times (1 - s_{ratchet})) \times \rho_{med} \times (1 - e_{med})^{15} \times (1 \\ + r_{relief} \times f_{cure,15,treaty}) \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,15} \\ = \frac{GDP_{treaty,15}}{Pop_{2040}} \\ = \frac{\$238T}{8.9B} \\ = \$26.7K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,15} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,15} \\ + g_{peace,treaty,15} + g_{cyber,treaty,15} \\ + g_{health,treaty,15})^{15} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,15} \\ = \bar{s}_{treaty,15} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 4.4\% \times 5.5\% \times 2 \times 1.67 \\ = 0.807\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,15} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,15}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,15} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,15} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,15} \\ = \frac{f_{cure,15,treaty} - d_{disease}}{-7.22} \\ = \frac{100\% - 13\%}{-7.22} \\ = 0.818\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ where: $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Prize Settlement Target: Median Income (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Median After-Tax Consumable Income, Treaty (Year 15) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Prize Settlement Target: Median Income (Year 15) (10,000 simulations) > > **Simulation Results Summary: Prize Settlement Target: Median Income (Year 15)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$4,381 | > | Mean (expected value) | \$4,312 | > | Median (50th percentile) | \$4,291 | > | Standard Deviation | \$646 | > | 90% Range (5th-95th percentile) | \[\$3,292, \$5,411\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Prize Settlement Target: Median Income (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Prize Settlement Target: Median Income (Year 15) > > *This exceedance probability chart shows the likelihood that Prize Settlement Target: Median Income (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### RECOVERY Trial Cost Reduction Factor: 82x > **NOTE:** > > Cost reduction factor demonstrated by RECOVERY trial (traditional Phase 3 cost / RECOVERY cost per patient) > > **Inputs**: > > - [Phase 3 Cost per Patient](#sec-traditional_phase3_cost_per_patient) 📊: \$41,000 (95% CI: \$20,000 - \$120,000) > - [Recovery Trial Cost per Patient](#sec-recovery_trial_cost_per_patient) 📊: \$500 (95% CI: \$400 - \$2,500) > > $$ \begin{gathered} k_{RECOVERY} \\ = \frac{Cost_{P3,pt}}{Cost_{RECOVERY,pt}} \\ = \frac{\$41K}{\$500} \\ = 82 \end{gathered} $$ > > **Methodology**:^([8](../references.llms.md#ref-recovery-trial-82x-cost-reduction)) > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for RECOVERY Trial Cost Reduction Factor** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Phase 3 Cost per Patient (USD/patient) | 0.8407 | Strong driver | > | Recovery Trial Cost per Patient (USD/patient) | -0.4419 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: RECOVERY Trial Cost Reduction Factor (10,000 simulations) > > **Simulation Results Summary: RECOVERY Trial Cost Reduction Factor** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 82x | > | Mean (expected value) | 84.2x | > | Median (50th percentile) | 69.2x | > | Standard Deviation | 54.5x | > | 90% Range (5th-95th percentile) | \[21.4x, 195x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for RECOVERY Trial Cost Reduction Factor; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: RECOVERY Trial Cost Reduction Factor > > *This exceedance probability chart shows the likelihood that RECOVERY Trial Cost Reduction Factor will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### RECOVERY Trial Total QALYs Generated: 5 million QALYs > **NOTE:** > > Total QALYs generated by RECOVERY trial’s discoveries (lives saved × QALYs per life). Uses global impact methodology: counts all downstream health gains from the discovery. > > **Inputs**: > > - [RECOVERY Trial Global Lives Saved](#sec-recovery_trial_global_lives_saved) 📊: 1 million lives (95% CI: 500 thousand lives - 2 million lives) > - [QALYs per COVID Death Averted](#sec-qalys_per_covid_death_averted): 5 QALYs/death (95% CI: 3 QALYs/death - 10 QALYs/death) > > $$ \begin{gathered} QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for RECOVERY Trial Total QALYs Generated** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | RECOVERY Trial Global Lives Saved (lives) | 0.7055 | Strong driver | > | QALYs per COVID Death Averted (QALYs/death) | 0.6520 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: RECOVERY Trial Total QALYs Generated (10,000 simulations) > > **Simulation Results Summary: RECOVERY Trial Total QALYs Generated** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 5 million | > | Mean (expected value) | 4.97 million | > | Median (50th percentile) | 4.35 million | > | Standard Deviation | 2.53 million | > | 90% Range (5th-95th percentile) | \[2.1 million, 10.1 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for RECOVERY Trial Total QALYs Generated; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: RECOVERY Trial Total QALYs Generated > > *This exceedance probability chart shows the likelihood that RECOVERY Trial Total QALYs Generated will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Retirement-Equivalent 2-Claims Target Payout: \$9,982 > **NOTE:** > > Target success-side payout for two referred votes: what one representative annual savings contribution would become in a conventional retirement account by PRIZE resolution. > > **Inputs**: > > - [Global Annual Savings Per Capita](#sec-global_annual_savings_per_capita) 🔢: \$3,881 > - [Conventional Retirement Horizon Multiple](#sec-conventional_retirement_horizon_multiple) 🔢: 2.57x > > $$ \begin{gathered} V_{2claims,target} \\ = S_{annual,pc} \times M_{retire} \\ = \$3.88K \times 2.57 \\ = \$9.98K \end{gathered} $$ where: $$ \begin{gathered} S_{annual,pc} \\ = \frac{S_{annual}}{Pop_{global}} \\ = \frac{\$31.1T}{8B} \\ = \$3.88K \end{gathered} $$ where: $$ \begin{gathered} S_{annual} \\ = s_{global} \times GDP_{global} \\ = 27\% \times \$115T \\ = \$31.1T \end{gathered} $$ where: $$ M_{retire} = (1 + r_{retire})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Retirement-Equivalent 2-Claims Target Payout** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Conventional Retirement Horizon Multiple (x) | 0.8781 | Strong driver | > | Global Annual Savings Per Capita (USD/person/year) | 0.4719 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Retirement-Equivalent 2-Claims Target Payout (10,000 simulations) > > **Simulation Results Summary: Retirement-Equivalent 2-Claims Target Payout** > > | Statistic | Value | > |:--------------------------------|----------------------:| > | Baseline (deterministic) | \$9,982 | > | Mean (expected value) | \$10,037 | > | Median (50th percentile) | \$9,975 | > | Standard Deviation | \$1,178 | > | 90% Range (5th-95th percentile) | \[\$8,198, \$12,088\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Retirement-Equivalent 2-Claims Target Payout; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Retirement-Equivalent 2-Claims Target Payout > > *This exceedance probability chart shows the likelihood that Retirement-Equivalent 2-Claims Target Payout will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Retirement-Equivalent Claim Value Target: \$4,991 > **NOTE:** > > Target value of one referred-voter claim when two claims are meant to match the conventional-retirement future value of one representative annual savings contribution. > > **Inputs**: > > - [Retirement-Equivalent 2-Claims Target Payout](#sec-retirement_equivalent_2_claims_target_payout) 🔢: \$9,982 > > $$ \begin{gathered} V_{claim,target} \\ = V_{2claims,target} \times 0.5 \\ = \$9.98K \times 0.5 \\ = \$4.99K \end{gathered} $$ where: $$ \begin{gathered} V_{2claims,target} \\ = S_{annual,pc} \times M_{retire} \\ = \$3.88K \times 2.57 \\ = \$9.98K \end{gathered} $$ where: $$ \begin{gathered} S_{annual,pc} \\ = \frac{S_{annual}}{Pop_{global}} \\ = \frac{\$31.1T}{8B} \\ = \$3.88K \end{gathered} $$ where: $$ \begin{gathered} S_{annual} \\ = s_{global} \times GDP_{global} \\ = 27\% \times \$115T \\ = \$31.1T \end{gathered} $$ where: $$ M_{retire} = (1 + r_{retire})^{Y_{50\%} - Y_0} $$ where: $$ \begin{gathered} Y_{50\%} \\ = Y_0 \\ + \frac{\ln\left(0.50 / \text{DESTRUCTIVE\_PCT\_GDP}\right)}{\ln\left(1 + \text{DESTRUCTIVE\_GROWTH} - \text{GDP\_GROWTH}\right)} \end{gathered} $$ where: $$ \begin{gathered} r_{destruct:GDP} \\ = \frac{Cost_{destruct}}{GDP_{global}} \\ = \frac{\$13.2T}{\$115T} \\ = 11.5\% \end{gathered} $$ where: $$ \begin{gathered} Cost_{destruct} \\ = Spending_{mil} + Cost_{cyber} \\ = \$2.72T + \$10.5T \\ = \$13.2T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Retirement-Equivalent Claim Value Target** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Retirement-Equivalent 2-Claims Target Payout (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Retirement-Equivalent Claim Value Target (10,000 simulations) > > **Simulation Results Summary: Retirement-Equivalent Claim Value Target** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$4,991 | > | Mean (expected value) | \$5,018 | > | Median (50th percentile) | \$4,987 | > | Standard Deviation | \$589 | > | 90% Range (5th-95th percentile) | \[\$4,099, \$6,044\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Retirement-Equivalent Claim Value Target; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Retirement-Equivalent Claim Value Target > > *This exceedance probability chart shows the likelihood that Retirement-Equivalent Claim Value Target will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### SE Bot Annual Modeled Belief Changes: 4.38 million people/year > **NOTE:** > > Modeled annual belief updates from the outbound bot before deduplicating repeat exposures across posts. This is not a count of unique people. > > **Inputs**: > > - [Relevant Posts Per Day (Global)](#sec-se_bot_relevant_posts_per_day_global): 100 thousand posts/day (95% CI: 5,000 posts/day - 1 million posts/day) > - [People Persuaded Per Post](#sec-se_bot_people_persuaded_per_post) 🔢: 0.12 people/post > > $$ \begin{gathered} N_{belief,annual} \\ = V_{posts} \times N_{persuaded} \times 365 \\ = 100{,}000 \times 0.12 \times 365 \\ = 4.38M \end{gathered} $$ where: $$ \begin{gathered} N_{persuaded} \\ = P_{target} + M_{obs} \times P_{obs} \\ = 2\% + 20 \times 0.5\% \\ = 0.12 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for SE Bot Annual Modeled Belief Changes** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | People Persuaded Per Post (people/post) | 0.4973 | Moderate driver | > | Relevant Posts Per Day (Global) (posts/day) | 0.3007 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: SE Bot Annual Modeled Belief Changes (10,000 simulations) > > **Simulation Results Summary: SE Bot Annual Modeled Belief Changes** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | 4.38 million | > | Mean (expected value) | 3.93 million | > | Median (50th percentile) | 575 thousand | > | Standard Deviation | 21 million | > | 90% Range (5th-95th percentile) | \[32,254, 14.3 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for SE Bot Annual Modeled Belief Changes; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: SE Bot Annual Modeled Belief Changes > > *This exceedance probability chart shows the likelihood that SE Bot Annual Modeled Belief Changes will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Special Education Peace-Dividend Expected Value: \$11.4 million > **NOTE:** > > Conservative expected annual social value from outbound Special Education: attribution_fraction x annual peace dividend. This is a treaty-only fallback, not the universal-owner portfolio case. > > **Inputs**: > > - [Special Education Outcome Attribution Fraction](#sec-se_bot_outcome_attribution_fraction): 0.01% (95% CI: 0.0001% - 1%) > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > > $$ \begin{gathered} EV_{bot} \\ = \alpha_{bot} \times Benefit_{peace,soc} \\ = 0.01\% \times \$114B \\ = \$11.4M \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Special Education Peace-Dividend Expected Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Special Education Outcome Attribution Fraction (rate) | 0.9961 | Strong driver | > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 0.0148 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Special Education Peace-Dividend Expected Value (10,000 simulations) > > **Simulation Results Summary: Special Education Peace-Dividend Expected Value** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$11.4 million | > | Mean (expected value) | \$9.28 million | > | Median (50th percentile) | \$428,409 | > | Standard Deviation | \$57.6 million | > | 90% Range (5th-95th percentile) | \[\$104,608, \$29.2 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Special Education Peace-Dividend Expected Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Special Education Peace-Dividend Expected Value > > *This exceedance probability chart shows the likelihood that Special Education Peace-Dividend Expected Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### SE Bot Annual Operational Cost: \$292,000 > **NOTE:** > > Annual cost to correct all relevant posts globally: cost_per_post \* posts_per_day \* days_per_year. > > **Inputs**: > > - [SE Bot Cost Per Post](#sec-se_bot_cost_per_post_usd) 🔢: \$0.008 > - [Relevant Posts Per Day (Global)](#sec-se_bot_relevant_posts_per_day_global): 100 thousand posts/day (95% CI: 5,000 posts/day - 1 million posts/day) > > $$ \begin{gathered} C_{annual} \\ = C_{post} \times V_{posts} \times 365 \\ = \$0.008 \times 100{,}000 \times 365 \\ = \$292K \end{gathered} $$ where: $$ \begin{gathered} C_{post} \\ = C_{llm} + C_{platform} \\ = \$0.006 + \$0.002 \\ = \$0.008 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for SE Bot Annual Operational Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Relevant Posts Per Day (Global) (posts/day) | 0.7242 | Strong driver | > | SE Bot Cost Per Post (USD) | 0.3512 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: SE Bot Annual Operational Cost (10,000 simulations) > > **Simulation Results Summary: SE Bot Annual Operational Cost** > > | Statistic | Value | > |:--------------------------------|----------------------------:| > | Baseline (deterministic) | \$292,000 | > | Mean (expected value) | \$273,961 | > | Median (50th percentile) | \$83,425 | > | Standard Deviation | \$644,394 | > | 90% Range (5th-95th percentile) | \[\$8,608, \$1.15 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for SE Bot Annual Operational Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: SE Bot Annual Operational Cost > > *This exceedance probability chart shows the likelihood that SE Bot Annual Operational Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### \$1T AUM Universal-Owner Breakeven Attribution Fraction: 0.000166% > **NOTE:** > > Political dysfunction attribution fraction required for a \$1T universal-owner investor’s portfolio gain to cover annual Special Education operating cost. > > **Inputs**: > > - [SE Bot Annual Operational Cost](#sec-se_bot_annual_operational_cost_usd) 🔢: \$292,000 > - [Global Listed Equity Market Capitalization](#sec-se_bot_global_equity_market_cap_usd) 📊: \$115 trillion (95% CI: \$90 trillion - \$140 trillion) > - [Global Opportunity Cost Total](#sec-political_dysfunction_global_opportunity_cost_total) 🔢: \$101 trillion > - [Equity Capture Share of Public Value](#sec-se_bot_equity_uplift_capture_fraction): 20% (95% CI: 5% - 40%) > - [Reference AUM for SE Bot Portfolio Case](#sec-se_bot_reference_aum_usd): \$1 trillion > > $$ \begin{gathered} \alpha_{breakeven,UO} \\ = C_{annual} \times M_{equity} / (O_{total} \times f_{equity} \times AUM_{ref}) \end{gathered} $$ where: $$ \begin{gathered} C_{annual} \\ = C_{post} \times V_{posts} \times 365 \\ = \$0.008 \times 100{,}000 \times 365 \\ = \$292K \end{gathered} $$ where: $$ \begin{gathered} C_{post} \\ = C_{llm} + C_{platform} \\ = \$0.006 + \$0.002 \\ = \$0.008 \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for \$1T AUM Universal-Owner Breakeven Attribution Fraction** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | SE Bot Annual Operational Cost (USD/year) | 0.8127 | Strong driver | > | Equity Capture Share of Public Value (percentage) | -0.1575 | Weak driver | > | Global Opportunity Cost Total (USD) | -0.0960 | Minimal effect | > | Global Listed Equity Market Capitalization (USD) | 0.0324 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: \$1T AUM Universal-Owner Breakeven Attribution Fraction (10,000 simulations) > > **Simulation Results Summary: \$1T AUM Universal-Owner Breakeven Attribution Fraction** > > | Statistic | Value | > |:--------------------------------|------------------------:| > | Baseline (deterministic) | 0.000166% | > | Mean (expected value) | 0.000213% | > | Median (50th percentile) | 5.54e-05% | > | Standard Deviation | 0.000607% | > | 90% Range (5th-95th percentile) | \[4.7e-06%, 0.000877%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for \$1T AUM Universal-Owner Breakeven Attribution Fraction; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: \$1T AUM Universal-Owner Breakeven Attribution Fraction > > *This exceedance probability chart shows the likelihood that \$1T AUM Universal-Owner Breakeven Attribution Fraction will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### \$1T AUM Universal-Owner Portfolio Gain from Special Education: \$17.6 million > **NOTE:** > > Expected portfolio gain for a \$1T universal-owner fund from one year of outbound Special Education. Calculation: attribution_fraction x political_dysfunction_tax x equity_capture_share / global_equity_market_cap x reference_AUM. > > **Inputs**: > > - [Special Education Outcome Attribution Fraction](#sec-se_bot_outcome_attribution_fraction): 0.01% (95% CI: 0.0001% - 1%) > - [Global Opportunity Cost Total](#sec-political_dysfunction_global_opportunity_cost_total) 🔢: \$101 trillion > - [Equity Capture Share of Public Value](#sec-se_bot_equity_uplift_capture_fraction): 20% (95% CI: 5% - 40%) > - [Global Listed Equity Market Capitalization](#sec-se_bot_global_equity_market_cap_usd) 📊: \$115 trillion (95% CI: \$90 trillion - \$140 trillion) > - [Reference AUM for SE Bot Portfolio Case](#sec-se_bot_reference_aum_usd): \$1 trillion > > $$ \begin{gathered} G_{UO,1T} \\ = \alpha_{bot} \times O_{total} \times \frac{f_{equity}}{M_{equity}} \times AUM_{ref} \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for \$1T AUM Universal-Owner Portfolio Gain from Special Education** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Special Education Outcome Attribution Fraction (rate) | 0.8704 | Strong driver | > | Equity Capture Share of Public Value (percentage) | 0.0517 | Minimal effect | > | Global Opportunity Cost Total (USD) | 0.0469 | Minimal effect | > | Global Listed Equity Market Capitalization (USD) | -0.0166 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: \$1T AUM Universal-Owner Portfolio Gain from Special Education (10,000 simulations) > > **Simulation Results Summary: \$1T AUM Universal-Owner Portfolio Gain from Special Education** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | \$17.6 million | > | Mean (expected value) | \$15.7 million | > | Median (50th percentile) | \$591,637 | > | Standard Deviation | \$120 million | > | 90% Range (5th-95th percentile) | \[\$81,721, \$40.9 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for \$1T AUM Universal-Owner Portfolio Gain from Special Education; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: \$1T AUM Universal-Owner Portfolio Gain from Special Education > > *This exceedance probability chart shows the likelihood that \$1T AUM Universal-Owner Portfolio Gain from Special Education will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### \$1T AUM Universal-Owner Portfolio ROI from Special Education: 60.2:1 > **NOTE:** > > Portfolio-only ROI for a \$1T universal-owner investor: expected portfolio gain from political dysfunction tax recovery divided by annual Special Education operating cost. > > **Inputs**: > > - [\$1T AUM Universal-Owner Portfolio Gain from Special Education](#sec-se_bot_aum_1t_universal_owner_portfolio_gain_usd) 🔢: \$17.6 million > - [SE Bot Annual Operational Cost](#sec-se_bot_annual_operational_cost_usd) 🔢: \$292,000 > > $$ \begin{gathered} ROI_{UO,1T} \\ = \frac{G_{UO,1T}}{C_{annual}} \\ = \frac{\$17.6M}{\$292K} \\ = 60.2 \end{gathered} $$ where: $$ \begin{gathered} G_{UO,1T} \\ = \alpha_{bot} \times O_{total} \times \frac{f_{equity}}{M_{equity}} \times AUM_{ref} \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ where: $$ \begin{gathered} C_{annual} \\ = C_{post} \times V_{posts} \times 365 \\ = \$0.008 \times 100{,}000 \times 365 \\ = \$292K \end{gathered} $$ where: $$ \begin{gathered} C_{post} \\ = C_{llm} + C_{platform} \\ = \$0.006 + \$0.002 \\ = \$0.008 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for \$1T AUM Universal-Owner Portfolio ROI from Special Education** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | \$1T AUM Universal-Owner Portfolio Gain from Special Education (USD) | 0.7621 | Strong driver | > | SE Bot Annual Operational Cost (USD/year) | -0.0240 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: \$1T AUM Universal-Owner Portfolio ROI from Special Education (10,000 simulations) > > **Simulation Results Summary: \$1T AUM Universal-Owner Portfolio ROI from Special Education** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 60.2:1 | > | Mean (expected value) | 464:1 | > | Median (50th percentile) | 8.56:1 | > | Standard Deviation | 6,386:1 | > | 90% Range (5th-95th percentile) | \[0.25:1, 934:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for \$1T AUM Universal-Owner Portfolio ROI from Special Education; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: \$1T AUM Universal-Owner Portfolio ROI from Special Education > > *This exceedance probability chart shows the likelihood that \$1T AUM Universal-Owner Portfolio ROI from Special Education will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### SE Bot Cost Per Belief Change: \$0.067 > **NOTE:** > > Cost per person whose belief is durably updated: cost_per_post / people_persuaded_per_post. > > **Inputs**: > > - [SE Bot Cost Per Post](#sec-se_bot_cost_per_post_usd) 🔢: \$0.008 > - [People Persuaded Per Post](#sec-se_bot_people_persuaded_per_post) 🔢: 0.12 people/post > > $$ \begin{gathered} C_{belief} \\ = \frac{C_{post}}{N_{persuaded}} \\ = \frac{\$0.008}{0.12} \\ = \$0.0667 \end{gathered} $$ where: $$ \begin{gathered} C_{post} \\ = C_{llm} + C_{platform} \\ = \$0.006 + \$0.002 \\ = \$0.008 \end{gathered} $$ where: $$ \begin{gathered} N_{persuaded} \\ = P_{target} + M_{obs} \times P_{obs} \\ = 2\% + 20 \times 0.5\% \\ = 0.12 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for SE Bot Cost Per Belief Change** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | SE Bot Cost Per Post (USD) | 0.4281 | Moderate driver | > | People Persuaded Per Post (people/post) | -0.1725 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: SE Bot Cost Per Belief Change (10,000 simulations) > > **Simulation Results Summary: SE Bot Cost Per Belief Change** > > | Statistic | Value | > |:--------------------------------|--------------------:| > | Baseline (deterministic) | \$0.067 | > | Mean (expected value) | \$0.369 | > | Median (50th percentile) | \$0.149 | > | Standard Deviation | \$0.679 | > | 90% Range (5th-95th percentile) | \[\$0.012, \$1.44\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for SE Bot Cost Per Belief Change; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: SE Bot Cost Per Belief Change > > *This exceedance probability chart shows the likelihood that SE Bot Cost Per Belief Change will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### SE Bot Cost Per Post: \$0.008 > **NOTE:** > > Total marginal cost to generate, screen, and post one correction reply. > > **Inputs**: > > - [SE Bot LLM Cost Per Post](#sec-se_bot_llm_cost_per_post_usd) 📊: \$0.006 (95% CI: \$0.002 - \$0.03) > - [SE Bot Platform Overhead Per Post](#sec-se_bot_platform_overhead_per_post_usd): \$0.002 (95% CI: \$0.0005 - \$0.02) > > $$ \begin{gathered} C_{post} \\ = C_{llm} + C_{platform} \\ = \$0.006 + \$0.002 \\ = \$0.008 \end{gathered} $$ > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for SE Bot Cost Per Post** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | SE Bot LLM Cost Per Post (USD) | 0.8864 | Strong driver | > | SE Bot Platform Overhead Per Post (USD) | 0.4695 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: SE Bot Cost Per Post (10,000 simulations) > > **Simulation Results Summary: SE Bot Cost Per Post** > > | Statistic | Value | > |:--------------------------------|----------------------:| > | Baseline (deterministic) | \$0.008 | > | Mean (expected value) | \$0.00796 | > | Median (50th percentile) | \$0.0057 | > | Standard Deviation | \$0.00649 | > | 90% Range (5th-95th percentile) | \[\$0.0025, \$0.023\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for SE Bot Cost Per Post; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: SE Bot Cost Per Post > > *This exceedance probability chart shows the likelihood that SE Bot Cost Per Post will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### People Persuaded Per Post: 0.12 people/post > **NOTE:** > > Expected number of people whose belief is durably updated per correction post: (1 target \* target_change_rate) + (observer_multiplier \* observer_change_rate). This counts modeled belief updates, not unique humans across the whole campaign. > > **Inputs**: > > - [Target Belief Change Rate](#sec-se_bot_target_belief_change_rate): 2% (95% CI: 0.2% - 10%) > - [Observer Multiplier](#sec-se_bot_observer_multiplier): 20 people per post (95% CI: 2 people per post - 200 people per post) > - [Observer Belief Change Rate](#sec-se_bot_observer_belief_change_rate): 0.5% (95% CI: 0.05% - 3%) > > $$ \begin{gathered} N_{persuaded} \\ = P_{target} + M_{obs} \times P_{obs} \\ = 2\% + 20 \times 0.5\% \\ = 0.12 \end{gathered} $$ > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for People Persuaded Per Post** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Observer Multiplier (people per post) | 0.5411 | Strong driver | > | Observer Belief Change Rate (rate) | 0.4356 | Moderate driver | > | Target Belief Change Rate (rate) | 0.0805 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: People Persuaded Per Post (10,000 simulations) > > **Simulation Results Summary: People Persuaded Per Post** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.12 | > | Mean (expected value) | 0.116 | > | Median (50th percentile) | 0.0419 | > | Standard Deviation | 0.304 | > | 90% Range (5th-95th percentile) | \[0.00554, 0.437\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for People Persuaded Per Post; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: People Persuaded Per Post > > *This exceedance probability chart shows the likelihood that People Persuaded Per Post will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Special Education Political Dysfunction Expected Social Value: \$10.1 billion > **NOTE:** > > Expected annual social value from outbound Special Education under the political dysfunction tax framing: attribution_fraction x annual global opportunity cost from governance failures. This is the broad public-value case, not a direct portfolio return. > > **Inputs**: > > - [Special Education Outcome Attribution Fraction](#sec-se_bot_outcome_attribution_fraction): 0.01% (95% CI: 0.0001% - 1%) > - [Global Opportunity Cost Total](#sec-political_dysfunction_global_opportunity_cost_total) 🔢: \$101 trillion > > $$ \begin{gathered} EV_{SE,PDT} \\ = \alpha_{bot} \times O_{total} \\ = 0.01\% \times \$101T \\ = \$10.1B \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Special Education Political Dysfunction Expected Social Value** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Special Education Outcome Attribution Fraction (rate) | 0.9363 | Strong driver | > | Global Opportunity Cost Total (USD) | 0.0558 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Special Education Political Dysfunction Expected Social Value (10,000 simulations) > > **Simulation Results Summary: Special Education Political Dysfunction Expected Social Value** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$10.1 billion | > | Mean (expected value) | \$8.58 billion | > | Median (50th percentile) | \$371 million | > | Standard Deviation | \$58.9 billion | > | 90% Range (5th-95th percentile) | \[\$71 million, \$25.4 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Special Education Political Dysfunction Expected Social Value; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Special Education Political Dysfunction Expected Social Value > > *This exceedance probability chart shows the likelihood that Special Education Political Dysfunction Expected Social Value will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Special Education Political Dysfunction Social ROI: 34.6 thousand:1 > **NOTE:** > > Broad social ROI for outbound Special Education: political_dysfunction_annual_EV / annual_operational_cost. This is useful as a social-value ceiling, while the universal-owner ROI estimates the investable portfolio case. > > **Inputs**: > > - [Special Education Political Dysfunction Expected Social Value](#sec-se_bot_political_dysfunction_annual_ev_usd) 🔢: \$10.1 billion > - [SE Bot Annual Operational Cost](#sec-se_bot_annual_operational_cost_usd) 🔢: \$292,000 > > $$ \begin{gathered} ROI_{SE,PDT} \\ = \frac{EV_{SE,PDT}}{C_{annual}} \\ = \frac{\$10.1B}{\$292K} \\ = 34{,}600 \end{gathered} $$ where: $$ \begin{gathered} EV_{SE,PDT} \\ = \alpha_{bot} \times O_{total} \\ = 0.01\% \times \$101T \\ = \$10.1B \end{gathered} $$ where: $$ \begin{gathered} O_{total} \\ = O_{health} + O_{science} + O_{lead} + O_{migration} \\ = \$34T + \$4T + \$6T + \$57T \\ = \$101T \end{gathered} $$ where: $$ \begin{gathered} C_{annual} \\ = C_{post} \times V_{posts} \times 365 \\ = \$0.008 \times 100{,}000 \times 365 \\ = \$292K \end{gathered} $$ where: $$ \begin{gathered} C_{post} \\ = C_{llm} + C_{platform} \\ = \$0.006 + \$0.002 \\ = \$0.008 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Special Education Political Dysfunction Social ROI** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Special Education Political Dysfunction Expected Social Value (USD/year) | 0.6404 | Strong driver | > | SE Bot Annual Operational Cost (USD/year) | -0.0334 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Special Education Political Dysfunction Social ROI (10,000 simulations) > > **Simulation Results Summary: Special Education Political Dysfunction Social ROI** > > | Statistic | Value | > |:--------------------------------|--------------------------:| > | Baseline (deterministic) | 34.6 thousand:1 | > | Mean (expected value) | 237 thousand:1 | > | Median (50th percentile) | 5,472:1 | > | Standard Deviation | 2.67 million:1 | > | 90% Range (5th-95th percentile) | \[170:1, 536 thousand:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Special Education Political Dysfunction Social ROI; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Special Education Political Dysfunction Social ROI > > *This exceedance probability chart shows the likelihood that Special Education Political Dysfunction Social ROI will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Special Education Peace-Dividend Social ROI: 38.9:1 > **NOTE:** > > Conservative treaty-only social ROI for outbound Special Education: peace_dividend_annual_EV / annual_operational_cost. This excludes broader political dysfunction tax recovery and universal-owner portfolio gains. > > **Inputs**: > > - [Special Education Peace-Dividend Expected Value](#sec-se_bot_annual_ev_usd) 🔢: \$11.4 million > - [SE Bot Annual Operational Cost](#sec-se_bot_annual_operational_cost_usd) 🔢: \$292,000 > > $$ \begin{gathered} ROI_{bot} \\ = \frac{EV_{bot}}{C_{annual}} \\ = \frac{\$11.4M}{\$292K} \\ = 38.9 \end{gathered} $$ where: $$ \begin{gathered} EV_{bot} \\ = \alpha_{bot} \times Benefit_{peace,soc} \\ = 0.01\% \times \$114B \\ = \$11.4M \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} C_{annual} \\ = C_{post} \times V_{posts} \times 365 \\ = \$0.008 \times 100{,}000 \times 365 \\ = \$292K \end{gathered} $$ where: $$ \begin{gathered} C_{post} \\ = C_{llm} + C_{platform} \\ = \$0.006 + \$0.002 \\ = \$0.008 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Special Education Peace-Dividend Social ROI** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Special Education Peace-Dividend Expected Value (USD/year) | 0.6255 | Strong driver | > | SE Bot Annual Operational Cost (USD/year) | -0.0405 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Special Education Peace-Dividend Social ROI (10,000 simulations) > > **Simulation Results Summary: Special Education Peace-Dividend Social ROI** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 38.9:1 | > | Mean (expected value) | 251:1 | > | Median (50th percentile) | 6.34:1 | > | Standard Deviation | 2,314:1 | > | 90% Range (5th-95th percentile) | \[0.207:1, 607:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Special Education Peace-Dividend Social ROI; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Special Education Peace-Dividend Social ROI > > *This exceedance probability chart shows the likelihood that Special Education Peace-Dividend Social ROI will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Sharing Breakeven (1 in N): 8.66 million:1 > **NOTE:** > > Breakeven probability expressed as ‘1 in N’. Forwarding has positive expected value if you believe there is at least a 1-in-N chance the plan works. For context, lightning strike odds are ~1 in 1.2 million. > > **Inputs**: > > - [Sharing Breakeven Probability](#sec-sharing_breakeven_probability_treaty) 🔢: 1 in 8.66 million > > $$ N_{breakeven} = P_{breakeven} = 1\text{ in }8.66M = 8.66M $$ where: $$ \begin{gathered} P_{breakeven} \\ = \frac{C_{share}}{\Delta Y_{lifetime,treaty}} \\ = \frac{\$0.0599}{\$519K} \\ = 1\text{ in }8.66M \end{gathered} $$ where: $$ \begin{gathered} C_{share} \\ = t_{share} \times \bar{w}_{hour} \times 0.0167 \\ = 0.5 \times \$7.19 \times 0.0167 \\ = \$0.0599 \end{gathered} $$ where: $$ \begin{gathered} \bar{w}_{hour} \\ = \frac{\bar{y}_{0}}{H_{work}} \\ = \frac{\$14.4K}{2{,}000} \\ = \$7.19 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Sharing Breakeven (1 in N)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Sharing Breakeven Probability (probability) | -0.7470 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Sharing Breakeven (1 in N) (10,000 simulations) > > **Simulation Results Summary: Sharing Breakeven (1 in N)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | 8.66 million:1 | > | Mean (expected value) | 8.88 million:1 | > | Median (50th percentile) | 8.7 million:1 | > | Standard Deviation | 3.18 million:1 | > | 90% Range (5th-95th percentile) | \[3.71 million:1, 14.4 million:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Sharing Breakeven (1 in N); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Sharing Breakeven (1 in N) > > *This exceedance probability chart shows the likelihood that Sharing Breakeven (1 in N) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Sharing Breakeven Probability: 1 in 8.66 million > **NOTE:** > > Minimum probability that the plan works for forwarding to have positive expected value. EV \> 0 when P(works) \> cost_of_sharing / gain_if_works. Below this probability, not forwarding is rational. Above it, forwarding dominates. For context, the odds of being struck by lightning are ~1 in 1.2 million. > > **Inputs**: > > - [Sharing Opportunity Cost](#sec-sharing_opportunity_cost) 🔢: \$0.06 > - [Treaty Trajectory Lifetime Income Gain (Per Capita)](#sec-treaty_trajectory_lifetime_income_gain_per_capita) 🔢: \$518,879 > > $$ \begin{gathered} P_{breakeven} \\ = \frac{C_{share}}{\Delta Y_{lifetime,treaty}} \\ = \frac{\$0.0599}{\$519K} \\ = 1\text{ in }8.66M \end{gathered} $$ where: $$ \begin{gathered} C_{share} \\ = t_{share} \times \bar{w}_{hour} \times 0.0167 \\ = 0.5 \times \$7.19 \times 0.0167 \\ = \$0.0599 \end{gathered} $$ where: $$ \begin{gathered} \bar{w}_{hour} \\ = \frac{\bar{y}_{0}}{H_{work}} \\ = \frac{\$14.4K}{2{,}000} \\ = \$7.19 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Sharing Breakeven Probability** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Lifetime Income Gain (Per Capita) (USD) | -0.7472 | Strong driver | > | Sharing Opportunity Cost (USD) | 0.0172 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Sharing Breakeven Probability (10,000 simulations) > > **Simulation Results Summary: Sharing Breakeven Probability** > > | Statistic | Value | > |:--------------------------------|-----------------------------------------:| > | Baseline (deterministic) | 1 in 8.66 million | > | Mean (expected value) | 1 in 7.33 million | > | Median (50th percentile) | 1 in 8.7 million | > | Standard Deviation | 1 in 11.2 million | > | 90% Range (5th-95th percentile) | \[1 in 14.4 million, 1 in 3.71 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Sharing Breakeven Probability; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Sharing Breakeven Probability > > *This exceedance probability chart shows the likelihood that Sharing Breakeven Probability will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Sharing Opportunity Cost: \$0.06 > **NOTE:** > > Dollar cost of 30 seconds at global average hourly income. The maximum downside of forwarding the message if the plan is impossible. > > **Inputs**: > > - [Sharing Time](#sec-sharing_time_minutes): 0.5 minutes > - [Global Average Hourly Income](#sec-global_avg_hourly_income) 🔢: \$7.19 > > $$ \begin{gathered} C_{share} \\ = t_{share} \times \bar{w}_{hour} \times 0.0167 \\ = 0.5 \times \$7.19 \times 0.0167 \\ = \$0.0599 \end{gathered} $$ where: $$ \begin{gathered} \bar{w}_{hour} \\ = \frac{\bar{y}_{0}}{H_{work}} \\ = \frac{\$14.4K}{2{,}000} \\ = \$7.19 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Sharing Opportunity Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Average Hourly Income (USD/hour) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Sharing Opportunity Cost (10,000 simulations) > > **Simulation Results Summary: Sharing Opportunity Cost** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$0.06 | > | Mean (expected value) | \$0.06 | > | Median (50th percentile) | \$0.06 | > | Standard Deviation | \$0.000728 | > | 90% Range (5th-95th percentile) | \[\$0.059, \$0.061\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Sharing Opportunity Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Sharing Opportunity Cost > > *This exceedance probability chart shows the likelihood that Sharing Opportunity Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Sharing Upside/Downside Ratio: 8.7Mx > **NOTE:** > > Raw ratio of upside (lifetime income gain if plan works) to downside (cost of sharing if plan is impossible). Not expected value; see SHARING_BREAKEVEN_PROBABILITY_TREATY for the probability threshold that makes forwarding rational. > > **Inputs**: > > - [Treaty Trajectory Lifetime Income Gain (Per Capita)](#sec-treaty_trajectory_lifetime_income_gain_per_capita) 🔢: \$518,879 > - [Sharing Opportunity Cost](#sec-sharing_opportunity_cost) 🔢: \$0.06 > > $$ \begin{gathered} k_{upside:downside} \\ = \frac{\Delta Y_{lifetime,treaty}}{C_{share}} \\ = \frac{\$519K}{\$0.0599} \\ = 8.66M \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} C_{share} \\ = t_{share} \times \bar{w}_{hour} \times 0.0167 \\ = 0.5 \times \$7.19 \times 0.0167 \\ = \$0.0599 \end{gathered} $$ where: $$ \begin{gathered} \bar{w}_{hour} \\ = \frac{\bar{y}_{0}}{H_{work}} \\ = \frac{\$14.4K}{2{,}000} \\ = \$7.19 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Sharing Upside/Downside Ratio** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Lifetime Income Gain (Per Capita) (USD) | 0.9992 | Strong driver | > | Sharing Opportunity Cost (USD) | -0.0339 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Sharing Upside/Downside Ratio (10,000 simulations) > > **Simulation Results Summary: Sharing Upside/Downside Ratio** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 8.7Mx | > | Mean (expected value) | 8.9Mx | > | Median (50th percentile) | 8.7Mx | > | Standard Deviation | 3.2Mx | > | 90% Range (5th-95th percentile) | \[3.7Mx, 14.4Mx\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Sharing Upside/Downside Ratio; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Sharing Upside/Downside Ratio > > *This exceedance probability chart shows the likelihood that Sharing Upside/Downside Ratio will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Shirt-Induced Laughs Gained: 3.51 quadrillion laughs > **NOTE:** > > Conservative first-order count of additional laughs across human history attributable to the shirt-triggered cascade. Computed as DALYs averted (healthy life-years restored by disease eradication) multiplied by laughs per healthy life-year. Does not count second-order laughs in future generations of human and post-human civilization whose existence is contingent on cascade triggering. > > **Inputs**: > > - [Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_dalys) 🔢: 565 billion DALYs > - [Human Laughs per Healthy Life-Year](#sec-human_laughs_per_healthy_life_year) 🔢: 6,205 laughs > > $$ \begin{gathered} L_{shirt} \\ = DALYs_{max} \times L_{year} \\ = 565B \times 6{,}200 \\ = 3510T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ L_{year} = L_{day} \times 365 = 17 \times 365 = 6{,}200 $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Shirt-Induced Laughs Gained** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Human Laughs per Healthy Life-Year (laughs) | 0.8124 | Strong driver | > | Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | 0.4948 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Shirt-Induced Laughs Gained (10,000 simulations) > > **Simulation Results Summary: Shirt-Induced Laughs Gained** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | 3.51 quadrillion | > | Mean (expected value) | 3.9 quadrillion | > | Median (50th percentile) | 3.07 quadrillion | > | Standard Deviation | 2.92 quadrillion | > | 90% Range (5th-95th percentile) | \[971 trillion, 9.71 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Shirt-Induced Laughs Gained; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Shirt-Induced Laughs Gained > > *This exceedance probability chart shows the likelihood that Shirt-Induced Laughs Gained will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Shirt Program Expected Value per Dollar: 37.6 million:1 > **NOTE:** > > Probability-weighted expected value per foundation-escrow dollar: treaty value multiplied by the cascade probability given seed, divided by the seed-program escrow. The defensible expected-value pitch for a skeptical foundation officer who does not want to bet on the headline ROI. > > **Inputs**: > > - [Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_economic_value) 🔢: \$7.53 quadrillion > - [Shirt Cascade Probability Given Seed](#sec-shirt_cascade_probability_given_seed): 25% (95% CI: 5% - 60%) > - [Shirt Seed Program Total Cost](#sec-shirt_seed_program_total_usd) 🔢: \$50 million > > $$ \begin{gathered} EV_{shirt} \\ = (Value_{max} \times P_{cascade,shirt}) / C_{seed,total} \end{gathered} $$ where: $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} C_{seed,total} \\ = N_{seed,shirt} \times C_{seed,wearer} \\ = 1M \times \$50 \\ = \$50M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Shirt Program Expected Value per Dollar** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Shirt Cascade Probability Given Seed (rate) | 0.3126 | Moderate driver | > | Shirt Seed Program Total Cost (USD) | -0.2652 | Weak driver | > | Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.2086 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Shirt Program Expected Value per Dollar (10,000 simulations) > > **Simulation Results Summary: Shirt Program Expected Value per Dollar** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | 37.6 million:1 | > | Mean (expected value) | 168 million:1 | > | Median (50th percentile) | 67.7 million:1 | > | Standard Deviation | 306 million:1 | > | 90% Range (5th-95th percentile) | \[5.66 million:1, 676 million:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Shirt Program Expected Value per Dollar; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Shirt Program Expected Value per Dollar > > *This exceedance probability chart shows the likelihood that Shirt Program Expected Value per Dollar will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Shirt Program ROI Ratio: 151 million:1 > **NOTE:** > > Unconditional ROI ratio: treaty-trajectory total economic value divided by the seed-program escrow. Headline ‘X-to-one’ framing for foundations. Does NOT discount for cascade probability; see SHIRT_PROGRAM_EXPECTED_VALUE_PER_DOLLAR for the probability-weighted view. > > **Inputs**: > > - [Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_economic_value) 🔢: \$7.53 quadrillion > - [Shirt Seed Program Total Cost](#sec-shirt_seed_program_total_usd) 🔢: \$50 million > > $$ \begin{gathered} ROI_{shirt} \\ = \frac{Value_{max}}{C_{seed,total}} \\ = \frac{\$7530T}{\$50M} \\ = 151M \end{gathered} $$ where: $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} C_{seed,total} \\ = N_{seed,shirt} \times C_{seed,wearer} \\ = 1M \times \$50 \\ = \$50M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Shirt Program ROI Ratio** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Shirt Seed Program Total Cost (USD) | -0.3152 | Moderate driver | > | Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.2500 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Shirt Program ROI Ratio (10,000 simulations) > > **Simulation Results Summary: Shirt Program ROI Ratio** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | 151 million:1 | > | Mean (expected value) | 676 million:1 | > | Median (50th percentile) | 313 million:1 | > | Standard Deviation | 1028 million:1 | > | 90% Range (5th-95th percentile) | \[35.6 million:1, 2551 million:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Shirt Program ROI Ratio; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Shirt Program ROI Ratio > > *This exceedance probability chart shows the likelihood that Shirt Program ROI Ratio will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Shirt Seed Program Total Cost: \$50 million > **NOTE:** > > Total foundation escrow required to fund the seed-wearer program: threshold of visible humans multiplied by blended cost per wearer. Held in Earth Optimization Prize assurance contract; refunded at structural EOP return rate if neither treaty passage nor target hit. > > **Inputs**: > > - [Shirt Seed Wearers Threshold](#sec-shirt_seed_wearers_threshold): 1 million of people (95% CI: 100 thousand of people - 5 million of people) > - [Shirt Seed Cost per Wearer](#sec-shirt_seed_cost_per_wearer_usd): \$50 (95% CI: \$10 - \$200) > > $$ \begin{gathered} C_{seed,total} \\ = N_{seed,shirt} \times C_{seed,wearer} \\ = 1M \times \$50 \\ = \$50M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Shirt Seed Program Total Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Shirt Seed Wearers Threshold (of people) | 0.6454 | Strong driver | > | Shirt Seed Cost per Wearer (USD) | 0.5351 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Shirt Seed Program Total Cost (10,000 simulations) > > **Simulation Results Summary: Shirt Seed Program Total Cost** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$50 million | > | Mean (expected value) | \$47.2 million | > | Median (50th percentile) | \$23.3 million | > | Standard Deviation | \$73.7 million | > | 90% Range (5th-95th percentile) | \[\$3.19 million, \$168 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Shirt Seed Program Total Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Shirt Seed Program Total Cost > > *This exceedance probability chart shows the likelihood that Shirt Seed Program Total Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Shirt Value per Wearer: \$940,697 > **NOTE:** > > Treaty-trajectory economic value per shirt-wearing human: total treaty value (DFDA trial capacity plus efficacy lag elimination) divided by the 8B-human target wearer base. This is the headline framing for the foundation pitch: each marginal wearer carries this much unrealized treaty value. Computationally identical to CORPORATE_DAMAGES_FORWARD_SETTLEMENT_VALUE_PER_CAPITA under a different semantic frame. > > **Inputs**: > > - [Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_economic_value) 🔢: \$7.53 quadrillion > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} V_{wearer} \\ = \frac{Value_{max}}{Pop_{global}} \\ = \frac{\$7530T}{8B} \\ = \$941K \end{gathered} $$ where: $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Shirt Value per Wearer** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.9997 | Strong driver | > | Global Population in 2024 (of people) | -0.0322 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Shirt Value per Wearer (10,000 simulations) > > **Simulation Results Summary: Shirt Value per Wearer** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | \$940,697 | > | Mean (expected value) | \$974,494 | > | Median (50th percentile) | \$966,091 | > | Standard Deviation | \$368,164 | > | 90% Range (5th-95th percentile) | \[\$385,781, \$1.6 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Shirt Value per Wearer; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Shirt Value per Wearer > > *This exceedance probability chart shows the likelihood that Shirt Value per Wearer will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Universal Right to Try with Evidence Implementation Cost per DALY: \$0.00237 > **NOTE:** > > Conditional implementation cost per DALY if all 50 states adopt, a mature pooled pragmatic-trial system operates under applicable federal authorization, and the modeled treatment-discovery acceleration occurs. The numerator includes the 50-state campaign and ten-year registry launch costs, excludes patient or payer spending on treatment delivery, trial-site services, and permitted study costs, and assumes center assessments fund the registry thereafter. The denominator counts the global treatment schedule shift once, with DALYs discounted at the standard rate so the near-term launch cost is compared with health benefits in present value. > > **Inputs**: > > - [Universal Right to Try with Evidence Implementation Cost](#sec-state_rtt_implementation_cost_total): \$65 million (95% CI: \$25 million - \$200 million) > - [Global Annual DALY Burden](#sec-global_annual_daly_burden) 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year) > - [Eventually Avoidable DALY Percentage](#sec-eventually_avoidable_daly_pct): 92.6% (95% CI: 50% - 98%) > - [Discounted Treatment Acceleration from Universal Right to Try with Evidence](#sec-state_rtt_treatment_acceleration_years_discounted) 🔢: 10.3 years > > $$ \begin{gathered} Cost_{RTT,DALY} \\ = C_{RTT} / (DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,RTT}^{PV}) \end{gathered} $$ where: $$ \begin{gathered} T_{accel,RTT}^{PV} \\ = [A\left(\frac{T_{queue,SQ}}{k_{RTT}}\right) - A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Universal Right to Try with Evidence Implementation Cost per DALY** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Discounted Treatment Acceleration from Universal Right to Try with Evidence (years) | -0.3477 | Moderate driver | > | Universal Right to Try with Evidence Implementation Cost (USD) | 0.2456 | Weak driver | > | Eventually Avoidable DALY Percentage (percentage) | -0.0527 | Minimal effect | > | Global Annual DALY Burden (DALYs/year) | -0.0236 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Universal Right to Try with Evidence Implementation Cost per DALY (10,000 simulations) > > **Simulation Results Summary: Universal Right to Try with Evidence Implementation Cost per DALY** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$0.00237 | > | Mean (expected value) | \$0.00574 | > | Median (50th percentile) | \$0.00276 | > | Standard Deviation | \$0.014 | > | 90% Range (5th-95th percentile) | \[\$0.00075, \$0.017\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Universal Right to Try with Evidence Implementation Cost per DALY; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Universal Right to Try with Evidence Implementation Cost per DALY > > *This exceedance probability chart shows the likelihood that Universal Right to Try with Evidence Implementation Cost per DALY will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Universal Right to Try with Evidence Implementation Cost per Life Saved: \$0.125 > **NOTE:** > > Conditional implementation cost per modeled premature death prevented if all 50 states adopt, a mature pooled pragmatic-trial system operates, and the modeled treatment-discovery acceleration occurs. This uses the same campaign and registry numerator as the cost-per-DALY estimate, and discounts future deaths prevented at the standard rate so they compare with near-term costs and with GiveWell’s near-term lives saved. > > **Inputs**: > > - [Universal Right to Try with Evidence Implementation Cost](#sec-state_rtt_implementation_cost_total): \$65 million (95% CI: \$25 million - \$200 million) > - [Global Daily Deaths from Disease and Aging](#sec-global_disease_deaths_daily) 📊: 150 thousand deaths/day (SE: ±7,500 deaths/day) > - [Eventually Avoidable Death Percentage](#sec-eventually_avoidable_death_pct): 92.6% (95% CI: 50% - 98%) > - [Discounted Treatment Acceleration from Universal Right to Try with Evidence](#sec-state_rtt_treatment_acceleration_years_discounted) 🔢: 10.3 years > > $$ \begin{gathered} Cost_{RTT,life} \\ = C_{RTT} / (Deaths_{disease,daily} \times \text{DAYS\_PER\_YEAR} \times Pct_{avoid,death} \times T_{accel,RTT}^{PV}) \end{gathered} $$ where: $$ \begin{gathered} T_{accel,RTT}^{PV} \\ = [A\left(\frac{T_{queue,SQ}}{k_{RTT}}\right) - A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Universal Right to Try with Evidence Implementation Cost per Life Saved** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Discounted Treatment Acceleration from Universal Right to Try with Evidence (years) | -0.3474 | Moderate driver | > | Universal Right to Try with Evidence Implementation Cost (USD) | 0.2413 | Weak driver | > | Eventually Avoidable Death Percentage (percentage) | -0.0713 | Minimal effect | > | Global Daily Deaths from Disease and Aging (deaths/day) | -0.0216 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Universal Right to Try with Evidence Implementation Cost per Life Saved (10,000 simulations) > > **Simulation Results Summary: Universal Right to Try with Evidence Implementation Cost per Life Saved** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$0.125 | > | Mean (expected value) | \$0.304 | > | Median (50th percentile) | \$0.143 | > | Standard Deviation | \$0.749 | > | 90% Range (5th-95th percentile) | \[\$0.039, \$0.912\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Universal Right to Try with Evidence Implementation Cost per Life Saved; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Universal Right to Try with Evidence Implementation Cost per Life Saved > > *This exceedance probability chart shows the likelihood that Universal Right to Try with Evidence Implementation Cost per Life Saved will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### DALYs Averted from Universal Right to Try with Evidence: 483 billion DALYs > **NOTE:** > > Conditional lifetime DALYs averted by shifting the global treatment-discovery schedule forward. By design, this applies the therapeutic-discovery timeline proxy to the eventually avoidable burden of all global diseases and aging-related degeneration. It is a schedule-shift calculation across future generations, not an observed epidemiological forecast. > > **Inputs**: > > - [Global Annual DALY Burden](#sec-global_annual_daly_burden) 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year) > - [Eventually Avoidable DALY Percentage](#sec-eventually_avoidable_daly_pct): 92.6% (95% CI: 50% - 98%) > - [Average Treatment Acceleration from Universal Right to Try with Evidence](#sec-state_rtt_treatment_acceleration_years) 🔢: 181 years > > $$ \begin{gathered} DALYs_{RTT} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,RTT} \\ = 2.88B \times 92.6\% \times 181 \\ = 483B \end{gathered} $$ where: $$ \begin{gathered} T_{accel,RTT} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{RTT}}\right) \\ = 222 \times \left(1 - \frac{1}{5.48}\right) \\ = 181 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for DALYs Averted from Universal Right to Try with Evidence** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Average Treatment Acceleration from Universal Right to Try with Evidence (years) | 0.9488 | Strong driver | > | Eventually Avoidable DALY Percentage (percentage) | 0.2682 | Weak driver | > | Global Annual DALY Burden (DALYs/year) | 0.1167 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: DALYs Averted from Universal Right to Try with Evidence (10,000 simulations) > > **Simulation Results Summary: DALYs Averted from Universal Right to Try with Evidence** > > | Statistic | Value | > |:--------------------------------|-----------------------------:| > | Baseline (deterministic) | 483 billion | > | Mean (expected value) | 495 billion | > | Median (50th percentile) | 462 billion | > | Standard Deviation | 217 billion | > | 90% Range (5th-95th percentile) | \[195 billion, 907 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for DALYs Averted from Universal Right to Try with Evidence; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: DALYs Averted from Universal Right to Try with Evidence > > *This exceedance probability chart shows the likelihood that DALYs Averted from Universal Right to Try with Evidence will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Lives Saved from Universal Right to Try with Evidence: 9.19 billion deaths > **NOTE:** > > Conditional cumulative premature deaths from global diseases and aging prevented across future generations by shifting the treatment-discovery schedule forward. The total can exceed the current population because it sums deaths prevented over the full acceleration period. > > **Inputs**: > > - [Global Daily Deaths from Disease and Aging](#sec-global_disease_deaths_daily) 📊: 150 thousand deaths/day (SE: ±7,500 deaths/day) > - [Eventually Avoidable Death Percentage](#sec-eventually_avoidable_death_pct): 92.6% (95% CI: 50% - 98%) > - [Average Treatment Acceleration from Universal Right to Try with Evidence](#sec-state_rtt_treatment_acceleration_years) 🔢: 181 years > > $$ \begin{gathered} Lives_{RTT} \\ = Deaths_{disease,daily} \times Pct_{avoid,death} \times T_{accel,RTT} \times 365 \\ = 150{,}000 \times 92.6\% \times 181 \times 365 \\ = 9.19B \end{gathered} $$ where: $$ \begin{gathered} T_{accel,RTT} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{RTT}}\right) \\ = 222 \times \left(1 - \frac{1}{5.48}\right) \\ = 181 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Lives Saved from Universal Right to Try with Evidence** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Average Treatment Acceleration from Universal Right to Try with Evidence (years) | 0.9436 | Strong driver | > | Eventually Avoidable Death Percentage (percentage) | 0.2710 | Weak driver | > | Global Daily Deaths from Disease and Aging (deaths/day) | 0.1115 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Lives Saved from Universal Right to Try with Evidence (10,000 simulations) > > **Simulation Results Summary: Lives Saved from Universal Right to Try with Evidence** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 9.19 billion | > | Mean (expected value) | 9.4 billion | > | Median (50th percentile) | 8.82 billion | > | Standard Deviation | 4.13 billion | > | 90% Range (5th-95th percentile) | \[3.71 billion, 17.2 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Lives Saved from Universal Right to Try with Evidence; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Lives Saved from Universal Right to Try with Evidence > > *This exceedance probability chart shows the likelihood that Lives Saved from Universal Right to Try with Evidence will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Disability-Equivalent Suffering Hours Prevented by Universal Right to Try with Evidence: 1.65 quadrillion hours > **NOTE:** > > Conditional disability-equivalent hours prevented by the treatment schedule shift. Converts the years-lived-with-disability share of DALYs into hours; it does not claim every hour is an hour of conscious pain. > > **Inputs**: > > - [DALYs Averted from Universal Right to Try with Evidence](#sec-state_rtt_treatment_acceleration_dalys) 🔢: 483 billion DALYs > - [YLD Proportion of Total DALYs](#sec-global_yld_proportion_of_dalys) 📊: 0.39 proportion (SE: ±0.03 proportion) > > $$ \begin{gathered} Hours_{suffer,RTT} \\ = DALYs_{RTT} \times Pct_{YLD} \times 8760 \\ = 483B \times 0.39 \times 8760 \\ = 1650T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{RTT} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,RTT} \\ = 2.88B \times 92.6\% \times 181 \\ = 483B \end{gathered} $$ where: $$ \begin{gathered} T_{accel,RTT} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{RTT}}\right) \\ = 222 \times \left(1 - \frac{1}{5.48}\right) \\ = 181 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Disability-Equivalent Suffering Hours Prevented by Universal Right to Try with Evidence** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | DALYs Averted from Universal Right to Try with Evidence (DALYs) | 0.9822 | Strong driver | > | YLD Proportion of Total DALYs (proportion) | 0.1721 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Disability-Equivalent Suffering Hours Prevented by Universal Right to Try with Evidence (10,000 simulations) > > **Simulation Results Summary: Disability-Equivalent Suffering Hours Prevented by Universal Right to Try with Evidence** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | 1.65 quadrillion | > | Mean (expected value) | 1.69 quadrillion | > | Median (50th percentile) | 1.57 quadrillion | > | Standard Deviation | 756 trillion | > | 90% Range (5th-95th percentile) | \[659 trillion, 3.14 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Disability-Equivalent Suffering Hours Prevented by Universal Right to Try with Evidence; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Disability-Equivalent Suffering Hours Prevented by Universal Right to Try with Evidence > > *This exceedance probability chart shows the likelihood that Disability-Equivalent Suffering Hours Prevented by Universal Right to Try with Evidence will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Disability-Equivalent Suffering Years Prevented by Universal Right to Try with Evidence: 189 billion years > **NOTE:** > > Conditional disability-equivalent years of suffering prevented by the treatment schedule shift: the years-lived-with-disability share of the DALYs averted. A disability weight of 0.25 sustained for four years equals one full-disability-equivalent year; it does not claim every year is a year of maximum conscious pain. > > **Inputs**: > > - [DALYs Averted from Universal Right to Try with Evidence](#sec-state_rtt_treatment_acceleration_dalys) 🔢: 483 billion DALYs > - [YLD Proportion of Total DALYs](#sec-global_yld_proportion_of_dalys) 📊: 0.39 proportion (SE: ±0.03 proportion) > > $$ \begin{gathered} Years_{suffer,RTT} \\ = DALYs_{RTT} \times Pct_{YLD} \\ = 483B \times 0.39 \\ = 189B \end{gathered} $$ where: $$ \begin{gathered} DALYs_{RTT} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,RTT} \\ = 2.88B \times 92.6\% \times 181 \\ = 483B \end{gathered} $$ where: $$ \begin{gathered} T_{accel,RTT} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{RTT}}\right) \\ = 222 \times \left(1 - \frac{1}{5.48}\right) \\ = 181 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Disability-Equivalent Suffering Years Prevented by Universal Right to Try with Evidence** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | DALYs Averted from Universal Right to Try with Evidence (DALYs) | 0.9822 | Strong driver | > | YLD Proportion of Total DALYs (proportion) | 0.1721 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Disability-Equivalent Suffering Years Prevented by Universal Right to Try with Evidence (10,000 simulations) > > **Simulation Results Summary: Disability-Equivalent Suffering Years Prevented by Universal Right to Try with Evidence** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 189 billion | > | Mean (expected value) | 193 billion | > | Median (50th percentile) | 180 billion | > | Standard Deviation | 86.3 billion | > | 90% Range (5th-95th percentile) | \[75.2 billion, 358 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Disability-Equivalent Suffering Years Prevented by Universal Right to Try with Evidence; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Disability-Equivalent Suffering Years Prevented by Universal Right to Try with Evidence > > *This exceedance probability chart shows the likelihood that Disability-Equivalent Suffering Years Prevented by Universal Right to Try with Evidence will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Average Treatment Acceleration from Universal Right to Try with Evidence: 181 years > **NOTE:** > > Average years earlier the first effective treatment arrives across the global therapeutic frontier after all 50 states adopt Universal Right to Try with Evidence. Uses the same schedule-shift structure as the 1% Treaty impact model: the status quo discovery timeline multiplied by one minus the inverse treatment-discovery multiplier. > > **Inputs**: > > - [Status Quo Average Years to First Treatment](#sec-status_quo_avg_years_to_first_treatment) 🔢: 222 years > - [Universal Right to Try with Evidence Treatment Discovery Multiplier](#sec-state_rtt_treatment_discovery_multiplier): 5.48x (95% CI: 1.1x - 15x) > > $$ \begin{gathered} T_{accel,RTT} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{RTT}}\right) \\ = 222 \times \left(1 - \frac{1}{5.48}\right) \\ = 181 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Average Treatment Acceleration from Universal Right to Try with Evidence** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Status Quo Average Years to First Treatment (years) | 0.8444 | Strong driver | > | Universal Right to Try with Evidence Treatment Discovery Multiplier (x) | 0.3959 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Average Treatment Acceleration from Universal Right to Try with Evidence (10,000 simulations) > > **Simulation Results Summary: Average Treatment Acceleration from Universal Right to Try with Evidence** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 181 | > | Mean (expected value) | 187 | > | Median (50th percentile) | 176 | > | Standard Deviation | 77.8 | > | 90% Range (5th-95th percentile) | \[79.1, 332\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Average Treatment Acceleration from Universal Right to Try with Evidence; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Average Treatment Acceleration from Universal Right to Try with Evidence > > *This exceedance probability chart shows the likelihood that Average Treatment Acceleration from Universal Right to Try with Evidence will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Discounted Treatment Acceleration from Universal Right to Try with Evidence: 10.3 years > **NOTE:** > > Present value, in years, of the average treatment schedule shift from Universal Right to Try with Evidence, discounted at the standard social discount rate. Same timing as the undiscounted shift: first treatments are found evenly over the status quo clearance period, versus evenly over that period divided by the discovery multiplier. The cost-per-DALY and cost-per-life ratios use it so the near-term launch cost is compared with health benefits in present value, as in the 1% Treaty model. A(T) = (1 - (1+r)^(-T)) / (T ln(1+r)) is the average discount factor over \[0, T\]. > > **Inputs**: > > - [Status Quo Therapeutic Space Exploration Time](#sec-status_quo_queue_clearance_years) 🔢: 443 years > - [Universal Right to Try with Evidence Treatment Discovery Multiplier](#sec-state_rtt_treatment_discovery_multiplier): 5.48x (95% CI: 1.1x - 15x) > - [Standard Discount Rate for NPV Analysis](#sec-npv_discount_rate_standard): 3% > > $$ \begin{gathered} T_{accel,RTT}^{PV} \\ = [A\left(\frac{T_{queue,SQ}}{k_{RTT}}\right) - A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Discounted Treatment Acceleration from Universal Right to Try with Evidence** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Universal Right to Try with Evidence Treatment Discovery Multiplier (x) | 0.8874 | Strong driver | > | Status Quo Therapeutic Space Exploration Time (years) | -0.3840 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Discounted Treatment Acceleration from Universal Right to Try with Evidence (10,000 simulations) > > **Simulation Results Summary: Discounted Treatment Acceleration from Universal Right to Try with Evidence** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 10.3 | > | Mean (expected value) | 8.76 | > | Median (50th percentile) | 8.15 | > | Standard Deviation | 5.03 | > | 90% Range (5th-95th percentile) | \[1.63, 18\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Discounted Treatment Acceleration from Universal Right to Try with Evidence; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Discounted Treatment Acceleration from Universal Right to Try with Evidence > > *This exceedance probability chart shows the likelihood that Discounted Treatment Acceleration from Universal Right to Try with Evidence will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Universal Right to Try with Evidence Cost-Effectiveness vs GiveWell Range Midpoint: 36.1kx > **NOTE:** > > Conditional cost-effectiveness of adopting Universal Right to Try with Evidence in all 50 states relative to the midpoint of GiveWell’s cited modeled cost-per-life-saved range. Right to Try lives saved are discounted to present value at the standard rate so they compare with GiveWell’s near-term lives saved. The cost scopes differ: the Right to Try numerator counts only the campaign and registry launch and excludes patient and payer spending on treatment delivery, trial-site services, and permitted study costs, while the GiveWell figure includes full program costs. This comparison is valid only if full adoption and mature implementation produce the modeled treatment schedule shift. > > **Inputs**: > > - [GiveWell Midpoint of Modeled Cost per Life Saved Range](#sec-givewell_cost_per_life_avg) 📊: \$4,500 > - [Universal Right to Try with Evidence Implementation Cost per Life Saved](#sec-state_rtt_implementation_cost_per_life_saved) 🔢: \$0.125 > > $$ \begin{gathered} k_{RTT,GiveWell} \\ = \frac{Cost_{GW,avg}}{Cost_{RTT,life}} \\ = \frac{\$4.5K}{\$0.125} \\ = 36{,}100 \end{gathered} $$ where: $$ \begin{gathered} Cost_{RTT,life} \\ = C_{RTT} / (Deaths_{disease,daily} \times \text{DAYS\_PER\_YEAR} \times Pct_{avoid,death} \times T_{accel,RTT}^{PV}) \end{gathered} $$ where: $$ \begin{gathered} T_{accel,RTT}^{PV} \\ = [A\left(\frac{T_{queue,SQ}}{k_{RTT}}\right) - A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Universal Right to Try with Evidence Cost-Effectiveness vs GiveWell Range Midpoint** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Universal Right to Try with Evidence Implementation Cost per Life Saved (USD/life) | -0.3093 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Universal Right to Try with Evidence Cost-Effectiveness vs GiveWell Range Midpoint (10,000 simulations) > > **Simulation Results Summary: Universal Right to Try with Evidence Cost-Effectiveness vs GiveWell Range Midpoint** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 36.1kx | > | Mean (expected value) | 41.4kx | > | Median (50th percentile) | 31.4kx | > | Standard Deviation | 34.9kx | > | 90% Range (5th-95th percentile) | \[4.9kx, 114.5kx\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Universal Right to Try with Evidence Cost-Effectiveness vs GiveWell Range Midpoint; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Universal Right to Try with Evidence Cost-Effectiveness vs GiveWell Range Midpoint > > *This exceedance probability chart shows the likelihood that Universal Right to Try with Evidence Cost-Effectiveness vs GiveWell Range Midpoint will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Status Quo Average Years to First Treatment: 222 years > **NOTE:** > > Average years until first treatment discovered for a typical disease under current system. At current discovery rates, the average disease waits half the total exploration time (~443/2 = ~222 years). > > **Inputs**: > > - [Status Quo Therapeutic Space Exploration Time](#sec-status_quo_queue_clearance_years) 🔢: 443 years > > $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ **Methodology**:^([12](../references.llms.md#ref-status-quo-cure-timeline-estimate)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Status Quo Average Years to First Treatment** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Status Quo Therapeutic Space Exploration Time (years) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Status Quo Average Years to First Treatment (10,000 simulations) > > **Simulation Results Summary: Status Quo Average Years to First Treatment** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 222 | > | Mean (expected value) | 251 | > | Median (50th percentile) | 238 | > | Standard Deviation | 88.8 | > | 90% Range (5th-95th percentile) | \[128, 420\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Status Quo Average Years to First Treatment; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Status Quo Average Years to First Treatment > > *This exceedance probability chart shows the likelihood that Status Quo Average Years to First Treatment will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Status Quo Therapeutic Space Exploration Time: 443 years > **NOTE:** > > Years to explore the entire therapeutic search space under current system. At current discovery rate of ~15 diseases/year getting first treatments, finding treatments for all ~6,650 untreated diseases would take ~443 years. > > **Inputs**: > > - [Diseases Without Effective Treatment](#sec-diseases_without_effective_treatment) 🔢: 6,650 diseases > - [Diseases Getting First Treatment Per Year](#sec-new_disease_first_treatments_per_year) 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year) > > $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ **Methodology**:^([12](../references.llms.md#ref-status-quo-cure-timeline-estimate)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Status Quo Therapeutic Space Exploration Time** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Diseases Getting First Treatment Per Year (diseases/year) | -0.8696 | Strong driver | > | Diseases Without Effective Treatment (diseases) | 0.3427 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Status Quo Therapeutic Space Exploration Time (10,000 simulations) > > **Simulation Results Summary: Status Quo Therapeutic Space Exploration Time** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 443 | > | Mean (expected value) | 502 | > | Median (50th percentile) | 475 | > | Standard Deviation | 178 | > | 90% Range (5th-95th percentile) | \[255, 841\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Status Quo Therapeutic Space Exploration Time; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Status Quo Therapeutic Space Exploration Time > > *This exceedance probability chart shows the likelihood that Status Quo Therapeutic Space Exploration Time will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cost per Life Saved per Verified Response (Conditional on Passage): \$2.5 > **NOTE:** > > Cost per life saved for one verified survey response, conditional on the treaty passing: blended activation cost per participant divided by lives saved per verified voter. Not probability-weighted; the probability-weighted campaign figure is TREATY_EXPECTED_COST_PER_DALY. > > **Inputs**: > > - [Activation Cost per Participant](#sec-global_coordination_activation_cost_per_participant) 🔢: \$6.5 > - [Lives Saved per Verified Voter](#sec-voter_lives_saved) 🔢: 2.6 lives > > $$ \begin{gathered} C_{life,resp} \\ = \frac{C_{activate,pp}}{Lives_{voter}} \\ = \frac{\$6.5}{2.6} \\ = \$2.5 \end{gathered} $$ where: $$ \begin{gathered} C_{activate,pp} \\ = R_{activate} + C_{verify,pp} \\ = \$5 + \$1.5 \\ = \$6.5 \end{gathered} $$ where: $$ \begin{gathered} Lives_{voter} \\ = \frac{Lives_{max}}{N_{voters,global}} \\ = \frac{10.7B}{4.13B} \\ = 2.6 \end{gathered} $$ where: $$ \begin{gathered} Lives_{max} \\ = Deaths_{disease,daily} \times T_{accel,max} \times 338 \\ = 150{,}000 \times 212 \times 338 \\ = 10.7B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cost per Life Saved per Verified Response (Conditional on Passage)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Lives Saved per Verified Voter (lives) | -0.7494 | Strong driver | > | Activation Cost per Participant (USD) | 0.5346 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cost per Life Saved per Verified Response (Conditional on Passage) (10,000 simulations) > > **Simulation Results Summary: Cost per Life Saved per Verified Response (Conditional on Passage)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | \$2.5 | > | Mean (expected value) | \$2.52 | > | Median (50th percentile) | \$2.31 | > | Standard Deviation | \$1.12 | > | 90% Range (5th-95th percentile) | \[\$1.12, \$4.68\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cost per Life Saved per Verified Response (Conditional on Passage); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cost per Life Saved per Verified Response (Conditional on Passage) > > *This exceedance probability chart shows the likelihood that Cost per Life Saved per Verified Response (Conditional on Passage) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Thalidomide DALYs Per Event: 41,760 DALYs > **NOTE:** > > Total DALYs per US-scale thalidomide event (YLL + YLD) > > **Inputs**: > > - [Thalidomide YLD Per Event](#sec-thalidomide_yld_per_event) 🔢: 12,960 years > - [Thalidomide YLL Per Event](#sec-thalidomide_yll_per_event) 🔢: 28,800 years > > $$ \begin{gathered} DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \end{gathered} $$ where: $$ \begin{gathered} YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ where: $$ \begin{gathered} YLL_{thal} \\ = Deaths_{thal} \times 80 \\ = 360 \times 80 \\ = 28{,}800 \end{gathered} $$ where: $$ \begin{gathered} Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Thalidomide DALYs Per Event** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:----------------| > | Thalidomide YLL Per Event (years) | 0.7035 | Strong driver | > | Thalidomide YLD Per Event (years) | 0.3838 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Thalidomide DALYs Per Event (10,000 simulations) > > **Simulation Results Summary: Thalidomide DALYs Per Event** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 41,760 | > | Mean (expected value) | 41,590 | > | Median (50th percentile) | 41,110 | > | Standard Deviation | 7,233 | > | 90% Range (5th-95th percentile) | \[30,379, 54,467\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Thalidomide DALYs Per Event; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Thalidomide DALYs Per Event > > *This exceedance probability chart shows the likelihood that Thalidomide DALYs Per Event will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Thalidomide Deaths Per Event: 360 deaths > **NOTE:** > > Deaths per US-scale thalidomide event > > **Inputs**: > > - [Thalidomide Mortality Rate](#sec-thalidomide_mortality_rate) 📊: 40% (95% CI: 35% - 45%) > - [Thalidomide US Cases Prevented](#sec-thalidomide_us_cases_prevented) 🔢: 900 cases > > $$ \begin{gathered} Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Thalidomide Deaths Per Event** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Thalidomide US Cases Prevented (cases) | 0.9385 | Strong driver | > | Thalidomide Mortality Rate (percentage) | 0.3437 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Thalidomide Deaths Per Event (10,000 simulations) > > **Simulation Results Summary: Thalidomide Deaths Per Event** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 360 | > | Mean (expected value) | 359 | > | Median (50th percentile) | 353 | > | Standard Deviation | 63.6 | > | 90% Range (5th-95th percentile) | \[261, 472\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Thalidomide Deaths Per Event; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Thalidomide Deaths Per Event > > *This exceedance probability chart shows the likelihood that Thalidomide Deaths Per Event will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Thalidomide Survivors Per Event: 540 cases > **NOTE:** > > Survivors per US-scale thalidomide event > > **Inputs**: > > - [Thalidomide Mortality Rate](#sec-thalidomide_mortality_rate) 📊: 40% (95% CI: 35% - 45%) > - [Thalidomide US Cases Prevented](#sec-thalidomide_us_cases_prevented) 🔢: 900 cases > > $$ \begin{gathered} N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Thalidomide Survivors Per Event** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Thalidomide US Cases Prevented (cases) | 0.9700 | Strong driver | > | Thalidomide Mortality Rate (percentage) | -0.2364 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Thalidomide Survivors Per Event (10,000 simulations) > > **Simulation Results Summary: Thalidomide Survivors Per Event** > > | Statistic | Value | > |:--------------------------------|-------------:| > | Baseline (deterministic) | 540 | > | Mean (expected value) | 538 | > | Median (50th percentile) | 531 | > | Standard Deviation | 92.5 | > | 90% Range (5th-95th percentile) | \[396, 704\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Thalidomide Survivors Per Event; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Thalidomide Survivors Per Event > > *This exceedance probability chart shows the likelihood that Thalidomide Survivors Per Event will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Thalidomide US Cases Prevented: 900 cases > **NOTE:** > > Estimated US thalidomide cases prevented by FDA rejection > > **Inputs**: > > - [Thalidomide Cases Worldwide](#sec-thalidomide_cases_worldwide) 📊: 15,000 cases (95% CI: 10,000 cases - 20,000 cases) > - [US Population Share 1960](#sec-thalidomide_us_population_share_1960) 📊: 6% (95% CI: 5.5% - 6.5%) > > $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Thalidomide US Cases Prevented** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Thalidomide Cases Worldwide (cases) | 0.9707 | Strong driver | > | US Population Share 1960 (percentage) | 0.2437 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Thalidomide US Cases Prevented (10,000 simulations) > > **Simulation Results Summary: Thalidomide US Cases Prevented** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 900 | > | Mean (expected value) | 897 | > | Median (50th percentile) | 886 | > | Standard Deviation | 149 | > | 90% Range (5th-95th percentile) | \[666, 1,166\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Thalidomide US Cases Prevented; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Thalidomide US Cases Prevented > > *This exceedance probability chart shows the likelihood that Thalidomide US Cases Prevented will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Thalidomide YLD Per Event: 12,960 years > **NOTE:** > > Years Lived with Disability per thalidomide event > > **Inputs**: > > - [Thalidomide Disability Weight](#sec-thalidomide_disability_weight) 📊: 0.4:1 (95% CI: 0.32:1 - 0.48:1) > - [Thalidomide Survivors Per Event](#sec-thalidomide_survivors_per_event) 🔢: 540 cases > - [Thalidomide Survivor Lifespan](#sec-thalidomide_survivor_lifespan) 📊: 60 years (95% CI: 50 years - 70 years) > > $$ \begin{gathered} YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Thalidomide YLD Per Event** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Thalidomide Survivors Per Event (cases) | 0.7990 | Strong driver | > | Thalidomide Disability Weight (ratio) | 0.4526 | Moderate driver | > | Thalidomide Survivor Lifespan (years) | 0.3747 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Thalidomide YLD Per Event (10,000 simulations) > > **Simulation Results Summary: Thalidomide YLD Per Event** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 12,960 | > | Mean (expected value) | 12,907 | > | Median (50th percentile) | 12,644 | > | Standard Deviation | 2,776 | > | 90% Range (5th-95th percentile) | \[8,780, 17,931\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Thalidomide YLD Per Event; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Thalidomide YLD Per Event > > *This exceedance probability chart shows the likelihood that Thalidomide YLD Per Event will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Thalidomide YLL Per Event: 28,800 years > **NOTE:** > > Years of Life Lost per thalidomide event (infant deaths) > > **Inputs**: > > - [Thalidomide Deaths Per Event](#sec-thalidomide_deaths_per_event) 🔢: 360 deaths > > $$ \begin{gathered} YLL_{thal} \\ = Deaths_{thal} \times 80 \\ = 360 \times 80 \\ = 28{,}800 \end{gathered} $$ where: $$ \begin{gathered} Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Thalidomide YLL Per Event** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Thalidomide Deaths Per Event (deaths) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Thalidomide YLL Per Event (10,000 simulations) > > **Simulation Results Summary: Thalidomide YLL Per Event** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 28,800 | > | Mean (expected value) | 28,684 | > | Median (50th percentile) | 28,274 | > | Standard Deviation | 5,088 | > | 90% Range (5th-95th percentile) | \[20,873, 37,748\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Thalidomide YLL Per Event; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Thalidomide YLL Per Event > > *This exceedance probability chart shows the likelihood that Thalidomide YLL Per Event will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### 3.5% Activism Benchmark: 280 million of people > **NOTE:** > > Headcount implied by the 3.5% activism threshold applied to global population. This is a historical tipping-point benchmark, not the public majority-of-humanity coordination target. Wide CI reflects uncertainty in applying Chenoweth’s national threshold to global treaty adoption. > > **Inputs**: > > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > - [Critical Mass Threshold for Social Change](#sec-global_population_activism_threshold_pct) 📊: 3.5% (95% CI: 1% - 10%) > > $$ \begin{gathered} N_{activism} \\ = Pop_{global} \times Threshold_{activism} \\ = 8B \times 3.5\% \\ = 280M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for 3.5% Activism Benchmark** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Critical Mass Threshold for Social Change (percent) | 0.9997 | Strong driver | > | Global Population in 2024 (of people) | 0.0206 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: 3.5% Activism Benchmark (10,000 simulations) > > **Simulation Results Summary: 3.5% Activism Benchmark** > > | Statistic | Value | > |:--------------------------------|----------------------------:| > | Baseline (deterministic) | 280 million | > | Mean (expected value) | 278 million | > | Median (50th percentile) | 237 million | > | Standard Deviation | 164 million | > | 90% Range (5th-95th percentile) | \[88 million, 628 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for 3.5% Activism Benchmark; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: 3.5% Activism Benchmark > > *This exceedance probability chart shows the likelihood that 3.5% Activism Benchmark will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Funding from 1% of Global Military Spending Redirected to DIH: \$27.2 billion > **NOTE:** > > Annual funding from 1% of global military spending redirected to DIH > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > > $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Annual Funding from 1% of Global Military Spending Redirected to DIH is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.720e-02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$27.2 billion | > | Mean (expected value) | \$27.2 billion | > | Median (50th percentile) | \$27.2 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$27.2 billion, \$27.2 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Annual Funding from 1% of Global Military Spending Redirected to DIH collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.720e-02) > > Approximate deterministic value: **\$27.2 billion** ### Treaty System Benefit Multiplier vs Childhood Vaccination Programs: 10.3x > **NOTE:** > > Treaty system benefit multiplier vs childhood vaccination programs > > **Inputs**: > > - [Estimated Annual Global Economic Benefit from Childhood Vaccination Programs](#sec-childhood_vaccination_annual_benefit) 📊: \$15 billion (SE: ±\$4.5 billion) > - [1% treaty Basic Annual Benefits (Peace + R&D Savings)](#sec-treaty_peace_plus_rd_annual_benefits) 🔢: \$154 billion > > $$ \begin{gathered} k_{treaty:vax} \\ = \frac{Benefit_{peace+RD}}{Benefit_{vax,ann}} \\ = \frac{\$154B}{\$15B} \\ = 10.3 \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$40.5B \\ = \$154B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty System Benefit Multiplier vs Childhood Vaccination Programs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Estimated Annual Global Economic Benefit from Childhood Vaccination Programs (USD/year) | -0.8959 | Strong driver | > | 1% treaty Basic Annual Benefits (Peace + R&D Savings) (USD/year) | 0.2260 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty System Benefit Multiplier vs Childhood Vaccination Programs (10,000 simulations) > > **Simulation Results Summary: Treaty System Benefit Multiplier vs Childhood Vaccination Programs** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 10.3x | > | Mean (expected value) | 11.1x | > | Median (50th percentile) | 10.7x | > | Standard Deviation | 3.4x | > | 90% Range (5th-95th percentile) | \[6.54x, 17.3x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty System Benefit Multiplier vs Childhood Vaccination Programs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty System Benefit Multiplier vs Childhood Vaccination Programs > > *This exceedance probability chart shows the likelihood that Treaty System Benefit Multiplier vs Childhood Vaccination Programs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Amortized Annual Treaty Campaign Cost: \$250 million > **NOTE:** > > Amortized annual campaign cost (total cost ÷ campaign duration) > > **Inputs**: > > - [Treaty Campaign Duration](#sec-treaty_campaign_duration_years): 4 years (95% CI: 3 years - 5 years) > - [Total 1% Treaty Campaign Cost](#sec-treaty_campaign_total_cost) 🔢: \$1 billion > > $$ \begin{gathered} Cost_{camp,amort} \\ = \frac{Cost_{campaign}}{T_{campaign}} \\ = \frac{\$1B}{4} \\ = \$250M \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Amortized Annual Treaty Campaign Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:------------------------------------|------------------------:|:---------------| > | Total 1% Treaty Campaign Cost (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Amortized Annual Treaty Campaign Cost (10,000 simulations) > > **Simulation Results Summary: Amortized Annual Treaty Campaign Cost** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$250 million | > | Mean (expected value) | \$250 million | > | Median (50th percentile) | \$239 million | > | Standard Deviation | \$62.8 million | > | 90% Range (5th-95th percentile) | \[\$168 million, \$369 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Amortized Annual Treaty Campaign Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Amortized Annual Treaty Campaign Cost > > *This exceedance probability chart shows the likelihood that Amortized Annual Treaty Campaign Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total 1% Treaty Campaign Cost: \$1 billion > **NOTE:** > > Total treaty campaign cost (100% VICTORY Incentive Alignment Bonds) > > **Inputs**: > > - [Viral Referendum Budget](#sec-treaty_campaign_viral_referendum_base_case): \$250 million (95% CI: \$150 million - \$410 million) > - [Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance](#sec-treaty_campaign_budget_lobbying): \$650 million (95% CI: \$325 million - \$1.3 billion) > - [Reserve Fund / Contingency Buffer](#sec-treaty_campaign_budget_reserve): \$100 million (95% CI: \$20 million - \$150 million) > > $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total 1% Treaty Campaign Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance (USD) | 0.9915 | Strong driver | > | Reserve Fund / Contingency Buffer (USD) | 0.1132 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total 1% Treaty Campaign Cost (10,000 simulations) > > **Simulation Results Summary: Total 1% Treaty Campaign Cost** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$1 billion | > | Mean (expected value) | \$999 million | > | Median (50th percentile) | \$956 million | > | Standard Deviation | \$251 million | > | 90% Range (5th-95th percentile) | \[\$672 million, \$1.48 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total 1% Treaty Campaign Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total 1% Treaty Campaign Cost > > *This exceedance probability chart shows the likelihood that Total 1% Treaty Campaign Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput): \$0.02 > **NOTE:** > > Cost per DALY averted from elimination of efficacy lag plus earlier treatment discovery from increased trial throughput, with DALYs discounted at the standard rate. Only counts campaign cost; ignores economic benefits from funding and R&D savings. > > **Inputs**: > > - [Total 1% Treaty Campaign Cost](#sec-treaty_campaign_total_cost) 🔢: \$1 billion > - [Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_dalys_discounted) 🔢: 50.2 billion DALYs > > $$ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}^{PV}} \\ = \frac{\$1B}{50.2B} \\ = \$0.0199 \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Discounted DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | -0.7545 | Strong driver | > | Total 1% Treaty Campaign Cost (USD) | 0.4160 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) (10,000 simulations) > > **Simulation Results Summary: Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$0.02 | > | Mean (expected value) | \$0.022 | > | Median (50th percentile) | \$0.019 | > | Standard Deviation | \$0.013 | > | 90% Range (5th-95th percentile) | \[\$0.011, \$0.047\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) > > *This exceedance probability chart shows the likelihood that Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion: \$3.16 trillion > **NOTE:** > > Cumulative treaty funding over 20 years with IAB ratchet expansion following roadmap timeline. Expansion driven by bondholder lobbying incentives (10% of treaty inflows). > > **Inputs**: > > - [Global Military Spending in 2024](#sec-global_military_spending_annual_2024) 📊: \$2.72 trillion > - [Treaty Ratchet Terminal Redirect Share](#sec-treaty_ratchet_terminal_share): 10% (95% CI: 1% - 19%) > > $$ \begin{gathered} Fund_{20yr,ratchet} \\ = \text{GLOBAL\_MILITARY} \times (0.01 \times 3 \\ + \min\left(0.02, s_{ratchet}\right) \times 4 \\ + \min\left(0.05, s_{ratchet}\right) \times 5 \\ + s_{ratchet} \times 8) \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Ratchet Terminal Redirect Share (percent) | 0.9946 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion (10,000 simulations) > > **Simulation Results Summary: Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$3.16 trillion | > | Mean (expected value) | \$3.13 trillion | > | Median (50th percentile) | \$3.16 trillion | > | Standard Deviation | \$1.04 trillion | > | 90% Range (5th-95th percentile) | \[\$1.19 trillion, \$4.81 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion > > *This exceedance probability chart shows the likelihood that Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Cybercrime Recovery GDP Growth Bonus (Year 15): 0.402% > **NOTE:** > > Annual GDP growth bonus by year 15 from reducing cybercrime drag as the treaty weakens the destructive economy feedback loop. > > **Inputs**: > > - [Global Cybercrime Costs (2025)](#sec-global_cybercrime_cost_annual_2025) 📊: \$10.5 trillion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Treaty Effective Reallocation Share (Year 15)](#sec-treaty_effective_reallocation_share_year_15) 🔢: 4.4% > - [Peace Dividend Conflict Elasticity](#sec-peace_dividend_conflict_elasticity): 1:1 (95% CI: 0.25:1 - 1.5:1) > > $$ \begin{gathered} g_{cyber,treaty,15} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,15} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Cybercrime Recovery GDP Growth Bonus (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Effective Reallocation Share (Year 15) (rate) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Cybercrime Recovery GDP Growth Bonus (Year 15) (10,000 simulations) > > **Simulation Results Summary: Treaty Cybercrime Recovery GDP Growth Bonus (Year 15)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.402% | > | Mean (expected value) | 0.394% | > | Median (50th percentile) | 0.402% | > | Standard Deviation | 0.101% | > | 90% Range (5th-95th percentile) | \[0.189%, 0.541%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Cybercrime Recovery GDP Growth Bonus (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Cybercrime Recovery GDP Growth Bonus (Year 15) > > *This exceedance probability chart shows the likelihood that Treaty Cybercrime Recovery GDP Growth Bonus (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Cybercrime Recovery GDP Growth Bonus (Year 20): 0.53% > **NOTE:** > > Annual GDP growth bonus by year 20 from reducing cybercrime drag as the treaty weakens the destructive economy feedback loop. > > **Inputs**: > > - [Global Cybercrime Costs (2025)](#sec-global_cybercrime_cost_annual_2025) 📊: \$10.5 trillion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Treaty Effective Reallocation Share (Year 20)](#sec-treaty_effective_reallocation_share_year_20) 🔢: 5.8% > - [Peace Dividend Conflict Elasticity](#sec-peace_dividend_conflict_elasticity): 1:1 (95% CI: 0.25:1 - 1.5:1) > > $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Cybercrime Recovery GDP Growth Bonus (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Effective Reallocation Share (Year 20) (rate) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Cybercrime Recovery GDP Growth Bonus (Year 20) (10,000 simulations) > > **Simulation Results Summary: Treaty Cybercrime Recovery GDP Growth Bonus (Year 20)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.53% | > | Mean (expected value) | 0.525% | > | Median (50th percentile) | 0.531% | > | Standard Deviation | 0.175% | > | 90% Range (5th-95th percentile) | \[0.199%, 0.808%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Cybercrime Recovery GDP Growth Bonus (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Cybercrime Recovery GDP Growth Bonus (Year 20) > > *This exceedance probability chart shows the likelihood that Treaty Cybercrime Recovery GDP Growth Bonus (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Disease Cure Fraction (15yr, Ratchet Schedule): 100% > **NOTE:** > > Fraction of currently untreatable diseases with a first effective treatment by year 15 under the treaty: queue progress integrated over the ratchet schedule, with trial capacity scaling linearly with funding up to the physical participant ceiling. Binds the single ratchet knob: at TREATY_RATCHET_TERMINAL_SHARE = 0.01 (ratchet off) this degrades to 15/36 of the queue (~42%); on the central schedule the queue clears around year 12, so the central is 100%. The ~36-year queue-clearance figure quoted elsewhere is the flat-1% case by construction. > > **Inputs**: > > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > - [Maximum Trial Capacity Multiplier (Physical Limit)](#sec-dfda_max_trial_capacity_multiplier_physical) 🔢: 566x > - [Treaty Ratchet Terminal Redirect Share](#sec-treaty_ratchet_terminal_share): 10% (95% CI: 1% - 19%) > - [Status Quo Therapeutic Space Exploration Time](#sec-status_quo_queue_clearance_years) 🔢: 443 years > > $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Disease Cure Fraction (15yr, Ratchet Schedule)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Ratchet Terminal Redirect Share (percent) | 0.3304 | Moderate driver | > | Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (x) | 0.3187 | Moderate driver | > | Status Quo Therapeutic Space Exploration Time (years) | -0.2646 | Weak driver | > | Maximum Trial Capacity Multiplier (Physical Limit) (x) | 0.0268 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Disease Cure Fraction (15yr, Ratchet Schedule) (10,000 simulations) > > **Simulation Results Summary: Treaty Disease Cure Fraction (15yr, Ratchet Schedule)** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 100% | > | Mean (expected value) | 93.3% | > | Median (50th percentile) | 100% | > | Standard Deviation | 16.7% | > | 90% Range (5th-95th percentile) | \[49.5%, 100%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Disease Cure Fraction (15yr, Ratchet Schedule); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Disease Cure Fraction (15yr, Ratchet Schedule) > > *This exceedance probability chart shows the likelihood that Treaty Disease Cure Fraction (15yr, Ratchet Schedule) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Disease Cure Fraction (20yr, Ratchet Schedule): 100% > **NOTE:** > > Fraction of currently untreatable diseases with a first effective treatment by year 20 under the treaty: queue progress integrated over the ratchet schedule, with trial capacity scaling linearly with funding up to the physical participant ceiling. Binds the single ratchet knob: at TREATY_RATCHET_TERMINAL_SHARE = 0.01 (ratchet off) this degrades to 20/36 of the queue (~56%); on the central schedule the queue clears around year 12, so the central is 100%. The ~36-year queue-clearance figure quoted elsewhere is the flat-1% case by construction. > > **Inputs**: > > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > - [Maximum Trial Capacity Multiplier (Physical Limit)](#sec-dfda_max_trial_capacity_multiplier_physical) 🔢: 566x > - [Treaty Ratchet Terminal Redirect Share](#sec-treaty_ratchet_terminal_share): 10% (95% CI: 1% - 19%) > - [Status Quo Therapeutic Space Exploration Time](#sec-status_quo_queue_clearance_years) 🔢: 443 years > > $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Disease Cure Fraction (20yr, Ratchet Schedule)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Ratchet Terminal Redirect Share (percent) | 0.3450 | Moderate driver | > | Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (x) | 0.1896 | Weak driver | > | Status Quo Therapeutic Space Exploration Time (years) | -0.1628 | Weak driver | > | Maximum Trial Capacity Multiplier (Physical Limit) (x) | 0.0196 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Disease Cure Fraction (20yr, Ratchet Schedule) (10,000 simulations) > > **Simulation Results Summary: Treaty Disease Cure Fraction (20yr, Ratchet Schedule)** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 100% | > | Mean (expected value) | 97.4% | > | Median (50th percentile) | 100% | > | Standard Deviation | 11% | > | 90% Range (5th-95th percentile) | \[78.3%, 100%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Disease Cure Fraction (20yr, Ratchet Schedule); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Disease Cure Fraction (20yr, Ratchet Schedule) > > *This exceedance probability chart shows the likelihood that Treaty Disease Cure Fraction (20yr, Ratchet Schedule) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Effective Reallocation Share (Year 15): 4.4% > **NOTE:** > > Average military-to-medicine reallocation share over 15 years under the treaty take-hold path (1% for 3 years, 2% for 4 years, 5% for 5 years, terminal ratchet share for 3 years). Binds the single ratchet knob: at TREATY_RATCHET_TERMINAL_SHARE = 0.01 this degrades to a flat 1%. > > **Inputs**: > > - [Treaty Ratchet Terminal Redirect Share](#sec-treaty_ratchet_terminal_share): 10% (95% CI: 1% - 19%) > > $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Effective Reallocation Share (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Ratchet Terminal Redirect Share (percent) | 0.9707 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Effective Reallocation Share (Year 15) (10,000 simulations) > > **Simulation Results Summary: Treaty Effective Reallocation Share (Year 15)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 4.4% | > | Mean (expected value) | 4.31% | > | Median (50th percentile) | 4.41% | > | Standard Deviation | 1.1% | > | 90% Range (5th-95th percentile) | \[2.07%, 5.92%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Effective Reallocation Share (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Effective Reallocation Share (Year 15) > > *This exceedance probability chart shows the likelihood that Treaty Effective Reallocation Share (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Effective Reallocation Share (Year 20): 5.8% > **NOTE:** > > Average military-to-medicine reallocation share over 20 years under the treaty take-hold path (1% for 3 years, 2% for 4 years, 5% for 5 years, terminal ratchet share for 8 years). Binds the single ratchet knob: at TREATY_RATCHET_TERMINAL_SHARE = 0.01 this degrades to a flat 1%. > > **Inputs**: > > - [Treaty Ratchet Terminal Redirect Share](#sec-treaty_ratchet_terminal_share): 10% (95% CI: 1% - 19%) > > $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Effective Reallocation Share (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Ratchet Terminal Redirect Share (percent) | 0.9946 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Effective Reallocation Share (Year 20) (10,000 simulations) > > **Simulation Results Summary: Treaty Effective Reallocation Share (Year 20)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 5.8% | > | Mean (expected value) | 5.75% | > | Median (50th percentile) | 5.81% | > | Standard Deviation | 1.92% | > | 90% Range (5th-95th percentile) | \[2.18%, 8.85%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Effective Reallocation Share (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Effective Reallocation Share (Year 20) > > *This exceedance probability chart shows the likelihood that Treaty Effective Reallocation Share (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Expected Cost per DALY (Risk-Adjusted): \$1.99 > **NOTE:** > > Expected cost per DALY accounting for political success probability uncertainty. Monte Carlo samples from beta(0.1%, 10%) distribution. At the conservative 1% estimate, this is still more cost-effective than bed nets (BED_NETS_COST_PER_DALY). > > **Inputs**: > > - [Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)](#sec-treaty_cost_per_daly_trial_capacity_plus_efficacy_lag) 🔢: \$0.02 > - [Political Success Probability](#sec-political_success_probability) 📊: 1% (95% CI: 0.1% - 10%) > > $$ \begin{gathered} E[Cost_{DALY}] \\ = \frac{Cost_{treaty,DALY}}{P_{success}} \\ = \frac{\$0.0199}{1\%} \\ = \$1.99 \end{gathered} $$ where: $$ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}^{PV}} \\ = \frac{\$1B}{50.2B} \\ = \$0.0199 \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Expected Cost per DALY (Risk-Adjusted)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) (USD/DALY) | 0.5769 | Strong driver | > | Political Success Probability (rate) | -0.4390 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Expected Cost per DALY (Risk-Adjusted) (10,000 simulations) > > **Simulation Results Summary: Expected Cost per DALY (Risk-Adjusted)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | \$1.99 | > | Mean (expected value) | \$13 | > | Median (50th percentile) | \$11 | > | Standard Deviation | \$14 | > | 90% Range (5th-95th percentile) | \[\$0.37, \$38\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Expected Cost per DALY (Risk-Adjusted); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Expected Cost per DALY (Risk-Adjusted) > > *This exceedance probability chart shows the likelihood that Expected Cost per DALY (Risk-Adjusted) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Expected Treaty ROI (Risk-Adjusted): 75.3 thousand:1 > **NOTE:** > > Expected ROI for 1% treaty accounting for political success probability uncertainty. Monte Carlo samples POLITICAL_SUCCESS_PROBABILITY from beta(0.1%, 10%) distribution to generate full expected value distribution. Central value uses 1% probability. > > **Inputs**: > > - [Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput](#sec-treaty_roi_trial_capacity_plus_efficacy_lag) 🔢: 7.53 million:1 > - [Political Success Probability](#sec-political_success_probability) 📊: 1% (95% CI: 0.1% - 10%) > > $$ \begin{gathered} E[ROI_{max}] \\ = ROI_{max} \times P_{success} \\ = 7.53M \times 1\% \\ = 75{,}300 \end{gathered} $$ where: $$ \begin{gathered} ROI_{max} \\ = \frac{Value_{max}}{Cost_{campaign}} \\ = \frac{\$7530T}{\$1B} \\ = 7.53M \end{gathered} $$ where: $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ **Methodology**: [Direct Calculation](#calculated) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Expected Treaty ROI (Risk-Adjusted)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Political Success Probability (rate) | 0.8870 | Strong driver | > | Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput (ratio) | 0.2249 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Expected Treaty ROI (Risk-Adjusted) (10,000 simulations) > > **Simulation Results Summary: Expected Treaty ROI (Risk-Adjusted)** > > | Statistic | Value | > |:--------------------------------|----------------------------:| > | Baseline (deterministic) | 75.3 thousand:1 | > | Mean (expected value) | 81.9 thousand:1 | > | Median (50th percentile) | 14.3 thousand:1 | > | Standard Deviation | 165 thousand:1 | > | 90% Range (5th-95th percentile) | \[3,703:1, 404 thousand:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Expected Treaty ROI (Risk-Adjusted); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Expected Treaty ROI (Risk-Adjusted) > > *This exceedance probability chart shows the likelihood that Expected Treaty ROI (Risk-Adjusted) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Expected Cost-Effectiveness vs Bed Nets Multiplier: 92.1x > **NOTE:** > > Expected value multiplier vs bed nets (accounts for political uncertainty at 1% success rate) > > **Inputs**: > > - [Bed Nets Cost per DALY](#sec-bed_nets_cost_per_daly) 🔢: \$184 > - [Expected Cost per DALY (Risk-Adjusted)](#sec-treaty_expected_cost_per_daly) 🔢: \$1.99 > > $$ \begin{gathered} E[k_{nets}] \\ = \frac{Cost_{nets}}{E[Cost_{DALY}]} \\ = \frac{\$184}{\$1.99} \\ = 92.1 \end{gathered} $$ where: $$ \begin{gathered} Cost_{nets} \\ = Cost_{GW,max} / ((1 - (1 \\ + r_{discount})^{-LE_{global}}) / \ln\left(1 + r_{discount}\right)) \end{gathered} $$ where: $$ \begin{gathered} E[Cost_{DALY}] \\ = \frac{Cost_{treaty,DALY}}{P_{success}} \\ = \frac{\$0.0199}{1\%} \\ = \$1.99 \end{gathered} $$ where: $$ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}^{PV}} \\ = \frac{\$1B}{50.2B} \\ = \$0.0199 \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Expected Cost-Effectiveness vs Bed Nets Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Expected Cost per DALY (Risk-Adjusted) (USD/DALY) | -0.4018 | Moderate driver | > | Bed Nets Cost per DALY (USD/DALY) | 0.1062 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Expected Cost-Effectiveness vs Bed Nets Multiplier (10,000 simulations) > > **Simulation Results Summary: Expected Cost-Effectiveness vs Bed Nets Multiplier** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 92.1x | > | Mean (expected value) | 101x | > | Median (50th percentile) | 17.6x | > | Standard Deviation | 206x | > | 90% Range (5th-95th percentile) | \[4.55x, 493x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Expected Cost-Effectiveness vs Bed Nets Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Expected Cost-Effectiveness vs Bed Nets Multiplier > > *This exceedance probability chart shows the likelihood that Expected Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty HALE Gain at Year 15: 16.1 years > **NOTE:** > > HALE improvement at year 15 under Treaty Trajectory. It includes both closing the current HALE gap from disease/disability and a conservative partial realization of longer-run longevity gains. > > **Inputs**: > > - [Treaty Disease Cure Fraction (15yr, Ratchet Schedule)](#sec-treaty_disease_cure_fraction_15yr) 🔢: 100% > - [Global Healthy Life Expectancy (HALE)](#sec-global_hale_current) 📊: 63.3 years (SE: ±1.5 years) > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > - [Treaty Longevity HALE Gain at Year 15](#sec-treaty_longevity_hale_gain_year_15) 🔢: 6 years > > $$ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \Delta_{HALE} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty HALE Gain at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Longevity HALE Gain at Year 15 (years) | 0.8808 | Strong driver | > | Global Life Expectancy (2024) (years) | 0.2512 | Weak driver | > | Treaty Disease Cure Fraction (15yr, Ratchet Schedule) (rate) | 0.2343 | Weak driver | > | Global Healthy Life Expectancy (HALE) (years) | -0.1978 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty HALE Gain at Year 15 (10,000 simulations) > > **Simulation Results Summary: Treaty HALE Gain at Year 15** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 16.1 | > | Mean (expected value) | 15 | > | Median (50th percentile) | 13.8 | > | Standard Deviation | 7.08 | > | 90% Range (5th-95th percentile) | \[6.53, 27.4\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty HALE Gain at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty HALE Gain at Year 15 > > *This exceedance probability chart shows the likelihood that Treaty HALE Gain at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty HALE Value Per Capita: \$2.42 million > **NOTE:** > > Economic value of Treaty Trajectory HALE gains at year 15 using the standard QALY value. > > **Inputs**: > > - [Treaty HALE Gain at Year 15](#sec-treaty_hale_gain_year_15) 🔢: 16.1 years > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} Value_{HALE,treaty} \\ = \Delta HALE_{treaty,15} \times Value_{QALY} \\ = 16.1 \times \$150K \\ = \$2.42M \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \Delta_{HALE} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty HALE Value Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty HALE Gain at Year 15 (years) | 0.9148 | Strong driver | > | Standard Economic Value per QALY (USD/QALY) | 0.3496 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty HALE Value Per Capita (10,000 simulations) > > **Simulation Results Summary: Treaty HALE Value Per Capita** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$2.42 million | > | Mean (expected value) | \$2.25 million | > | Median (50th percentile) | \$2.03 million | > | Standard Deviation | \$1.17 million | > | 90% Range (5th-95th percentile) | \[\$893,314, \$4.33 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty HALE Value Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty HALE Value Per Capita > > *This exceedance probability chart shows the likelihood that Treaty HALE Value Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Health Recovery GDP Growth Bonus (Year 15): 0.818% > **NOTE:** > > Annualized GDP growth bonus by year 15 from lower disease burden under the treaty path. Deliberately EXCLUDES the monetized value of life-years from eliminating the existing-drug efficacy lag, matching the year-20 model and the chapter’s stated accounting rule: that value belongs in health and welfare accounting, not in the output ledger. (The previous version injected the \$259T cumulative mortality-valuation stock into an annual growth rate, producing a year-15 income ABOVE the year-20 income on the same trajectory.) > > **Inputs**: > > - [Treaty Disease Cure Fraction (15yr, Ratchet Schedule)](#sec-treaty_disease_cure_fraction_15yr) 🔢: 100% > - [Disease Burden as % of GDP](#sec-disease_burden_gdp_drag_pct) 📊: 13% > > $$ \begin{gathered} g_{health,treaty,15} \\ = \frac{f_{cure,15,treaty} - d_{disease}}{-7.22} \\ = \frac{100\% - 13\%}{-7.22} \\ = 0.818\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Health Recovery GDP Growth Bonus (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Disease Cure Fraction (15yr, Ratchet Schedule) (rate) | 0.9999 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Health Recovery GDP Growth Bonus (Year 15) (10,000 simulations) > > **Simulation Results Summary: Treaty Health Recovery GDP Growth Bonus (Year 15)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.818% | > | Mean (expected value) | 0.765% | > | Median (50th percentile) | 0.818% | > | Standard Deviation | 0.134% | > | 90% Range (5th-95th percentile) | \[0.417%, 0.818%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Health Recovery GDP Growth Bonus (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Health Recovery GDP Growth Bonus (Year 15) > > *This exceedance probability chart shows the likelihood that Treaty Health Recovery GDP Growth Bonus (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Health Recovery GDP Growth Bonus (Year 20): 0.613% > **NOTE:** > > Annualized GDP growth bonus by year 20 from lower disease burden under the treaty path. > > **Inputs**: > > - [Treaty Disease Cure Fraction (20yr, Ratchet Schedule)](#sec-treaty_disease_cure_fraction_20yr) 🔢: 100% > - [Disease Burden as % of GDP](#sec-disease_burden_gdp_drag_pct) 📊: 13% > - [Treaty Health Recovery Annualization Horizon](#sec-treaty_health_recovery_annualization_years): 20 years > > $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Health Recovery GDP Growth Bonus (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Disease Cure Fraction (20yr, Ratchet Schedule) (rate) | 0.9999 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Health Recovery GDP Growth Bonus (Year 20) (10,000 simulations) > > **Simulation Results Summary: Treaty Health Recovery GDP Growth Bonus (Year 20)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.613% | > | Mean (expected value) | 0.597% | > | Median (50th percentile) | 0.613% | > | Standard Deviation | 0.0656% | > | 90% Range (5th-95th percentile) | \[0.486%, 0.613%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Health Recovery GDP Growth Bonus (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Health Recovery GDP Growth Bonus (Year 20) > > *This exceedance probability chart shows the likelihood that Treaty Health Recovery GDP Growth Bonus (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Longevity HALE Gain at Year 15: 6 years > **NOTE:** > > Additional healthy years at year 15 from partial realization of longer-run treaty longevity gains. This removes the implicit cap at today’s life expectancy while keeping year-15 realization conservative. > > **Inputs**: > > - [Life Extension from Treaty Research Acceleration](#sec-life_extension_years) 📊: 20 years (95% CI: 5 years - 100 years) > - [HALE Longevity Realization Share (Year 15)](#sec-hale_longevity_realization_share_year_15): 30% > - [Treaty Disease Cure Fraction (15yr, Ratchet Schedule)](#sec-treaty_disease_cure_fraction_15yr) 🔢: 100% > > $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Longevity HALE Gain at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Life Extension from Treaty Research Acceleration (years) | 0.9714 | Strong driver | > | Treaty Disease Cure Fraction (15yr, Ratchet Schedule) (rate) | 0.1630 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Longevity HALE Gain at Year 15 (10,000 simulations) > > **Simulation Results Summary: Treaty Longevity HALE Gain at Year 15** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 6 | > | Mean (expected value) | 5.53 | > | Median (50th percentile) | 3.54 | > | Standard Deviation | 6.24 | > | 90% Range (5th-95th percentile) | \[0.683, 17.3\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Longevity HALE Gain at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Longevity HALE Gain at Year 15 > > *This exceedance probability chart shows the likelihood that Treaty Longevity HALE Gain at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### 1% treaty Basic Annual Benefits (Peace + R&D Savings): \$154 billion > **NOTE:** > > Basic annual benefits: peace dividend + pragmatic trial R&D savings only (2 of 8 benefit categories, excludes regulatory delay value) > > **Inputs**: > > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > - [Annual R&D Savings from Pragmatic Trials](#sec-dfda_benefit_rd_only_annual) 🔢: \$40.5 billion > > $$ \begin{gathered} Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$40.5B \\ = \$154B \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} $$ where: $$ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for 1% treaty Basic Annual Benefits (Peace + R&D Savings)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 0.8158 | Strong driver | > | Annual R&D Savings from Pragmatic Trials (USD/year) | 0.5782 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: 1% treaty Basic Annual Benefits (Peace + R&D Savings) (10,000 simulations) > > **Simulation Results Summary: 1% treaty Basic Annual Benefits (Peace + R&D Savings)** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$154 billion | > | Mean (expected value) | \$154 billion | > | Median (50th percentile) | \$153 billion | > | Standard Deviation | \$10.7 billion | > | 90% Range (5th-95th percentile) | \[\$137 billion, \$172 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for 1% treaty Basic Annual Benefits (Peace + R&D Savings); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: 1% treaty Basic Annual Benefits (Peace + R&D Savings) > > *This exceedance probability chart shows the likelihood that 1% treaty Basic Annual Benefits (Peace + R&D Savings) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Peace Recovery GDP Growth Bonus (Year 15): 0.435% > **NOTE:** > > Annual GDP growth bonus by year 15 from explicit avoided war-cost drag under the treaty take-hold path. > > **Inputs**: > > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Treaty Effective Reallocation Share (Year 15)](#sec-treaty_effective_reallocation_share_year_15) 🔢: 4.4% > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > - [Peace Dividend Conflict Elasticity](#sec-peace_dividend_conflict_elasticity): 1:1 (95% CI: 0.25:1 - 1.5:1) > > $$ \begin{gathered} g_{peace,treaty,15} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,15}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Peace Recovery GDP Growth Bonus (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Effective Reallocation Share (Year 15) (rate) | 0.9545 | Strong driver | > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 0.2909 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Peace Recovery GDP Growth Bonus (Year 15) (10,000 simulations) > > **Simulation Results Summary: Treaty Peace Recovery GDP Growth Bonus (Year 15)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 0.435% | > | Mean (expected value) | 0.424% | > | Median (50th percentile) | 0.431% | > | Standard Deviation | 0.114% | > | 90% Range (5th-95th percentile) | \[0.2%, 0.592%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Peace Recovery GDP Growth Bonus (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Peace Recovery GDP Growth Bonus (Year 15) > > *This exceedance probability chart shows the likelihood that Treaty Peace Recovery GDP Growth Bonus (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Peace Recovery GDP Growth Bonus (Year 20): 0.573% > **NOTE:** > > Annual GDP growth bonus by year 20 from explicit avoided war-cost drag under the treaty take-hold path. > > **Inputs**: > > - [Annual Peace Dividend from 1% Reduction in Total War Costs](#sec-peace_dividend_annual_societal_benefit) 🔢: \$114 billion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Treaty Effective Reallocation Share (Year 20)](#sec-treaty_effective_reallocation_share_year_20) 🔢: 5.8% > - [1% Reduction in Military Spending/War Costs from Treaty](#sec-treaty_reduction_pct): 1% > - [Peace Dividend Conflict Elasticity](#sec-peace_dividend_conflict_elasticity): 1:1 (95% CI: 0.25:1 - 1.5:1) > > $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Peace Recovery GDP Growth Bonus (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Effective Reallocation Share (Year 20) (rate) | 0.9717 | Strong driver | > | Annual Peace Dividend from 1% Reduction in Total War Costs (USD/year) | 0.2271 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Peace Recovery GDP Growth Bonus (Year 20) (10,000 simulations) > > **Simulation Results Summary: Treaty Peace Recovery GDP Growth Bonus (Year 20)** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 0.573% | > | Mean (expected value) | 0.566% | > | Median (50th percentile) | 0.569% | > | Standard Deviation | 0.194% | > | 90% Range (5th-95th percentile) | \[0.211%, 0.877%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Peace Recovery GDP Growth Bonus (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Peace Recovery GDP Growth Bonus (Year 20) > > *This exceedance probability chart shows the likelihood that Treaty Peace Recovery GDP Growth Bonus (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Personal Upside (Income + Health): \$2.93 million > **NOTE:** > > Personal upside under Treaty Trajectory: lifetime income gain plus valued healthy-life gains. > > **Inputs**: > > - [Treaty Trajectory Lifetime Income Gain (Per Capita)](#sec-treaty_trajectory_lifetime_income_gain_per_capita) 🔢: \$518,879 > - [Treaty HALE Value Per Capita](#sec-treaty_hale_value_per_capita) 🔢: \$2.42 million > > $$ \begin{gathered} Upside_{income+health,treaty} \\ = \Delta Y_{lifetime,treaty} + Value_{HALE,treaty} \\ = \$519K + \$2.42M \\ = \$2.93M \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} Value_{HALE,treaty} \\ = \Delta HALE_{treaty,15} \times Value_{QALY} \\ = 16.1 \times \$150K \\ = \$2.42M \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \Delta_{HALE} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Personal Upside (Income + Health)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty HALE Value Per Capita (USD/person) | 0.9609 | Strong driver | > | Treaty Trajectory Lifetime Income Gain (Per Capita) (USD) | 0.1566 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Personal Upside (Income + Health) (10,000 simulations) > > **Simulation Results Summary: Treaty Personal Upside (Income + Health)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$2.93 million | > | Mean (expected value) | \$2.78 million | > | Median (50th percentile) | \$2.58 million | > | Standard Deviation | \$1.21 million | > | 90% Range (5th-95th percentile) | \[\$1.29 million, \$4.91 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Personal Upside (Income + Health); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Personal Upside (Income + Health) > > *This exceedance probability chart shows the likelihood that Treaty Personal Upside (Income + Health) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Projected HALE at Year 15: 79.4 years > **NOTE:** > > Projected global HALE at year 15 under Treaty Trajectory. Current HALE plus the treaty-driven improvement from closing the disease gap. > > **Inputs**: > > - [Global Healthy Life Expectancy (HALE)](#sec-global_hale_current) 📊: 63.3 years (SE: ±1.5 years) > - [Treaty HALE Gain at Year 15](#sec-treaty_hale_gain_year_15) 🔢: 16.1 years > > $$ \begin{gathered} HALE_{treaty,15} \\ = HALE_{0} + \Delta HALE_{treaty,15} \\ = 63.3 + 16.1 \\ = 79.4 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \Delta_{HALE} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{treaty,longevity,15} \\ = T_{extend} \times \rho_{HALE,15} \times f_{cure,15,treaty} \\ = 20 \times 30\% \times 100\% \\ = 6 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Projected HALE at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty HALE Gain at Year 15 (years) | 1.0158 | Strong driver | > | Global Healthy Life Expectancy (HALE) (years) | 0.2166 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Projected HALE at Year 15 (10,000 simulations) > > **Simulation Results Summary: Treaty Projected HALE at Year 15** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 79.4 | > | Mean (expected value) | 78.3 | > | Median (50th percentile) | 77 | > | Standard Deviation | 6.97 | > | 90% Range (5th-95th percentile) | \[70.2, 90.7\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Projected HALE at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Projected HALE at Year 15 > > *This exceedance probability chart shows the likelihood that Treaty Projected HALE at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Redirect GDP Growth Bonus (Year 15): 0.807% > **NOTE:** > > Annual GDP growth bonus by year 15 from redirecting military spending to medical research under the treaty take-hold path, including R&D spillovers. > > **Inputs**: > > - [Treaty Effective Reallocation Share (Year 15)](#sec-treaty_effective_reallocation_share_year_15) 🔢: 4.4% > - [GDP Growth Boost at 30% Military Reallocation](#sec-military_redirect_gdp_boost_at_30pct): 5.5% (95% CI: 3.5% - 7.5%) > - [R&D Spillover Multiplier](#sec-rd_spillover_multiplier): 2x (95% CI: 1.5x - 2.5x) > > $$ \begin{gathered} g_{redirect,treaty,15} \\ = \bar{s}_{treaty,15} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 4.4\% \times 5.5\% \times 2 \times 1.67 \\ = 0.807\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Redirect GDP Growth Bonus (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Effective Reallocation Share (Year 15) (rate) | 0.7551 | Strong driver | > | GDP Growth Boost at 30% Military Reallocation (rate) | 0.5154 | Strong driver | > | R&D Spillover Multiplier (x) | 0.3510 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Redirect GDP Growth Bonus (Year 15) (10,000 simulations) > > **Simulation Results Summary: Treaty Redirect GDP Growth Bonus (Year 15)** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 0.807% | > | Mean (expected value) | 0.789% | > | Median (50th percentile) | 0.783% | > | Standard Deviation | 0.266% | > | 90% Range (5th-95th percentile) | \[0.342%, 1.24%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Redirect GDP Growth Bonus (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Redirect GDP Growth Bonus (Year 15) > > *This exceedance probability chart shows the likelihood that Treaty Redirect GDP Growth Bonus (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Redirect GDP Growth Bonus (Year 20): 1.06% > **NOTE:** > > Annual GDP growth bonus by year 20 from redirecting military spending to medical research under the treaty take-hold path, including R&D spillovers. > > **Inputs**: > > - [Treaty Effective Reallocation Share (Year 20)](#sec-treaty_effective_reallocation_share_year_20) 🔢: 5.8% > - [GDP Growth Boost at 30% Military Reallocation](#sec-military_redirect_gdp_boost_at_30pct): 5.5% (95% CI: 3.5% - 7.5%) > - [R&D Spillover Multiplier](#sec-rd_spillover_multiplier): 2x (95% CI: 1.5x - 2.5x) > > $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Redirect GDP Growth Bonus (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Effective Reallocation Share (Year 20) (rate) | 0.8265 | Strong driver | > | GDP Growth Boost at 30% Military Reallocation (rate) | 0.4320 | Moderate driver | > | R&D Spillover Multiplier (x) | 0.2939 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Redirect GDP Growth Bonus (Year 20) (10,000 simulations) > > **Simulation Results Summary: Treaty Redirect GDP Growth Bonus (Year 20)** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 1.06% | > | Mean (expected value) | 1.05% | > | Median (50th percentile) | 1.03% | > | Standard Deviation | 0.424% | > | 90% Range (5th-95th percentile) | \[0.363%, 1.79%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Redirect GDP Growth Bonus (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Redirect GDP Growth Bonus (Year 20) > > *This exceedance probability chart shows the likelihood that Treaty Redirect GDP Growth Bonus (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty ROI - Historical Rate (Existing Drugs): 290 thousand:1 > **NOTE:** > > Treaty ROI based on historical rate of drug development (existing drugs only). Total one-time benefit from avoiding regulatory delay for drugs already in development divided by campaign cost. Excludes future innovation effects. > > **Inputs**: > > - [Total Economic Loss from Historical Progress Delays](#sec-existing_drugs_efficacy_lag_economic_loss) 🔢: \$290 trillion > - [Total 1% Treaty Campaign Cost](#sec-treaty_campaign_total_cost) 🔢: \$1 billion > > $$ \begin{gathered} ROI_{drugs} \\ = \frac{Loss_{lag}}{Cost_{campaign}} \\ = \frac{\$290T}{\$1B} \\ = 290{,}000 \end{gathered} $$ where: $$ \begin{gathered} Loss_{lag} \\ = \text{DEATHS\_TOTAL} \times (REMAINING_LIFE_EXPECTANCY_AT_60 - (\text{MEAN\_AGE\_OF\_DEATH} - 60)) \times \text{VSLY} \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \end{gathered} $$ where: $$ \begin{gathered} Lives_{saved,annual} \\ = \frac{LY_{saved,annual}}{T_{ext}} \\ = \frac{149M}{12} \\ = 12.4M \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty ROI - Historical Rate (Existing Drugs)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Economic Loss from Historical Progress Delays (USD) | 0.8671 | Strong driver | > | Total 1% Treaty Campaign Cost (USD) | -0.4297 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty ROI - Historical Rate (Existing Drugs) (10,000 simulations) > > **Simulation Results Summary: Treaty ROI - Historical Rate (Existing Drugs)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | 290 thousand:1 | > | Mean (expected value) | 305 thousand:1 | > | Median (50th percentile) | 274 thousand:1 | > | Standard Deviation | 156 thousand:1 | > | 90% Range (5th-95th percentile) | \[117 thousand:1, 600 thousand:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty ROI - Historical Rate (Existing Drugs); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty ROI - Historical Rate (Existing Drugs) > > *This exceedance probability chart shows the likelihood that Treaty ROI - Historical Rate (Existing Drugs) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput: 7.53 million:1 > **NOTE:** > > Treaty ROI from elimination of efficacy lag plus earlier treatment discovery from increased trial throughput. Total one-time benefit divided by campaign cost. This is the primary ROI estimate for total health benefits. > > **Inputs**: > > - [Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_economic_value) 🔢: \$7.53 quadrillion > - [Total 1% Treaty Campaign Cost](#sec-treaty_campaign_total_cost) 🔢: \$1 billion > > $$ \begin{gathered} ROI_{max} \\ = \frac{Value_{max}}{Cost_{campaign}} \\ = \frac{\$7530T}{\$1B} \\ = 7.53M \end{gathered} $$ where: $$ \begin{gathered} Value_{max} \\ = DALYs_{max}^{PV} \times Value_{QALY} \\ = 50.2B \times \$150K \\ = \$7530T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.8350 | Strong driver | > | Total 1% Treaty Campaign Cost (USD) | -0.4848 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput (10,000 simulations) > > **Simulation Results Summary: Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | 7.53 million:1 | > | Mean (expected value) | 8.27 million:1 | > | Median (50th percentile) | 7.83 million:1 | > | Standard Deviation | 3.75 million:1 | > | 90% Range (5th-95th percentile) | \[2.96 million:1, 15.1 million:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput > > *This exceedance probability chart shows the likelihood that Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory Average Income at Year 15: \$26,713 > **NOTE:** > > Average income (GDP per capita) at year 15 under the Treaty Trajectory. > > **Inputs**: > > - [Treaty Trajectory GDP at Year 15](#sec-treaty_trajectory_gdp_year_15) 🔢: \$238 trillion > - [Global Population 2040 (Projected)](#sec-global_population_2040_projected) 📊: 8.9 billion of people > > $$ \begin{gathered} \bar{y}_{treaty,15} \\ = \frac{GDP_{treaty,15}}{Pop_{2040}} \\ = \frac{\$238T}{8.9B} \\ = \$26.7K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,15} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,15} \\ + g_{peace,treaty,15} + g_{cyber,treaty,15} \\ + g_{health,treaty,15})^{15} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,15} \\ = \bar{s}_{treaty,15} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 4.4\% \times 5.5\% \times 2 \times 1.67 \\ = 0.807\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,15} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,15}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,15} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,15} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,15} \\ = \frac{f_{cure,15,treaty} - d_{disease}}{-7.22} \\ = \frac{100\% - 13\%}{-7.22} \\ = 0.818\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory Average Income at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory GDP at Year 15 (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory Average Income at Year 15 (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory Average Income at Year 15** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$26,713 | > | Mean (expected value) | \$26,442 | > | Median (50th percentile) | \$26,559 | > | Standard Deviation | \$1,886 | > | 90% Range (5th-95th percentile) | \[\$22,882, \$29,274\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory Average Income at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory Average Income at Year 15 > > *This exceedance probability chart shows the likelihood that Treaty Trajectory Average Income at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory Average Income at Year 20: \$34,972 > **NOTE:** > > Average income (GDP per capita) at year 20 under the Treaty Trajectory. > > **Inputs**: > > - [Treaty Trajectory GDP at Year 20](#sec-treaty_trajectory_gdp_year_20) 🔢: \$322 trillion > - [Global Population 2045 (Projected)](#sec-global_population_2045_projected) 📊: 9.2 billion of people > > $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory Average Income at Year 20** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory GDP at Year 20 (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory Average Income at Year 20 (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory Average Income at Year 20** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$34,972 | > | Mean (expected value) | \$35,084 | > | Median (50th percentile) | \$34,830 | > | Standard Deviation | \$5,149 | > | 90% Range (5th-95th percentile) | \[\$26,587, \$43,903\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory Average Income at Year 20; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory Average Income at Year 20 > > *This exceedance probability chart shows the likelihood that Treaty Trajectory Average Income at Year 20 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory CAGR (20 Years): 5.28% > **NOTE:** > > Compound annual growth rate implied by Treaty Trajectory GDP trajectory over 20 years. > > **Inputs**: > > - [Treaty Trajectory GDP at Year 20](#sec-treaty_trajectory_gdp_year_20) 🔢: \$322 trillion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > > $$ \begin{gathered} g_{treaty,CAGR} \\ = \left(\frac{GDP_{treaty,20}}{GDP_{global}}\right)^{\frac{1}{20}} - 1 \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory CAGR (20 Years)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory GDP at Year 20 (USD) | 0.9948 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory CAGR (20 Years) (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory CAGR (20 Years)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 5.28% | > | Mean (expected value) | 5.24% | > | Median (50th percentile) | 5.26% | > | Standard Deviation | 0.783% | > | 90% Range (5th-95th percentile) | \[3.85%, 6.48%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory CAGR (20 Years); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory CAGR (20 Years) > > *This exceedance probability chart shows the likelihood that Treaty Trajectory CAGR (20 Years) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory Cumulative Lifetime Income (Per Capita): \$1.42 million > **NOTE:** > > Cumulative per-capita income over an average remaining lifespan under Treaty Trajectory. Uses implied per-capita CAGR for years 1-20 (derived from known year-0 and year-20 per-capita incomes), then current-trajectory per-capita growth from the year-20 level. Conservative: assumes no further treaty acceleration beyond year 20. > > **Inputs**: > > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > - [Treaty Trajectory Average Income at Year 20](#sec-treaty_trajectory_avg_income_year_20) 🔢: \$34,972 > - [Current Trajectory Average Income at Year 20](#sec-current_trajectory_avg_income_year_20) 🔢: \$20,483 > - [Average Remaining Years (Median Person)](#sec-global_avg_remaining_years) 🔢: 42.9 years > > $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory Cumulative Lifetime Income (Per Capita)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Average Income at Year 20 (USD) | 0.8700 | Strong driver | > | Average Remaining Years (Median Person) (years) | 0.4772 | Moderate driver | > | Global Average Income (2025 Baseline) (USD) | -0.0245 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory Cumulative Lifetime Income (Per Capita) (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory Cumulative Lifetime Income (Per Capita)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$1.42 million | > | Mean (expected value) | \$1.45 million | > | Median (50th percentile) | \$1.44 million | > | Standard Deviation | \$212,011 | > | 90% Range (5th-95th percentile) | \[\$1.11 million, \$1.82 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory Cumulative Lifetime Income (Per Capita); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory Cumulative Lifetime Income (Per Capita) > > *This exceedance probability chart shows the likelihood that Treaty Trajectory Cumulative Lifetime Income (Per Capita) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15): 1.43x > **NOTE:** > > Treaty Trajectory GDP at year 15 as a multiple of current trajectory GDP at year 15. > > **Inputs**: > > - [Treaty Trajectory GDP at Year 15](#sec-treaty_trajectory_gdp_year_15) 🔢: \$238 trillion > - [Current Trajectory GDP at Year 15](#sec-current_trajectory_gdp_year_15) 🔢: \$167 trillion > > $$ \begin{gathered} k_{treaty:base,15} \\ = \frac{GDP_{treaty,15}}{GDP_{base,15}} \\ = \frac{\$238T}{\$167T} \\ = 1.43 \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,15} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,15} \\ + g_{peace,treaty,15} + g_{cyber,treaty,15} \\ + g_{health,treaty,15})^{15} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,15} \\ = \bar{s}_{treaty,15} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 4.4\% \times 5.5\% \times 2 \times 1.67 \\ = 0.807\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,15} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,15}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,15} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,15} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,15} \\ = \frac{f_{cure,15,treaty} - d_{disease}}{-7.22} \\ = \frac{100\% - 13\%}{-7.22} \\ = 0.818\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ GDP_{base,15} = GDP_{global} \times (1 + g_{base})^{15} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory GDP at Year 15 (USD) | 1.0000 | Strong driver | > | Current Trajectory GDP at Year 15 (USD) | 0.0000 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15) (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 1.43x | > | Mean (expected value) | 1.41x | > | Median (50th percentile) | 1.42x | > | Standard Deviation | 0.101x | > | 90% Range (5th-95th percentile) | \[1.22x, 1.56x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15) > > *This exceedance probability chart shows the likelihood that Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20): 1.71x > **NOTE:** > > Treaty Trajectory GDP at year 20 as a multiple of current trajectory GDP at year 20. > > **Inputs**: > > - [Treaty Trajectory GDP at Year 20](#sec-treaty_trajectory_gdp_year_20) 🔢: \$322 trillion > - [Current Trajectory GDP at Year 20](#sec-current_trajectory_gdp_year_20) 🔢: \$188 trillion > > $$ \begin{gathered} k_{treaty:base,20} \\ = \frac{GDP_{treaty,20}}{GDP_{base,20}} \\ = \frac{\$322T}{\$188T} \\ = 1.71 \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory GDP at Year 20 (USD) | 1.0000 | Strong driver | > | Current Trajectory GDP at Year 20 (USD) | 0.0000 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20) (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20)** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 1.71x | > | Mean (expected value) | 1.71x | > | Median (50th percentile) | 1.7x | > | Standard Deviation | 0.251x | > | 90% Range (5th-95th percentile) | \[1.3x, 2.14x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20) > > *This exceedance probability chart shows the likelihood that Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory GDP at Year 15: \$238 trillion > **NOTE:** > > Projected global GDP at year 15 under the optimistic treaty take-hold path. Compounds baseline growth plus explicit military redirect spillovers, peace dividend recovery, cybercrime drag recovery, and health recovery from disease cures and faster deployment. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Treaty Redirect GDP Growth Bonus (Year 15)](#sec-treaty_redirect_gdp_growth_bonus_year_15) 🔢: 0.807% > - [Treaty Peace Recovery GDP Growth Bonus (Year 15)](#sec-treaty_peace_recovery_gdp_growth_bonus_year_15) 🔢: 0.435% > - [Treaty Cybercrime Recovery GDP Growth Bonus (Year 15)](#sec-treaty_cybercrime_recovery_gdp_growth_bonus_year_15) 🔢: 0.402% > - [Treaty Health Recovery GDP Growth Bonus (Year 15)](#sec-treaty_health_recovery_gdp_growth_bonus_year_15) 🔢: 0.818% > > $$ \begin{gathered} GDP_{treaty,15} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,15} \\ + g_{peace,treaty,15} + g_{cyber,treaty,15} \\ + g_{health,treaty,15})^{15} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,15} \\ = \bar{s}_{treaty,15} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 4.4\% \times 5.5\% \times 2 \times 1.67 \\ = 0.807\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,15} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,15}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,15} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,15} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,15} \\ = \frac{f_{cure,15,treaty} - d_{disease}}{-7.22} \\ = \frac{100\% - 13\%}{-7.22} \\ = 0.818\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory GDP at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Redirect GDP Growth Bonus (Year 15) (rate) | 0.5505 | Strong driver | > | Treaty Health Recovery GDP Growth Bonus (Year 15) (rate) | 0.2367 | Weak driver | > | Treaty Peace Recovery GDP Growth Bonus (Year 15) (rate) | 0.2337 | Weak driver | > | Treaty Cybercrime Recovery GDP Growth Bonus (Year 15) (rate) | 0.1650 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory GDP at Year 15 (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory GDP at Year 15** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$238 trillion | > | Mean (expected value) | \$235 trillion | > | Median (50th percentile) | \$236 trillion | > | Standard Deviation | \$16.8 trillion | > | 90% Range (5th-95th percentile) | \[\$204 trillion, \$261 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory GDP at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory GDP at Year 15 > > *This exceedance probability chart shows the likelihood that Treaty Trajectory GDP at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory GDP at Year 20: \$322 trillion > **NOTE:** > > Projected global GDP at year 20 under the optimistic treaty take-hold path. Compounds baseline growth plus explicit military redirect spillovers, peace dividend recovery, cybercrime drag recovery, and health recovery from lower disease burden. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Treaty Redirect GDP Growth Bonus (Year 20)](#sec-treaty_redirect_gdp_growth_bonus_year_20) 🔢: 1.06% > - [Treaty Peace Recovery GDP Growth Bonus (Year 20)](#sec-treaty_peace_recovery_gdp_growth_bonus_year_20) 🔢: 0.573% > - [Treaty Cybercrime Recovery GDP Growth Bonus (Year 20)](#sec-treaty_cybercrime_recovery_gdp_growth_bonus_year_20) 🔢: 0.53% > - [Treaty Health Recovery GDP Growth Bonus (Year 20)](#sec-treaty_health_recovery_gdp_growth_bonus_year_20) 🔢: 0.613% > > $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory GDP at Year 20** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Redirect GDP Growth Bonus (Year 20) (rate) | 0.5961 | Strong driver | > | Treaty Peace Recovery GDP Growth Bonus (Year 20) (rate) | 0.2722 | Weak driver | > | Treaty Cybercrime Recovery GDP Growth Bonus (Year 20) (rate) | 0.1642 | Weak driver | > | Treaty Health Recovery GDP Growth Bonus (Year 20) (rate) | 0.0317 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory GDP at Year 20 (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory GDP at Year 20** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$322 trillion | > | Mean (expected value) | \$323 trillion | > | Median (50th percentile) | \$320 trillion | > | Standard Deviation | \$47.4 trillion | > | 90% Range (5th-95th percentile) | \[\$245 trillion, \$404 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory GDP at Year 20; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory GDP at Year 20 > > *This exceedance probability chart shows the likelihood that Treaty Trajectory GDP at Year 20 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory Lifetime Income Gain (Per Capita): \$518,879 > **NOTE:** > > Lifetime per-capita income gain from Treaty Trajectory vs current trajectory. Cumulative treaty income minus cumulative earth income over average remaining lifespan. Uses global averages; individual gain scales with starting income. > > **Inputs**: > > - [Treaty Trajectory Cumulative Lifetime Income (Per Capita)](#sec-treaty_trajectory_cumulative_lifetime_income) 🔢: \$1.42 million > - [Current Trajectory Cumulative Lifetime Income (Per Capita)](#sec-current_trajectory_cumulative_lifetime_income) 🔢: \$903,908 > > $$ \begin{gathered} \Delta Y_{lifetime,treaty} \\ = Y_{cum,treaty} - Y_{cum,earth} \\ = \$1.42M - \$904K \\ = \$519K \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory Lifetime Income Gain (Per Capita)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Cumulative Lifetime Income (Per Capita) (USD) | 1.1148 | Strong driver | > | Current Trajectory Cumulative Lifetime Income (Per Capita) (USD) | -0.3147 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory Lifetime Income Gain (Per Capita) (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory Lifetime Income Gain (Per Capita)** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | \$518,879 | > | Mean (expected value) | \$532,105 | > | Median (50th percentile) | \$521,963 | > | Standard Deviation | \$190,180 | > | 90% Range (5th-95th percentile) | \[\$221,703, \$860,930\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory Lifetime Income Gain (Per Capita); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory Lifetime Income Gain (Per Capita) > > *This exceedance probability chart shows the likelihood that Treaty Trajectory Lifetime Income Gain (Per Capita) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Trajectory Lifetime Income Multiplier: 1.57x > **NOTE:** > > Ratio of cumulative lifetime income under Treaty Trajectory vs current trajectory. Income-agnostic: applies as a multiplier to any individual’s lifetime earnings. > > **Inputs**: > > - [Treaty Trajectory Cumulative Lifetime Income (Per Capita)](#sec-treaty_trajectory_cumulative_lifetime_income) 🔢: \$1.42 million > - [Current Trajectory Cumulative Lifetime Income (Per Capita)](#sec-current_trajectory_cumulative_lifetime_income) 🔢: \$903,908 > > $$ \begin{gathered} k_{lifetime,treaty:earth} \\ = \frac{Y_{cum,treaty}}{Y_{cum,earth}} \\ = \frac{\$1.42M}{\$904K} \\ = 1.57 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,treaty} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,treaty})((1+g_{pc,treaty})^{20}-1)}{g_{pc,treaty}} \\ + \bar{y}_{treaty,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Trajectory Lifetime Income Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Cumulative Lifetime Income (Per Capita) (USD) | 1.1413 | Strong driver | > | Current Trajectory Cumulative Lifetime Income (Per Capita) (USD) | -0.5084 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Trajectory Lifetime Income Multiplier (10,000 simulations) > > **Simulation Results Summary: Treaty Trajectory Lifetime Income Multiplier** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 1.57x | > | Mean (expected value) | 1.58x | > | Median (50th percentile) | 1.57x | > | Standard Deviation | 0.201x | > | 90% Range (5th-95th percentile) | \[1.24x, 1.92x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Trajectory Lifetime Income Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Trajectory Lifetime Income Multiplier > > *This exceedance probability chart shows the likelihood that Treaty Trajectory Lifetime Income Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Median After-Tax Consumable Income, Treaty (Year 15): \$4,381 > **NOTE:** > > Median after-tax consumable income at year 15 under the Treaty Trajectory. Same construction as the year-20 variant: military share reduced by the ratchet’s terminal redirect (the single ratchet knob), same erosion as the status quo (best guess zero) so distributional claims cancel, plus the WHO-anchored health-burden relief channel scaled by the ratchet-schedule cure fraction. Feeds the Prize settlement target. > > **Inputs**: > > - [Treaty Trajectory Average Income at Year 15](#sec-treaty_trajectory_avg_income_year_15) 🔢: \$26,713 > - [Military Share of Global GDP](#sec-global_military_share_of_gdp) 🔢: 2.37% > - [Treaty Ratchet Terminal Redirect Share](#sec-treaty_ratchet_terminal_share): 10% (95% CI: 1% - 19%) > - [Global Median-to-Mean Income Ratio](#sec-global_median_to_mean_income_ratio) 🔢: 0.203:1 > - [Median Share Erosion Rate (Annual)](#sec-global_median_share_erosion_rate_annual): 0% (95% CI: -0.5% - 0.78%) > - [Median Income Relief from Full Disease Cure](#sec-median_health_burden_relief_share): 10% (95% CI: 5% - 15%) > - [Treaty Disease Cure Fraction (15yr, Ratchet Schedule)](#sec-treaty_disease_cure_fraction_15yr) 🔢: 100% > - [Effective Tax Rate on Median Earner](#sec-global_effective_tax_rate_median): 25% > > $$ \begin{gathered} \tilde{m}_{treaty,15} \\ = \bar{y}_{treaty,15} \times (1 - s_{mil} \times (1 - s_{ratchet})) \times \rho_{med} \times (1 - e_{med})^{15} \times (1 \\ + r_{relief} \times f_{cure,15,treaty}) \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,15} \\ = \frac{GDP_{treaty,15}}{Pop_{2040}} \\ = \frac{\$238T}{8.9B} \\ = \$26.7K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,15} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,15} \\ + g_{peace,treaty,15} + g_{cyber,treaty,15} \\ + g_{health,treaty,15})^{15} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,15} \\ = \bar{s}_{treaty,15} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 4.4\% \times 5.5\% \times 2 \times 1.67 \\ = 0.807\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,15} \\ = s_{ratchet} \times 0.212 \\ = 10\% \times 0.212 \\ = 4.4\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,15} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,15}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,15} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,15} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,15} \\ = \frac{f_{cure,15,treaty} - d_{disease}}{-7.22} \\ = \frac{100\% - 13\%}{-7.22} \\ = 0.818\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 3 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ where: $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Median After-Tax Consumable Income, Treaty (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Median-to-Mean Income Ratio (ratio) | 0.7769 | Strong driver | > | Treaty Trajectory Average Income at Year 15 (USD) | 0.4740 | Moderate driver | > | Median Share Erosion Rate (Annual) (rate) | -0.3085 | Moderate driver | > | Median Income Relief from Full Disease Cure (rate) | 0.1400 | Weak driver | > | Treaty Disease Cure Fraction (15yr, Ratchet Schedule) (rate) | 0.0933 | Minimal effect | > | Treaty Ratchet Terminal Redirect Share (percent) | 0.0079 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Median After-Tax Consumable Income, Treaty (Year 15) (10,000 simulations) > > **Simulation Results Summary: Median After-Tax Consumable Income, Treaty (Year 15)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$4,381 | > | Mean (expected value) | \$4,312 | > | Median (50th percentile) | \$4,291 | > | Standard Deviation | \$646 | > | 90% Range (5th-95th percentile) | \[\$3,292, \$5,411\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Median After-Tax Consumable Income, Treaty (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Median After-Tax Consumable Income, Treaty (Year 15) > > *This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Treaty (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Median After-Tax Consumable Income, Treaty (Year 20): \$5,736 > **NOTE:** > > Median after-tax consumable income at year 20 under the Treaty Trajectory. Mean income from the treaty GDP trajectory; military share reduced by the ratchet’s terminal redirect (the single ratchet knob); the same share erosion as the status quo (best guess zero) so distributional claims cancel out of the multiplier; plus the one pro-median channel: cures return out-of-pocket health costs and sick wages to the people who bear them (WHO-anchored relief share scaled by the ratchet-schedule cure fraction). > > **Inputs**: > > - [Treaty Trajectory Average Income at Year 20](#sec-treaty_trajectory_avg_income_year_20) 🔢: \$34,972 > - [Military Share of Global GDP](#sec-global_military_share_of_gdp) 🔢: 2.37% > - [Treaty Ratchet Terminal Redirect Share](#sec-treaty_ratchet_terminal_share): 10% (95% CI: 1% - 19%) > - [Global Median-to-Mean Income Ratio](#sec-global_median_to_mean_income_ratio) 🔢: 0.203:1 > - [Median Share Erosion Rate (Annual)](#sec-global_median_share_erosion_rate_annual): 0% (95% CI: -0.5% - 0.78%) > - [Median Income Relief from Full Disease Cure](#sec-median_health_burden_relief_share): 10% (95% CI: 5% - 15%) > - [Treaty Disease Cure Fraction (20yr, Ratchet Schedule)](#sec-treaty_disease_cure_fraction_20yr) 🔢: 100% > - [Effective Tax Rate on Median Earner](#sec-global_effective_tax_rate_median): 25% > > $$ \begin{gathered} \tilde{m}_{treaty,20} \\ = \bar{y}_{treaty,20} \times (1 - s_{mil} \times (1 - s_{ratchet})) \times \rho_{med} \times (1 - e_{med})^{20} \times (1 \\ + r_{relief} \times f_{cure,20,treaty}) \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ where: $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Median After-Tax Consumable Income, Treaty (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Treaty Trajectory Average Income at Year 20 (USD) | 0.7300 | Strong driver | > | Global Median-to-Mean Income Ratio (ratio) | 0.5792 | Strong driver | > | Median Share Erosion Rate (Annual) (rate) | -0.3062 | Moderate driver | > | Median Income Relief from Full Disease Cure (rate) | 0.1081 | Weak driver | > | Treaty Disease Cure Fraction (20yr, Ratchet Schedule) (rate) | 0.0391 | Minimal effect | > | Treaty Ratchet Terminal Redirect Share (percent) | 0.0055 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Median After-Tax Consumable Income, Treaty (Year 20) (10,000 simulations) > > **Simulation Results Summary: Median After-Tax Consumable Income, Treaty (Year 20)** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | \$5,736 | > | Mean (expected value) | \$5,742 | > | Median (50th percentile) | \$5,668 | > | Standard Deviation | \$1,154 | > | 90% Range (5th-95th percentile) | \[\$3,967, \$7,768\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Median After-Tax Consumable Income, Treaty (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Median After-Tax Consumable Income, Treaty (Year 20) > > *This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Treaty (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cost-Effectiveness vs Bed Nets Multiplier: 9.2kx > **NOTE:** > > How many times more cost-effective than bed nets (using bed net cost per DALY midpoint estimate) > > **Inputs**: > > - [Bed Nets Cost per DALY](#sec-bed_nets_cost_per_daly) 🔢: \$184 > - [Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)](#sec-treaty_cost_per_daly_trial_capacity_plus_efficacy_lag) 🔢: \$0.02 > > $$ \begin{gathered} k_{treaty:nets} \\ = \frac{Cost_{nets}}{Cost_{treaty,DALY}} \\ = \frac{\$184}{\$0.0199} \\ = 9{,}210 \end{gathered} $$ where: $$ \begin{gathered} Cost_{nets} \\ = Cost_{GW,max} / ((1 - (1 \\ + r_{discount})^{-LE_{global}}) / \ln\left(1 + r_{discount}\right)) \end{gathered} $$ where: $$ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}^{PV}} \\ = \frac{\$1B}{50.2B} \\ = \$0.0199 \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cost-Effectiveness vs Bed Nets Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) (USD/DALY) | -0.6959 | Strong driver | > | Bed Nets Cost per DALY (USD/DALY) | 0.4712 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cost-Effectiveness vs Bed Nets Multiplier (10,000 simulations) > > **Simulation Results Summary: Cost-Effectiveness vs Bed Nets Multiplier** > > | Statistic | Value | > |:--------------------------------|------------------:| > | Baseline (deterministic) | 9.2kx | > | Mean (expected value) | 10.1kx | > | Median (50th percentile) | 9.5kx | > | Standard Deviation | 4.8kx | > | 90% Range (5th-95th percentile) | \[3.5kx, 18.9kx\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cost-Effectiveness vs Bed Nets Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cost-Effectiveness vs Bed Nets Multiplier > > *This exceedance probability chart shows the likelihood that Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty vs Status Quo Median Income Multiplier (Year 20): 1.89x > **NOTE:** > > Median after-tax income at year 20: Treaty vs status quo. Larger than the GDP multiplier for two auditable reasons: the treaty cuts the military deduction by the ratchet’s terminal redirect while the status-quo military share drifts up on its measured trend, and the WHO-anchored health-burden relief channel applies only on the treaty branch. Share erosion is identical in both branches and cancels out of this ratio by construction. > > **Inputs**: > > - [Median After-Tax Consumable Income, Treaty (Year 20)](#sec-treaty_trajectory_median_after_tax_income_year_20) 🔢: \$5,736 > - [Median After-Tax Consumable Income, Status Quo (Year 20)](#sec-current_trajectory_median_after_tax_income_year_20) 🔢: \$3,033 > > $$ \begin{gathered} k_{med,treaty:base} \\ = \frac{\tilde{m}_{treaty,20}}{\tilde{m}_{base,20}} \\ = \frac{\$5.74K}{\$3.03K} \\ = 1.89 \end{gathered} $$ where: $$ \begin{gathered} \tilde{m}_{treaty,20} \\ = \bar{y}_{treaty,20} \times (1 - s_{mil} \times (1 - s_{ratchet})) \times \rho_{med} \times (1 - e_{med})^{20} \times (1 \\ + r_{relief} \times f_{cure,20,treaty}) \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{treaty,20} \\ = \frac{GDP_{treaty,20}}{Pop_{2045}} \\ = \frac{\$322T}{9.2B} \\ = \$35K \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ where: $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \tilde{m}_{base,20} \\ = \bar{y}_{base,20} \times (1 - s_{mil} \times \left(\frac{1+g_{mil,10yr}}{1+g_{base}}\right)^{20}) \times \rho_{med} \times (1 - e_{med})^{20} \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty vs Status Quo Median Income Multiplier (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Median After-Tax Consumable Income, Treaty (Year 20) (USD) | 1.2956 | Strong driver | > | Median After-Tax Consumable Income, Status Quo (Year 20) (USD) | -0.8490 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty vs Status Quo Median Income Multiplier (Year 20) (10,000 simulations) > > **Simulation Results Summary: Treaty vs Status Quo Median Income Multiplier (Year 20)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 1.89x | > | Mean (expected value) | 1.89x | > | Median (50th percentile) | 1.88x | > | Standard Deviation | 0.289x | > | 90% Range (5th-95th percentile) | \[1.42x, 2.39x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty vs Status Quo Median Income Multiplier (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty vs Status Quo Median Income Multiplier (Year 20) > > *This exceedance probability chart shows the likelihood that Treaty vs Status Quo Median Income Multiplier (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Treaty Campaign Leverage vs Direct Funding: 483x > **NOTE:** > > How many times more cost-effective the treaty campaign is vs direct pragmatic trial funding. Treaty campaign unlocks government funding at scale, avoiding need for philanthropists/NIH to directly commit equivalent amounts. Both approaches achieve same DALY timeline shift benefit. Treaty spreads cost across governments while building sustainable public funding infrastructure. > > **Inputs**: > > - [Direct Pragmatic Trial Funding Cost per DALY](#sec-dfda_direct_funding_cost_per_daly) 🔢: \$9.62 > - [Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)](#sec-treaty_cost_per_daly_trial_capacity_plus_efficacy_lag) 🔢: \$0.02 > > $$ \begin{gathered} Leverage_{treaty} \\ = \frac{Cost_{direct,DALY}}{Cost_{treaty,DALY}} \\ = \frac{\$9.62}{\$0.0199} \\ = 483 \end{gathered} $$ where: $$ \begin{gathered} Cost_{direct,DALY} \\ = \frac{NPV_{direct}}{DALYs_{max}^{PV}} \\ = \frac{\$483B}{50.2B} \\ = \$9.62 \end{gathered} $$ where: $$ \begin{gathered} NPV_{direct} \\ = Funding_{trial,ref} \times (1 - (1 \\ + r_{discount})^{-T_{queue,trial}}) / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max}^{PV} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times [A(T_{queue,trial}) - (1 \\ + r_{discount})^{-T_{lag}} \times A(T_{queue,SQ})] / \ln\left(1 + r_{discount}\right) \end{gathered} $$ where: $$ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}^{PV}} \\ = \frac{\$1B}{50.2B} \\ = \$0.0199 \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Treaty Campaign Leverage vs Direct Funding** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Direct Pragmatic Trial Funding Cost per DALY (USD/DALY) | 1.6702 | Strong driver | > | Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) (USD/DALY) | -1.1331 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Treaty Campaign Leverage vs Direct Funding (10,000 simulations) > > **Simulation Results Summary: Treaty Campaign Leverage vs Direct Funding** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 483x | > | Mean (expected value) | 457x | > | Median (50th percentile) | 434x | > | Standard Deviation | 215x | > | 90% Range (5th-95th percentile) | \[151x, 843x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Treaty Campaign Leverage vs Direct Funding; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Treaty Campaign Leverage vs Direct Funding > > *This exceedance probability chart shows the likelihood that Treaty Campaign Leverage vs Direct Funding will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cumulative Trial Capacity Years Over 20 Years: 247 years > **NOTE:** > > Cumulative trial-capacity-equivalent years over 20-year period > > **Inputs**: > > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > > $$ \begin{gathered} Capacity_{20yr} \\ = k_{capacity} \times 20 \\ = 12.3 \times 20 \\ = 247 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cumulative Trial Capacity Years Over 20 Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding (x) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cumulative Trial Capacity Years Over 20 Years (10,000 simulations) > > **Simulation Results Summary: Cumulative Trial Capacity Years Over 20 Years** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 247 | > | Mean (expected value) | 409 | > | Median (50th percentile) | 319 | > | Standard Deviation | 320 | > | 90% Range (5th-95th percentile) | \[98.5, 1,015\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cumulative Trial Capacity Years Over 20 Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cumulative Trial Capacity Years Over 20 Years > > *This exceedance probability chart shows the likelihood that Cumulative Trial Capacity Years Over 20 Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Ratio of Type II Error Cost to Type I Error Benefit: 3,389:1 > **NOTE:** > > Ratio of Type II error cost to Type I error benefit (harm from delay vs. harm prevented) > > **Inputs**: > > - [Total DALYs Lost from Disease Eradication Delay](#sec-dfda_efficacy_lag_elimination_dalys) 🔢: 8.77 billion DALYs > - [Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)](#sec-type_i_error_benefit_dalys) 🔢: 2.59 million DALYs > > $$ \begin{gathered} Ratio_{TypeII} \\ = \frac{DALYs_{lag}}{DALY_{TypeI}} \\ = \frac{8.77B}{2.59M} \\ = 3{,}390 \end{gathered} $$ where: $$ DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.9B + 873M = 8.77B $$ where: $$ \begin{gathered} YLL_{lag} \\ = \text{DEATHS\_TOTAL} \times (REMAINING_LIFE_EXPECTANCY_AT_60 - (\text{MEAN\_AGE\_OF\_DEATH} - 60)) \end{gathered} $$ where: $$ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} $$ where: $$ \begin{gathered} YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \end{gathered} $$ where: $$ \begin{gathered} DALY_{TypeI} \\ = DALY_{thal} \times 62 \\ = 41{,}800 \times 62 \\ = 2.59M \end{gathered} $$ where: $$ \begin{gathered} DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \end{gathered} $$ where: $$ \begin{gathered} YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ where: $$ \begin{gathered} YLL_{thal} \\ = Deaths_{thal} \times 80 \\ = 360 \times 80 \\ = 28{,}800 \end{gathered} $$ where: $$ \begin{gathered} Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Ratio of Type II Error Cost to Type I Error Benefit** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total DALYs Lost from Disease Eradication Delay (DALYs) | 0.8413 | Strong driver | > | Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) (DALYs) | -0.4986 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Ratio of Type II Error Cost to Type I Error Benefit (10,000 simulations) > > **Simulation Results Summary: Ratio of Type II Error Cost to Type I Error Benefit** > > | Statistic | Value | > |:--------------------------------|---------------------:| > | Baseline (deterministic) | 3,389:1 | > | Mean (expected value) | 3,510:1 | > | Median (50th percentile) | 3,348:1 | > | Standard Deviation | 1,215:1 | > | 90% Range (5th-95th percentile) | \[1,811:1, 5,734:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Ratio of Type II Error Cost to Type I Error Benefit; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Ratio of Type II Error Cost to Type I Error Benefit > > *This exceedance probability chart shows the likelihood that Ratio of Type II Error Cost to Type I Error Benefit will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024): 2.59 million DALYs > **NOTE:** > > Maximum DALYs saved by FDA preventing unsafe drugs over 62-year period 1962-2024 (extreme overestimate: one Thalidomide-scale event per year) > > **Inputs**: > > - [Thalidomide DALYs Per Event](#sec-thalidomide_dalys_per_event) 🔢: 41,760 DALYs > > $$ \begin{gathered} DALY_{TypeI} \\ = DALY_{thal} \times 62 \\ = 41{,}800 \times 62 \\ = 2.59M \end{gathered} $$ where: $$ \begin{gathered} DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \end{gathered} $$ where: $$ \begin{gathered} YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \end{gathered} $$ where: $$ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} $$ where: $$ \begin{gathered} YLL_{thal} \\ = Deaths_{thal} \times 80 \\ = 360 \times 80 \\ = 28{,}800 \end{gathered} $$ where: $$ \begin{gathered} Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:------------------------------------|------------------------:|:---------------| > | Thalidomide DALYs Per Event (DALYs) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) (10,000 simulations) > > **Simulation Results Summary: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 2.59 million | > | Mean (expected value) | 2.58 million | > | Median (50th percentile) | 2.55 million | > | Standard Deviation | 448 thousand | > | 90% Range (5th-95th percentile) | \[1.88 million, 3.38 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) > > *This exceedance probability chart shows the likelihood that Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Unexplored Therapeutic Frontier: 99.7% > **NOTE:** > > Fraction of possible drug-disease space that remains unexplored (\>99%) > > **Inputs**: > > - [Tested Drug-Disease Relationships](#sec-tested_relationships_estimate): 32,500 relationships (95% CI: 15,000 relationships - 50,000 relationships) > - [Possible Drug-Disease Combinations](#sec-drug_disease_combinations_possible) 🔢: 9.5 million combinations > > $$ \begin{gathered} Ratio_{unexplored} \\ = 1 - \frac{N_{tested}}{N_{combos}} \\ = 1 - \frac{32{,}500}{9.5M} \\ = 99.7\% \end{gathered} $$ where: $$ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Unexplored Therapeutic Frontier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Tested Drug-Disease Relationships (relationships) | -0.7794 | Strong driver | > | Possible Drug-Disease Combinations (combinations) | 0.5916 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Unexplored Therapeutic Frontier (10,000 simulations) > > **Simulation Results Summary: Unexplored Therapeutic Frontier** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 99.7% | > | Mean (expected value) | 99.6% | > | Median (50th percentile) | 99.7% | > | Standard Deviation | 0.116% | > | 90% Range (5th-95th percentile) | \[99.4%, 99.8%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Unexplored Therapeutic Frontier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Unexplored Therapeutic Frontier > > *This exceedance probability chart shows the likelihood that Unexplored Therapeutic Frontier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Universal Security Administration Annual Operating Cost: \$100 million > **NOTE:** > > Annual operating cost of the Universal Security Administration: population times the all-in comparator-anchored cost per citizen. Uncertainty propagates from the component distributions via Monte Carlo. > > **Inputs**: > > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > - [Universal Security Administration All-In Annual Cost per Citizen](#sec-universal_security_admin_all_in_cost_per_citizen_annual): \$0.3 (95% CI: \$0.1 - \$1) > > $$ \begin{gathered} Cost_{opex,ann} \\ = Pop_{US} \times Cost_{ann} \\ = 335M \times \$0.3 \\ = \$100M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Universal Security Administration Annual Operating Cost** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Universal Security Administration All-In Annual Cost per Citizen (USD) | 1.0000 | Strong driver | > | US Population in 2024 (people) | 0.0106 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Universal Security Administration Annual Operating Cost (10,000 simulations) > > **Simulation Results Summary: Universal Security Administration Annual Operating Cost** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$100 million | > | Mean (expected value) | \$101 million | > | Median (50th percentile) | \$79.8 million | > | Standard Deviation | \$67.9 million | > | 90% Range (5th-95th percentile) | \[\$33.5 million, \$250 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Universal Security Administration Annual Operating Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Universal Security Administration Annual Operating Cost > > *This exceedance probability chart shows the likelihood that Universal Security Administration Annual Operating Cost will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Pre-WW2 US Military Spending % Lower than Current: 96.7% > **NOTE:** > > How much lower pre-WW2 (1939) US military spending was than today’s peacetime budget, in constant 2024 dollars > > **Inputs**: > > - [US Military Spending in 1939 (Constant 2024 Dollars)](#sec-us_military_spending_1939_annual_2024usd) 📊: \$29 billion > - [US Military Spending in 2024](#sec-us_military_spending_2024_annual) 📊: \$886 billion > > $$ \begin{gathered} Pct_{1939<2024} \\ = 1 - \frac{Spending_{US,1939}}{Spending_{US,2024}} \\ = 1 - \frac{\$29B}{\$886B} \\ = 96.7\% \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Pre-WW2 US Military Spending % Lower than Current is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 96.7% | > | Mean (expected value) | 96.7% | > | Median (50th percentile) | 96.7% | > | Standard Deviation | 2.22e-14% | > | 90% Range (5th-95th percentile) | \[96.7%, 96.7%\] | > > #### Exceedance Probability > > **Exceedance note:** Pre-WW2 US Military Spending % Lower than Current collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **96.7%** ### US Congress Full Advocacy Cost: \$5.35 billion > **NOTE:** > > Upper-bound advocacy cost to match career incentives for all 535 members of Congress > > **Inputs**: > > - [US Congress Members](#sec-us_congress_member_count): 535 members > - [Post-Office Career Value (per politician)](#sec-politician_post_office_career_value) 📊: \$10 million (95% CI: \$5 million - \$20 million) > > $$ \begin{gathered} Cost_{US,congress} \\ = N_{congress} \times V_{post-office} \\ = 535 \times \$10M \\ = \$5.35B \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** US Congress Full Advocacy Cost is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 5.350e-03) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$5.35 billion | > | Mean (expected value) | \$5.35 billion | > | Median (50th percentile) | \$5.35 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$5.35 billion, \$5.35 billion\] | > > #### Exceedance Probability > > **Exceedance note:** US Congress Full Advocacy Cost collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 5.350e-03) > > Approximate deterministic value: **\$5.35 billion** ### First-Principles Defense Cut (%): 79.2% > **NOTE:** > > Implied cut from current US military spending (~\$886B) to the first-principles homeland-defense optimum (~\$184B): ~79%. For comparison, the US cut military spending 87.6% in two years after WWII. > > **Inputs**: > > - [First-Principles Optimal US Defense Budget](#sec-us_defense_first_principles_optimal) 🔢: \$184 billion > - [US Military Spending in 2024](#sec-us_military_spending_2024_annual) 📊: \$886 billion > > $$ \begin{gathered} Cut_{FP} \\ = 1 - \frac{D_{optimal}}{Spending_{US,2024}} \\ = 1 - \frac{\$184B}{\$886B} \\ = 79.2\% \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for First-Principles Defense Cut (%)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | First-Principles Optimal US Defense Budget (USD) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: First-Principles Defense Cut (%) (10,000 simulations) > > **Simulation Results Summary: First-Principles Defense Cut (%)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 79.2% | > | Mean (expected value) | 79.1% | > | Median (50th percentile) | 79.2% | > | Standard Deviation | 1.97% | > | 90% Range (5th-95th percentile) | \[75.7%, 82.2%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for First-Principles Defense Cut (%); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: First-Principles Defense Cut (%) > > *This exceedance probability chart shows the likelihood that First-Principles Defense Cut (%) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### First-Principles Optimal US Defense Budget: \$184 billion > **NOTE:** > > First-principles optimal US homeland-defense budget: the bottom-up sum of what defending the United States actually requires at efficient prices. Nuclear second strike \$30B + homeland air/missile defense \$35B + Coast Guard \$14B + National Guard \$30B + cyber defense \$15B + mobilization hedge \$60B = ~\$184B. Honest range ~\$130-260B. Compare: current ~\$886B. Restraint-school proposals (Posen, Cato) and peer benchmarks land higher (~\$450-675B) because they cost reduced hegemony and allied/peer deterrence, not homeland defense. > > **Inputs**: > > - [First-Principles Defense: Nuclear Second Strike](#sec-us_defense_fp_nuclear_deterrent): \$30 billion (95% CI: \$20 billion - \$50 billion) > - [First-Principles Defense: Homeland Air & Missile Defense](#sec-us_defense_fp_homeland_air_missile): \$35 billion (95% CI: \$25 billion - \$50 billion) > - [First-Principles Defense: Coast Guard](#sec-us_defense_fp_coast_guard): \$14 billion (95% CI: \$12 billion - \$16 billion) > - [First-Principles Defense: National Guard](#sec-us_defense_fp_national_guard): \$30 billion (95% CI: \$24 billion - \$40 billion) > - [First-Principles Defense: Cyber Defense](#sec-us_defense_fp_cyber): \$15 billion (95% CI: \$10 billion - \$25 billion) > - [First-Principles Defense: Mobilization Hedge](#sec-us_defense_fp_mobilization_hedge): \$60 billion (95% CI: \$40 billion - \$100 billion) > > $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for First-Principles Optimal US Defense Budget** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | First-Principles Defense: Mobilization Hedge (USD) | 0.7817 | Strong driver | > | First-Principles Defense: Nuclear Second Strike (USD) | 0.4131 | Moderate driver | > | First-Principles Defense: Homeland Air & Missile Defense (USD) | 0.3684 | Moderate driver | > | First-Principles Defense: National Guard (USD) | 0.2171 | Weak driver | > | First-Principles Defense: Cyber Defense (USD) | 0.2081 | Weak driver | > | First-Principles Defense: Coast Guard (USD) | 0.0717 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: First-Principles Optimal US Defense Budget (10,000 simulations) > > **Simulation Results Summary: First-Principles Optimal US Defense Budget** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$184 billion | > | Mean (expected value) | \$185 billion | > | Median (50th percentile) | \$185 billion | > | Standard Deviation | \$17.4 billion | > | 90% Range (5th-95th percentile) | \[\$158 billion, \$215 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for First-Principles Optimal US Defense Budget; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: First-Principles Optimal US Defense Budget > > *This exceedance probability chart shows the likelihood that First-Principles Optimal US Defense Budget will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Federal Spending per Capita: \$20,299 > **NOTE:** > > US federal spending per capita. \$6.8T total federal spending divided by 335M population. > > **Inputs**: > > - [US Federal Spending (FY2024)](#sec-us_federal_spending_2024) 📊: \$6.8 trillion > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} Spend_{fed,pc} \\ = \frac{Spending_{US,fed}}{Pop_{US}} \\ = \frac{\$6.8T}{335M} \\ = \$20.3K \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Federal Spending per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-------------------------------|------------------------:|:---------------| > | US Population in 2024 (people) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Federal Spending per Capita (10,000 simulations) > > **Simulation Results Summary: US Federal Spending per Capita** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$20,299 | > | Mean (expected value) | \$20,302 | > | Median (50th percentile) | \$20,303 | > | Standard Deviation | \$146 | > | 90% Range (5th-95th percentile) | \[\$20,052, \$20,553\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Federal Spending per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Federal Spending per Capita > > *This exceedance probability chart shows the likelihood that US Federal Spending per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Discretionary Efficiency: 35.4% > **NOTE:** > > US federal discretionary spending efficiency. What fraction of discretionary spending avoids direct waste (Cat 1 only: military overspend, corporate welfare, drug war, fossil/ag subsidies). ~41%. Some Cat 1 items (farm subsidies, tax expenditures) are technically mandatory/off-budget but are fungible policy choices. > > **Inputs**: > > - [Category 1: Direct Spending Waste](#sec-us_gov_waste_category_1_direct_spending) 🔢: \$1.1 trillion > - [US Federal Discretionary Spending (FY2024)](#sec-us_fed_discretionary_spending_2024) 📊: \$1.7 trillion > > $$ \begin{gathered} E_{US,disc} \\ = 1 - \frac{W_{cat1}}{Spending_{US,disc}} \\ = 1 - \frac{\$1.1T}{\$1.7T} \\ = 35.4\% \end{gathered} $$ where: $$ \begin{gathered} W_{cat1} \\ = W_{military} + W_{corporate} + W_{drugs} + W_{fossil} \\ + W_{agriculture} \\ = \$702B + \$181B + \$90B + \$50B + \$75B \\ = \$1.1T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Discretionary Efficiency** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Category 1: Direct Spending Waste (USD) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Discretionary Efficiency (10,000 simulations) > > **Simulation Results Summary: US Discretionary Efficiency** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 35.4% | > | Mean (expected value) | 35.4% | > | Median (50th percentile) | 35.5% | > | Standard Deviation | 2.56% | > | 90% Range (5th-95th percentile) | \[31.1%, 39.5%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Discretionary Efficiency; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Discretionary Efficiency > > *This exceedance probability chart shows the likelihood that US Discretionary Efficiency will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Governance Efficiency (GDP): 82.7% > **NOTE:** > > Total US governance efficiency: all 4 waste categories as share of GDP. 1 - (\$4.9T / \$28.78T) = ~83%. This broader metric captures direct spending waste, compliance burden, policy-induced GDP loss, and system inefficiency relative to total economic output. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > - [US GDP (2024)](#sec-us_gdp_2024) 📊: \$28.8 trillion > > $$ \begin{gathered} E_{US,GDP} \\ = 1 - \frac{W_{total,US}}{GDP_{US}} \\ = 1 - \frac{\$4.98T}{\$28.8T} \\ = 82.7\% \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Governance Efficiency (GDP)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Governance Efficiency (GDP) (10,000 simulations) > > **Simulation Results Summary: US Governance Efficiency (GDP)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 82.7% | > | Mean (expected value) | 82.7% | > | Median (50th percentile) | 82.8% | > | Standard Deviation | 1.29% | > | 90% Range (5th-95th percentile) | \[80.5%, 84.7%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Governance Efficiency (GDP); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Governance Efficiency (GDP) > > *This exceedance probability chart shows the likelihood that US Governance Efficiency (GDP) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Category 1: Direct Spending Waste: \$1.1 trillion > **NOTE:** > > Category 1: Direct Federal Spending Waste. Actual federal budget allocations that could be redirected. Includes military overspend (\$615B), corporate welfare (\$181B), drug war (\$90B), fossil fuel subsidies (\$50B), and agricultural subsidies (\$75B). Total: ~\$1.01T annually. Solution: Budget reallocation. > > **Inputs**: > > - [Military Overspend](#sec-us_gov_waste_military_overspend) 🔢: \$702 billion > - [Corporate Welfare Waste](#sec-us_gov_waste_corporate_welfare) 📊: \$181 billion (95% CI: \$150 billion - \$220 billion) > - [Drug War Cost](#sec-us_gov_waste_drug_war) 📊: \$90 billion (95% CI: \$60 billion - \$150 billion) > - [Fossil Fuel Subsidies (Explicit)](#sec-us_gov_waste_fossil_fuel_subsidies) 📊: \$50 billion (95% CI: \$30 billion - \$80 billion) > - [Agricultural Subsidies Deadweight Loss](#sec-us_gov_waste_agricultural_subsidies) 📊: \$75 billion (95% CI: \$50 billion - \$120 billion) > > $$ \begin{gathered} W_{cat1} \\ = W_{military} + W_{corporate} + W_{drugs} + W_{fossil} \\ + W_{agriculture} \\ = \$702B + \$181B + \$90B + \$50B + \$75B \\ = \$1.1T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Category 1: Direct Spending Waste** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Drug War Cost (USD) | 0.5866 | Strong driver | > | Agricultural Subsidies Deadweight Loss (USD) | 0.4824 | Moderate driver | > | Corporate Welfare Waste (USD) | 0.4285 | Moderate driver | > | Military Overspend (USD) | 0.4010 | Moderate driver | > | Fossil Fuel Subsidies (Explicit) (USD) | 0.3076 | Moderate driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Category 1: Direct Spending Waste (10,000 simulations) > > **Simulation Results Summary: Category 1: Direct Spending Waste** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$1.1 trillion | > | Mean (expected value) | \$1.1 trillion | > | Median (50th percentile) | \$1.1 trillion | > | Standard Deviation | \$43.5 billion | > | 90% Range (5th-95th percentile) | \[\$1.03 trillion, \$1.17 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Category 1: Direct Spending Waste; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Category 1: Direct Spending Waste > > *This exceedance probability chart shows the likelihood that Category 1: Direct Spending Waste will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Category 2: Compliance Burden: \$1.13 trillion > **NOTE:** > > Category 2: Compliance Burden on Private Sector. Private sector resources consumed by government-imposed compliance requirements. Includes tax compliance (\$546B) and regulatory red tape (\$580B). Total: ~\$1.13T annually. Solution: Simplification (tax code reform, regulatory streamlining). > > **Inputs**: > > - [Tax Compliance Waste](#sec-us_gov_waste_tax_compliance) 📊: \$546 billion (95% CI: \$450 billion - \$650 billion) > - [Regulatory Red Tape Waste](#sec-us_gov_waste_regulatory_red_tape) 📊: \$580 billion (95% CI: \$290 billion - \$1 trillion) > > $$ \begin{gathered} W_{cat2} \\ = W_{tax} + W_{regulatory} \\ = \$546B + \$580B \\ = \$1.13T \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Category 2: Compliance Burden** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:--------------------------------|------------------------:|:---------------| > | Regulatory Red Tape Waste (USD) | 0.9652 | Strong driver | > | Tax Compliance Waste (USD) | 0.2574 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Category 2: Compliance Burden (10,000 simulations) > > **Simulation Results Summary: Category 2: Compliance Burden** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$1.13 trillion | > | Mean (expected value) | \$1.12 trillion | > | Median (50th percentile) | \$1.1 trillion | > | Standard Deviation | \$188 billion | > | 90% Range (5th-95th percentile) | \[\$856 billion, \$1.49 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Category 2: Compliance Burden; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Category 2: Compliance Burden > > *This exceedance probability chart shows the likelihood that Category 2: Compliance Burden will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Category 3: GDP Loss: \$1.56 trillion > **NOTE:** > > Category 3: Policy-Induced GDP Loss. Economic output foregone due to policy constraints on markets. Includes housing/zoning restrictions (\$1.4T) and tariffs (\$160B). Total: ~\$1.56T annually. Solution: Policy reform (zoning liberalization, trade policy). > > **Inputs**: > > - [Housing/Zoning Restrictions Cost](#sec-us_gov_waste_housing_zoning) 📊: \$1.4 trillion (95% CI: \$500 billion - \$2 trillion) > - [Tariff Cost (GDP Loss)](#sec-us_gov_waste_tariffs) 📊: \$160 billion (95% CI: \$90 billion - \$250 billion) > > $$ \begin{gathered} W_{cat3} \\ = W_{housing} + W_{tariffs} \\ = \$1.4T + \$160B \\ = \$1.56T \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Category 3: GDP Loss** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Housing/Zoning Restrictions Cost (USD) | 0.9864 | Strong driver | > | Tariff Cost (GDP Loss) (USD) | 0.1558 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Category 3: GDP Loss (10,000 simulations) > > **Simulation Results Summary: Category 3: GDP Loss** > > | Statistic | Value | > |:--------------------------------|------------------------------------:| > | Baseline (deterministic) | \$1.56 trillion | > | Mean (expected value) | \$1.55 trillion | > | Median (50th percentile) | \$1.53 trillion | > | Standard Deviation | \$288 billion | > | 90% Range (5th-95th percentile) | \[\$1.11 trillion, \$2.1 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Category 3: GDP Loss; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Category 3: GDP Loss > > *This exceedance probability chart shows the likelihood that Category 3: GDP Loss will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Category 4: System Inefficiency: \$1.2 trillion > **NOTE:** > > Category 4: Total System Inefficiency. Fundamental system design failures requiring structural redesign. Currently only healthcare system inefficiency (\$1.2T). Solution: System redesign using competitive market models (Singapore’s catastrophic coverage + HSAs, Switzerland’s regulated competition). > > **Inputs**: > > - [Healthcare System Inefficiency](#sec-us_gov_waste_healthcare_inefficiency) 📊: \$1.2 trillion (95% CI: \$1 trillion - \$1.5 trillion) > > $$ W_{cat4} = W_{health} = \$1.2T = \$1.2T $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Category 4: System Inefficiency** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Healthcare System Inefficiency (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Category 4: System Inefficiency (10,000 simulations) > > **Simulation Results Summary: Category 4: System Inefficiency** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$1.2 trillion | > | Mean (expected value) | \$1.2 trillion | > | Median (50th percentile) | \$1.2 trillion | > | Standard Deviation | \$134 billion | > | 90% Range (5th-95th percentile) | \[\$1 trillion, \$1.44 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Category 4: System Inefficiency; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Category 4: System Inefficiency > > *This exceedance probability chart shows the likelihood that Category 4: System Inefficiency will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Military Overspend: \$702 billion > **NOTE:** > > US military spending above the first-principles homeland-defense optimum. Current US military spending (~\$886B) supports global power projection (~750 overseas bases). A bottom-up, threat-by-threat homeland-defense budget is ~\$184B (nuclear second strike \$30B, homeland air/missile defense \$35B, Coast Guard \$14B, National Guard \$30B, cyber defense \$15B, mobilization hedge \$60B). Delta = \$886B - \$184B = ~\$702B ‘Hegemony Tax’. \[CATEGORY 1: Direct Spending\] > > **Inputs**: > > - [US Military Spending in 2024](#sec-us_military_spending_2024_annual) 📊: \$886 billion > - [First-Principles Optimal US Defense Budget](#sec-us_defense_first_principles_optimal) 🔢: \$184 billion > > $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Military Overspend** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | First-Principles Optimal US Defense Budget (USD) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Military Overspend (10,000 simulations) > > **Simulation Results Summary: Military Overspend** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$702 billion | > | Mean (expected value) | \$701 billion | > | Median (50th percentile) | \$701 billion | > | Standard Deviation | \$17.4 billion | > | 90% Range (5th-95th percentile) | \[\$671 billion, \$728 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Military Overspend; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Military Overspend > > *This exceedance probability chart shows the likelihood that Military Overspend will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Waste (% GDP): 17.3% > **NOTE:** > > US government waste as percentage of GDP. ~\$4.90T waste / \$28.78T GDP = ~17%. This represents the ‘dysfunction tax’ that American citizens effectively pay through inefficient governance. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > - [US GDP (2024)](#sec-us_gdp_2024) 📊: \$28.8 trillion > > $$ \begin{gathered} W_{US,\%GDP} \\ = \frac{W_{total,US}}{GDP_{US}} \\ = \frac{\$4.98T}{\$28.8T} \\ = 17.3\% \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Waste (% GDP)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Waste (% GDP) (10,000 simulations) > > **Simulation Results Summary: US Waste (% GDP)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 17.3% | > | Mean (expected value) | 17.3% | > | Median (50th percentile) | 17.2% | > | Standard Deviation | 1.29% | > | 90% Range (5th-95th percentile) | \[15.3%, 19.5%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Waste (% GDP); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Waste (% GDP) > > *This exceedance probability chart shows the likelihood that US Waste (% GDP) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Governance Dysfunction per Person per Year: \$14,878 > **NOTE:** > > The US efficiency gap per person per year: the audit’s total governance dysfunction divided across the population. The invoice every American pays silently. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > - [US Population in 2024](#sec-us_population_2024) 📊: 335 million people (95% CI: 330 million people - 340 million people) > > $$ \begin{gathered} US_{annual} \\ = \frac{W_{total,US}}{Pop_{US}} \\ = \frac{\$4.98T}{335M} \\ = \$14.9K \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Governance Dysfunction per Person per Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | 0.9959 | Strong driver | > | US Population in 2024 (people) | -0.0961 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Governance Dysfunction per Person per Year (10,000 simulations) > > **Simulation Results Summary: US Governance Dysfunction per Person per Year** > > | Statistic | Value | > |:--------------------------------|-----------------------:| > | Baseline (deterministic) | \$14,878 | > | Mean (expected value) | \$14,856 | > | Median (50th percentile) | \$14,820 | > | Standard Deviation | \$1,113 | > | 90% Range (5th-95th percentile) | \[\$13,110, \$16,753\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Governance Dysfunction per Person per Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Governance Dysfunction per Person per Year > > *This exceedance probability chart shows the likelihood that US Governance Dysfunction per Person per Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Waste (QALY Equivalents): 49.8 million QALYs > **NOTE:** > > US government waste expressed as QALY equivalents. This is an economic equivalent, NOT epidemiological health outcomes. Dividing by QALY threshold yields a measure of foregone welfare. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > - [Medical QALY Threshold](#sec-medical_qaly_threshold) 📊: \$100,000 > > $$ \begin{gathered} W_{US,QALY} \\ = \frac{W_{total,US}}{QALY_{threshold}} \\ = \frac{\$4.98T}{\$100K} \\ = 49.8M \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Waste (QALY Equivalents)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Waste (QALY Equivalents) (10,000 simulations) > > **Simulation Results Summary: US Waste (QALY Equivalents)** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 49.8 million | > | Mean (expected value) | 49.8 million | > | Median (50th percentile) | 49.6 million | > | Standard Deviation | 3.71 million | > | 90% Range (5th-95th percentile) | \[43.9 million, 56.1 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Waste (QALY Equivalents); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Waste (QALY Equivalents) > > *This exceedance probability chart shows the likelihood that US Waste (QALY Equivalents) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Gov Waste (Raw Total): \$4.98 trillion > **NOTE:** > > Raw sum of US government waste components before overlap discount: healthcare (\$1.2T) + housing (\$1.4T) + military (\$615B) + regulatory (\$580B) + tax (\$546B) + corporate (\$181B) + tariffs (\$160B) + drug war (\$90B) + fossil fuel (\$50B) + agriculture (\$75B) = ~\$4.9T raw. > > **Inputs**: > > - [Healthcare System Inefficiency](#sec-us_gov_waste_healthcare_inefficiency) 📊: \$1.2 trillion (95% CI: \$1 trillion - \$1.5 trillion) > - [Housing/Zoning Restrictions Cost](#sec-us_gov_waste_housing_zoning) 📊: \$1.4 trillion (95% CI: \$500 billion - \$2 trillion) > - [Military Overspend](#sec-us_gov_waste_military_overspend) 🔢: \$702 billion > - [Regulatory Red Tape Waste](#sec-us_gov_waste_regulatory_red_tape) 📊: \$580 billion (95% CI: \$290 billion - \$1 trillion) > - [Tax Compliance Waste](#sec-us_gov_waste_tax_compliance) 📊: \$546 billion (95% CI: \$450 billion - \$650 billion) > - [Corporate Welfare Waste](#sec-us_gov_waste_corporate_welfare) 📊: \$181 billion (95% CI: \$150 billion - \$220 billion) > - [Tariff Cost (GDP Loss)](#sec-us_gov_waste_tariffs) 📊: \$160 billion (95% CI: \$90 billion - \$250 billion) > - [Drug War Cost](#sec-us_gov_waste_drug_war) 📊: \$90 billion (95% CI: \$60 billion - \$150 billion) > - [Fossil Fuel Subsidies (Explicit)](#sec-us_gov_waste_fossil_fuel_subsidies) 📊: \$50 billion (95% CI: \$30 billion - \$80 billion) > - [Agricultural Subsidies Deadweight Loss](#sec-us_gov_waste_agricultural_subsidies) 📊: \$75 billion (95% CI: \$50 billion - \$120 billion) > > $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Gov Waste (Raw Total)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Housing/Zoning Restrictions Cost (USD) | 0.7664 | Strong driver | > | Regulatory Red Tape Waste (USD) | 0.4876 | Moderate driver | > | Healthcare System Inefficiency (USD) | 0.3622 | Moderate driver | > | Tax Compliance Waste (USD) | 0.1300 | Weak driver | > | Tariff Cost (GDP Loss) (USD) | 0.1211 | Weak driver | > | Drug War Cost (USD) | 0.0687 | Minimal effect | > | Agricultural Subsidies Deadweight Loss (USD) | 0.0565 | Minimal effect | > | Corporate Welfare Waste (USD) | 0.0502 | Minimal effect | > | Military Overspend (USD) | 0.0470 | Minimal effect | > | Fossil Fuel Subsidies (Explicit) (USD) | 0.0360 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Gov Waste (Raw Total) (10,000 simulations) > > **Simulation Results Summary: US Gov Waste (Raw Total)** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$4.98 trillion | > | Mean (expected value) | \$4.98 trillion | > | Median (50th percentile) | \$4.96 trillion | > | Standard Deviation | \$371 billion | > | 90% Range (5th-95th percentile) | \[\$4.39 trillion, \$5.61 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Gov Waste (Raw Total); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Gov Waste (Raw Total) > > *This exceedance probability chart shows the likelihood that US Gov Waste (Raw Total) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Recoverable Capital: \$2.49 trillion > **NOTE:** > > Recoverable capital if US improved to OECD median efficiency. Current US efficiency ~38-48%; OECD median ~75-85%. Closing to ~80% would recover approximately half the gap. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > > $$ \begin{gathered} W_{US,recoverable} \\ = W_{total,US} \times 0.5 \\ = \$4.98T \times 0.5 \\ = \$2.49T \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Recoverable Capital** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Recoverable Capital (10,000 simulations) > > **Simulation Results Summary: Recoverable Capital** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$2.49 trillion | > | Mean (expected value) | \$2.49 trillion | > | Median (50th percentile) | \$2.48 trillion | > | Standard Deviation | \$186 billion | > | 90% Range (5th-95th percentile) | \[\$2.19 trillion, \$2.81 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Recoverable Capital; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Recoverable Capital > > *This exceedance probability chart shows the likelihood that Recoverable Capital will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Government Waste (Total): \$4.98 trillion > **NOTE:** > > Total annual US government waste (additive sum of components). Consolidates healthcare (\$1.2T), housing (\$1.4T), military (\$615B), regulatory (\$580B), tax (\$546B), corporate (\$181B), tariffs (\$160B), drug war (\$90B), fossil fuel (\$50B), agriculture (\$75B). Categories treated as additive; any overlap offset by excluded categories (state/local inefficiency, implicit subsidies, behavioral effects). ~\$4.9T annually. > > **Inputs**: > > - [US Gov Waste (Raw Total)](#sec-us_gov_waste_raw_total) 🔢: \$4.98 trillion > - [Overlap Discount Factor](#sec-us_gov_waste_overlap_discount): 1:1 > > $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Government Waste (Total)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:-------------------------------|------------------------:|:---------------| > | US Gov Waste (Raw Total) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Government Waste (Total) (10,000 simulations) > > **Simulation Results Summary: US Government Waste (Total)** > > | Statistic | Value | > |:--------------------------------|-------------------------------------:| > | Baseline (deterministic) | \$4.98 trillion | > | Mean (expected value) | \$4.98 trillion | > | Median (50th percentile) | \$4.96 trillion | > | Standard Deviation | \$371 billion | > | 90% Range (5th-95th percentile) | \[\$4.39 trillion, \$5.61 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Government Waste (Total); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Government Waste (Total) > > *This exceedance probability chart shows the likelihood that US Government Waste (Total) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### US Waste (VSL Equivalents): 364 thousand people > **NOTE:** > > US government waste expressed as VSL equivalents. This is an economic equivalent, NOT literal deaths. Dividing the efficiency gap by VSL yields a measure of foregone welfare. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > - [DOT VSL](#sec-dot_value_of_statistical_life) 📊: \$13.7 million > > $$ \begin{gathered} W_{US,VSL} \\ = \frac{W_{total,US}}{VSL_{DOT}} \\ = \frac{\$4.98T}{\$13.7M} \\ = 364{,}000 \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Waste (VSL Equivalents)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Waste (VSL Equivalents) (10,000 simulations) > > **Simulation Results Summary: US Waste (VSL Equivalents)** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 364 thousand | > | Mean (expected value) | 363 thousand | > | Median (50th percentile) | 362 thousand | > | Standard Deviation | 27,095 | > | 90% Range (5th-95th percentile) | \[320 thousand, 410 thousand\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Waste (VSL Equivalents); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Waste (VSL Equivalents) > > *This exceedance probability chart shows the likelihood that US Waste (VSL Equivalents) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Efficiency Gap / Treaty Funding: 183:1 > **NOTE:** > > How many times the US government efficiency gap could fund the 1% Treaty. The efficiency gap represents capital that could fund transformative health research many times over. > > **Inputs**: > > - [US Government Waste (Total)](#sec-us_gov_waste_total) 🔢: \$4.98 trillion > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > > $$ \begin{gathered} k_{waste:treaty} \\ = \frac{W_{total,US}}{Funding_{treaty}} \\ = \frac{\$4.98T}{\$27.2B} \\ = 183 \end{gathered} $$ where: $$ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.98T \times 1 \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$702B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.98T \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Efficiency Gap / Treaty Funding** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:----------------------------------|------------------------:|:---------------| > | US Government Waste (Total) (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Efficiency Gap / Treaty Funding (10,000 simulations) > > **Simulation Results Summary: Efficiency Gap / Treaty Funding** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 183:1 | > | Mean (expected value) | 183:1 | > | Median (50th percentile) | 182:1 | > | Standard Deviation | 13.6:1 | > | 90% Range (5th-95th percentile) | \[161:1, 206:1\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Efficiency Gap / Treaty Funding; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Efficiency Gap / Treaty Funding > > *This exceedance probability chart shows the likelihood that Efficiency Gap / Treaty Funding will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Patients Fundable by Military Overspend (Pragmatic Trials): 756 million patients/year > **NOTE:** > > Trial participants the annual US military overspend (~\$702B) could fund at the empirical pragmatic-trial cost (~\$929/patient): ~756M patients/year. Patient-based, the direct parallel of DFDA_PATIENTS_FUNDABLE_ANNUALLY (the treaty’s ~23.4M). > > **Inputs**: > > - [Military Overspend](#sec-us_gov_waste_military_overspend) 🔢: \$702 billion > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > > $$ \begin{gathered} N_{fundable,overspend} \\ = \frac{W_{military}}{Cost_{pragmatic,pt}} \\ = \frac{\$702B}{\$929} \\ = 756M \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Patients Fundable by Military Overspend (Pragmatic Trials)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Cost per Patient (USD/patient) | -0.6869 | Strong driver | > | Military Overspend (USD) | 0.0236 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Patients Fundable by Military Overspend (Pragmatic Trials) (10,000 simulations) > > **Simulation Results Summary: Patients Fundable by Military Overspend (Pragmatic Trials)** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 756 million | > | Mean (expected value) | 1.24 billion | > | Median (50th percentile) | 963 million | > | Standard Deviation | 950 million | > | 90% Range (5th-95th percentile) | \[298 million, 3.04 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Patients Fundable by Military Overspend (Pragmatic Trials); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Patients Fundable by Military Overspend (Pragmatic Trials) > > *This exceedance probability chart shows the likelihood that Patients Fundable by Military Overspend (Pragmatic Trials) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Military Overspend Pragmatic Trial Capacity Multiplier: 398x > **NOTE:** > > Patients the overspend could fund in pragmatic trials (~756M/year at ~\$929 each) relative to the ~1.9M people currently in trials: ~398x. Identical methodology to the treaty’s DFDA_TRIAL_CAPACITY_MULTIPLIER (12.3x), scaled to the overspend. The current 1.9M is capped by the ~\$41k cost of a traditional trial, NOT by a shortage of patients: aging is a universal trial population and billions have suboptimally-treated chronic disease. The binding constraint is building decentralized trial infrastructure, not patient supply. > > **Inputs**: > > - [Patients Fundable by Military Overspend (Pragmatic Trials)](#sec-us_military_overspend_patients_fundable) 🔢: 756 million patients/year > - [Annual Global Clinical Trial Participants](#sec-current_trial_slots_available) 📊: 1.9 million patients/year (95% CI: 1.5 million patients/year - 2.3 million patients/year) > > $$ \begin{gathered} k_{capacity,overspend} \\ = \frac{N_{fundable,overspend}}{Slots_{curr}} \\ = \frac{756M}{1.9M} \\ = 398 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,overspend} \\ = \frac{W_{military}}{Cost_{pragmatic,pt}} \\ = \frac{\$702B}{\$929} \\ = 756M \end{gathered} $$ where: $$ \begin{gathered} W_{military} \\ = Spending_{US,2024} - D_{optimal} \\ = \$886B - \$184B \\ = \$702B \end{gathered} $$ where: $$ \begin{gathered} D_{optimal} \\ = D_{nuclear} + D_{air} + D_{cg} + D_{guard} + D_{cyber} \\ + D_{hedge} \\ = \$30B + \$35B + \$14B + \$30B + \$15B + \$60B \\ = \$184B \end{gathered} $$ *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Military Overspend Pragmatic Trial Capacity Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Patients Fundable by Military Overspend (Pragmatic Trials) (patients/year) | 0.9867 | Strong driver | > | Annual Global Clinical Trial Participants (patients/year) | -0.1315 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Military Overspend Pragmatic Trial Capacity Multiplier (10,000 simulations) > > **Simulation Results Summary: Military Overspend Pragmatic Trial Capacity Multiplier** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 398x | > | Mean (expected value) | 659x | > | Median (50th percentile) | 515x | > | Standard Deviation | 514x | > | 90% Range (5th-95th percentile) | \[158x, 1.6kx\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Military Overspend Pragmatic Trial Capacity Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Military Overspend Pragmatic Trial Capacity Multiplier > > *This exceedance probability chart shows the likelihood that Military Overspend Pragmatic Trial Capacity Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Current US Military Spending vs Pre-WW2 Baseline (Multiplier): 30.6x > **NOTE:** > > Ratio of current US military spending to pre-WW2 baseline in constant dollars (\$886B / \$29B) > > **Inputs**: > > - [US Military Spending in 2024](#sec-us_military_spending_2024_annual) 📊: \$886 billion > - [US Military Spending in 1939 (Constant 2024 Dollars)](#sec-us_military_spending_1939_annual_2024usd) 📊: \$29 billion > > $$ \begin{gathered} Ratio_{US,2024:1939} \\ = \frac{Spending_{US,2024}}{Spending_{US,1939}} \\ = \frac{\$886B}{\$29B} \\ = 30.6 \end{gathered} $$ > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Current US Military Spending vs Pre-WW2 Baseline (Multiplier) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 3.055e-11) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 30.6x | > | Mean (expected value) | 30.6x | > | Median (50th percentile) | 30.6x | > | Standard Deviation | 7.11e-15x | > | 90% Range (5th-95th percentile) | \[30.6x, 30.6x\] | > > #### Exceedance Probability > > **Exceedance note:** Current US Military Spending vs Pre-WW2 Baseline (Multiplier) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 3.055e-11) > > Approximate deterministic value: **30.6x** ### US Political Reform Investment (Total): \$25.5 billion > **NOTE:** > > Total upper-bound investment for US political reform: (campaign spending + 2 years lobbying) × effort multiplier + Congress career advocacy. Represents cost to achieve democratic parity with incumbent interests. > > **Inputs**: > > - [US Federal Campaign Spending (2024)](#sec-us_total_federal_campaign_spending_2024) 📊: \$20 billion (95% CI: \$18 billion - \$22 billion) > - [US Total Lobbying (2024)](#sec-us_total_lobbying_annual) 📊: \$4.4 billion (95% CI: \$3.74 billion - \$5.06 billion) > - [Political Effort Multiplier (US)](#sec-us_political_effort_multiplier): 0.7x (95% CI: 0.4x - 1.2x) > - [US Congress Full Advocacy Cost](#sec-us_congress_full_advocacy_cost) 🔢: \$5.35 billion > > $$ \begin{gathered} Cost_{US,total} \\ = (Cost_{campaign} \\ + Cost_{lobby} \times 2) \times \mu_{effort} + Cost_{career} \end{gathered} $$ > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for US Political Reform Investment (Total)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Political Effort Multiplier (US) (multiplier) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: US Political Reform Investment (Total) (10,000 simulations) > > **Simulation Results Summary: US Political Reform Investment (Total)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$25.5 billion | > | Mean (expected value) | \$25.5 billion | > | Median (50th percentile) | \$24.8 billion | > | Standard Deviation | \$5.52 billion | > | 90% Range (5th-95th percentile) | \[\$17.5 billion, \$36.3 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for US Political Reform Investment (Total); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: US Political Reform Investment (Total) > > *This exceedance probability chart shows the likelihood that US Political Reform Investment (Total) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Expected Value of a Vote (US): \$0.000338 > **NOTE:** > > Expected monetary value of a single vote in a US presidential election. Calculated as the probability of being decisive (1 in 60M) times federal spending per capita (~\$20,300). Represents the expected influence over government resource allocation from casting one vote. > > **Inputs**: > > - [Probability of Decisive Vote (US)](#sec-us_vote_decisive_probability) 📊: 1 in 60 million > - [US Federal Spending per Capita](#sec-us_federal_spending_per_capita) 🔢: \$20,299 > > $$ \begin{gathered} EV_{vote} \\ = P_{decisive} \times Spend_{fed,pc} \\ = 1\text{ in }60M \times \$20.3K \\ = \$0.000338 \end{gathered} $$ where: $$ \begin{gathered} Spend_{fed,pc} \\ = \frac{Spending_{US,fed}}{Pop_{US}} \\ = \frac{\$6.8T}{335M} \\ = \$20.3K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Expected Value of a Vote (US)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | US Federal Spending per Capita (USD/person) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Expected Value of a Vote (US) (10,000 simulations) > > **Simulation Results Summary: Expected Value of a Vote (US)** > > | Statistic | Value | > |:--------------------------------|---------------------------:| > | Baseline (deterministic) | \$0.000338 | > | Mean (expected value) | \$0.000338 | > | Median (50th percentile) | \$0.000338 | > | Standard Deviation | \$0.00000244 | > | 90% Range (5th-95th percentile) | \[\$0.000334, \$0.000343\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Expected Value of a Vote (US); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Expected Value of a Vote (US) > > *This exceedance probability chart shows the likelihood that Expected Value of a Vote (US) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual VICTORY Incentive Alignment Bond Payout: \$2.72 billion > **NOTE:** > > Annual VICTORY Incentive Alignment Bond payout (treaty funding × bond percentage) > > **Inputs**: > > - [Annual Funding from 1% of Global Military Spending Redirected to DIH](#sec-treaty_annual_funding) 🔢: \$27.2 billion > - [Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds](#sec-victory_bond_funding_pct): 10% > > $$ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Annual VICTORY Incentive Alignment Bond Payout is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 2.720e-03) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$2.72 billion | > | Mean (expected value) | \$2.72 billion | > | Median (50th percentile) | \$2.72 billion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$2.72 billion, \$2.72 billion\] | > > #### Exceedance Probability > > **Exceedance note:** Annual VICTORY Incentive Alignment Bond Payout collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 2.720e-03) > > Approximate deterministic value: **\$2.72 billion** ### Annual Return Percentage for VICTORY Incentive Alignment Bondholders: 272% > **NOTE:** > > Annual return percentage for VICTORY Incentive Alignment Bondholders > > **Inputs**: > > - [Annual VICTORY Incentive Alignment Bond Payout](#sec-victory_bond_annual_payout) 🔢: \$2.72 billion > - [Total 1% Treaty Campaign Cost](#sec-treaty_campaign_total_cost) 🔢: \$1 billion > > $$ \begin{gathered} r_{bond} \\ = \frac{Payout_{bond,ann}}{Cost_{campaign}} \\ = \frac{\$2.72B}{\$1B} \\ = 272\% \end{gathered} $$ where: $$ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} $$ where: $$ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} $$ where: $$ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Return Percentage for VICTORY Incentive Alignment Bondholders** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:------------------------------------|------------------------:|:---------------| > | Total 1% Treaty Campaign Cost (USD) | -0.9466 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Return Percentage for VICTORY Incentive Alignment Bondholders (10,000 simulations) > > **Simulation Results Summary: Annual Return Percentage for VICTORY Incentive Alignment Bondholders** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 272% | > | Mean (expected value) | 288% | > | Median (50th percentile) | 285% | > | Standard Deviation | 66.8% | > | 90% Range (5th-95th percentile) | \[184%, 405%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Return Percentage for VICTORY Incentive Alignment Bondholders; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Return Percentage for VICTORY Incentive Alignment Bondholders > > *This exceedance probability chart shows the likelihood that Annual Return Percentage for VICTORY Incentive Alignment Bondholders will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Lives Saved per Verified Voter: 2.6 lives > **NOTE:** > > Average lives saved per verified voter if the treaty passes (total lives saved divided by the majority-of-humanity coordination target). > > **Inputs**: > > - [Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_lives_saved) 🔢: 10.7 billion deaths > - [Global Registered Voters](#sec-global_registered_voters) 📊: 4.13 billion of people > > $$ \begin{gathered} Lives_{voter} \\ = \frac{Lives_{max}}{N_{voters,global}} \\ = \frac{10.7B}{4.13B} \\ = 2.6 \end{gathered} $$ where: $$ \begin{gathered} Lives_{max} \\ = Deaths_{disease,daily} \times T_{accel,max} \times 338 \\ = 150{,}000 \times 212 \times 338 \\ = 10.7B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Lives Saved per Verified Voter** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (deaths) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Lives Saved per Verified Voter (10,000 simulations) > > **Simulation Results Summary: Lives Saved per Verified Voter** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 2.6 | > | Mean (expected value) | 2.94 | > | Median (50th percentile) | 2.78 | > | Standard Deviation | 1.04 | > | 90% Range (5th-95th percentile) | \[1.51, 4.92\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Lives Saved per Verified Voter; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Lives Saved per Verified Voter > > *This exceedance probability chart shows the likelihood that Lives Saved per Verified Voter will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Suffering Hours Prevented per Verified Voter: 468 thousand hours > **NOTE:** > > Average suffering hours prevented per verified voter if the treaty passes (total suffering hours divided by the majority-of-humanity coordination target). > > **Inputs**: > > - [Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput](#sec-dfda_trial_capacity_plus_efficacy_lag_suffering_hours) 🔢: 1.93 quadrillion hours > - [Global Registered Voters](#sec-global_registered_voters) 📊: 4.13 billion of people > > $$ \begin{gathered} Hours_{suffer,voter} \\ = \frac{Hours_{suffer,max}}{N_{voters,global}} \\ = \frac{1930T}{4.13B} \\ = 468{,}000 \end{gathered} $$ where: $$ \begin{gathered} Hours_{suffer,max} \\ = DALYs_{max} \times Pct_{YLD} \times 8760 \\ = 565B \times 0.39 \times 8760 \\ = 1930T \end{gathered} $$ where: $$ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} $$ where: $$ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 $$ where: $$ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} $$ where: $$ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Suffering Hours Prevented per Verified Voter** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (hours) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Suffering Hours Prevented per Verified Voter (10,000 simulations) > > **Simulation Results Summary: Suffering Hours Prevented per Verified Voter** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 468 thousand | > | Mean (expected value) | 525 thousand | > | Median (50th percentile) | 493 thousand | > | Standard Deviation | 201 thousand | > | 90% Range (5th-95th percentile) | \[251 thousand, 907 thousand\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Suffering Hours Prevented per Verified Voter; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Suffering Hours Prevented per Verified Voter > > *This exceedance probability chart shows the likelihood that Suffering Hours Prevented per Verified Voter will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Children Killed in Wars Since 1900: 102 million deaths > **NOTE:** > > Estimated children under 18 killed in wars, conflicts, genocides, and policy-induced famines since 1900 > > **Inputs**: > > - [Total War and Conflict Deaths Since 1900](#sec-war_deaths_since_1900): 310 million deaths (95% CI: 200 million deaths - 340 million deaths) > - [Child Share of War Deaths Since 1900](#sec-war_child_death_pct): 33% (95% CI: 25% - 40%) > > $$ \begin{gathered} Deaths_{war,child} \\ = Deaths_{war,1900} \times Pct_{war,child} \\ = 310M \times 33\% \\ = 102M \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Children Killed in Wars Since 1900** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total War and Conflict Deaths Since 1900 (deaths) | 0.7441 | Strong driver | > | Child Share of War Deaths Since 1900 (rate) | 0.6552 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Children Killed in Wars Since 1900 (10,000 simulations) > > **Simulation Results Summary: Children Killed in Wars Since 1900** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 102 million | > | Mean (expected value) | 87.9 million | > | Median (50th percentile) | 86.4 million | > | Standard Deviation | 17.8 million | > | 90% Range (5th-95th percentile) | \[60.2 million, 120 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Children Killed in Wars Since 1900; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Children Killed in Wars Since 1900 > > *This exceedance probability chart shows the likelihood that Children Killed in Wars Since 1900 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Cumulative War Costs over 20 Years (Current Trajectory): \$227 trillion > **NOTE:** > > Cumulative global war costs over 20 years if current spending levels continue. The price tag of the status quo trajectory. > > **Inputs**: > > - [Total Annual Cost of War Worldwide](#sec-global_annual_direct_indirect_war_cost) 🔢: \$11.4 trillion > > $$ \begin{gathered} Cost_{war,20yr} \\ = Cost_{war,total} \times 20 \\ = \$11.4T \times 20 \\ = \$227T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Cumulative War Costs over 20 Years (Current Trajectory)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Annual Cost of War Worldwide (USD/year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Cumulative War Costs over 20 Years (Current Trajectory) (10,000 simulations) > > **Simulation Results Summary: Cumulative War Costs over 20 Years (Current Trajectory)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$227 trillion | > | Mean (expected value) | \$226 trillion | > | Median (50th percentile) | \$225 trillion | > | Standard Deviation | \$17.5 trillion | > | 90% Range (5th-95th percentile) | \[\$199 trillion, \$257 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Cumulative War Costs over 20 Years (Current Trajectory); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Cumulative War Costs over 20 Years (Current Trajectory) > > *This exceedance probability chart shows the likelihood that Cumulative War Costs over 20 Years (Current Trajectory) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### GDP per Capita in Peace Timeline: \$333,636 > **NOTE:** > > Counterfactual global GDP per capita if all wars abolished since 1900. Actual is \$14,375. Mid-range counterfactual: \$333,636 (23.2x richer). 8 non-overlapping channels at +2.6pp. > > **Inputs**: > > - [Global GDP per Capita in 1900](#sec-global_gdp_per_capita_1900) 📊: \$3,150 (SE: ±\$500) > - [Peace Growth Boost (8 Channels, Overlap-Corrected)](#sec-war_counterfactual_annual_growth_boost): 2.6% (95% CI: 1.65% - 3.55%) > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > > $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for GDP per Capita in Peace Timeline** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Peace Growth Boost (8 Channels, Overlap-Corrected) (percentage points) | 0.9620 | Strong driver | > | Global Average Income (2025 Baseline) (USD) | 0.0171 | Minimal effect | > | Global GDP per Capita in 1900 (USD/person) | 0.0088 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: GDP per Capita in Peace Timeline (10,000 simulations) > > **Simulation Results Summary: GDP per Capita in Peace Timeline** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | \$333,636 | > | Mean (expected value) | \$406,710 | > | Median (50th percentile) | \$329,582 | > | Standard Deviation | \$256,532 | > | 90% Range (5th-95th percentile) | \[\$120,624, \$921,185\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for GDP per Capita in Peace Timeline; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: GDP per Capita in Peace Timeline > > *This exceedance probability chart shows the likelihood that GDP per Capita in Peace Timeline will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Peace Income Multiple (How Much Richer Without War): 23.2x > **NOTE:** > > How many times richer the average person would be if wars had been abolished in 1900. Counterfactual GDP per capita / actual GDP per capita. > > **Inputs**: > > - [GDP per Capita in Peace Timeline](#sec-war_counterfactual_gdp_per_capita) 🔢: \$333,636 > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > > $$ \begin{gathered} M_{war,income} \\ = \frac{GDP_{pc,peace}}{\bar{y}_{0}} \\ = \frac{\$334K}{\$14.4K} \\ = 23.2 \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Peace Income Multiple (How Much Richer Without War)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | GDP per Capita in Peace Timeline (USD/person) | 1.0000 | Strong driver | > | Global Average Income (2025 Baseline) (USD) | -0.0192 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Peace Income Multiple (How Much Richer Without War) (10,000 simulations) > > **Simulation Results Summary: Peace Income Multiple (How Much Richer Without War)** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 23.2x | > | Mean (expected value) | 28.3x | > | Median (50th percentile) | 22.9x | > | Standard Deviation | 17.8x | > | 90% Range (5th-95th percentile) | \[8.4x, 64x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Peace Income Multiple (How Much Richer Without War); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Peace Income Multiple (How Much Richer Without War) > > *This exceedance probability chart shows the likelihood that Peace Income Multiple (How Much Richer Without War) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Lost GDP Global from War: \$2.55 quadrillion > **NOTE:** > > Total annual global GDP lost to compound war effects since 1900. Lost GDP per capita × 8 billion people. > > **Inputs**: > > - [Annual Lost GDP per Capita from War](#sec-war_counterfactual_lost_gdp_per_capita) 🔢: \$319,261 > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} GDP_{lost,total} \\ = GDP_{pc,lost} \times Pop_{global} \\ = \$319K \times 8B \\ = \$2550T \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,lost} \\ = GDP_{pc,peace} - \bar{y}_{0} \\ = \$334K - \$14.4K \\ = \$319K \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Lost GDP Global from War** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Lost GDP per Capita from War (USD/person/year) | 1.0000 | Strong driver | > | Global Population in 2024 (of people) | 0.0185 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Lost GDP Global from War (10,000 simulations) > > **Simulation Results Summary: Annual Lost GDP Global from War** > > | Statistic | Value | > |:--------------------------------|---------------------------------------:| > | Baseline (deterministic) | \$2.55 quadrillion | > | Mean (expected value) | \$3.14 quadrillion | > | Median (50th percentile) | \$2.52 quadrillion | > | Standard Deviation | \$2.05 quadrillion | > | 90% Range (5th-95th percentile) | \[\$851 trillion, \$7.24 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Lost GDP Global from War; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Lost GDP Global from War > > *This exceedance probability chart shows the likelihood that Annual Lost GDP Global from War will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Annual Lost GDP per Capita from War: \$319,261 > **NOTE:** > > Annual GDP per capita lost due to compound war effects since 1900 > > **Inputs**: > > - [GDP per Capita in Peace Timeline](#sec-war_counterfactual_gdp_per_capita) 🔢: \$333,636 > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > > $$ \begin{gathered} GDP_{pc,lost} \\ = GDP_{pc,peace} - \bar{y}_{0} \\ = \$334K - \$14.4K \\ = \$319K \end{gathered} $$ where: $$ \begin{gathered} GDP_{pc,peace} \\ = GDP_{pc,1900} \times \left(1 + \left(\frac{\bar{y}_{0}}{GDP_{pc,1900}}\right)^{1/124} - 1 + g_{war,penalty}\right)^{124} \\[0.5em] = \$3.15K \times \left(1 + \left(\frac{\$14.4K}{\$3.15K}\right)^{1/124} - 1 + 2.6\%\right)^{124} \\[0.5em] = \$334K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Annual Lost GDP per Capita from War** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | GDP per Capita in Peace Timeline (USD/person) | 1.0000 | Strong driver | > | Global Average Income (2025 Baseline) (USD) | -0.0007 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Annual Lost GDP per Capita from War (10,000 simulations) > > **Simulation Results Summary: Annual Lost GDP per Capita from War** > > | Statistic | Value | > |:--------------------------------|-------------------------:| > | Baseline (deterministic) | \$319,261 | > | Mean (expected value) | \$392,333 | > | Median (50th percentile) | \$315,224 | > | Standard Deviation | \$256,529 | > | 90% Range (5th-95th percentile) | \[\$106,333, \$906,864\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual Lost GDP per Capita from War; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Annual Lost GDP per Capita from War > > *This exceedance probability chart shows the likelihood that Annual Lost GDP per Capita from War will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Life-Years Lost to War Since 1900: 8.37 billion life-years > **NOTE:** > > Total life-years stolen by war since 1900 (deaths x avg years lost per death) > > **Inputs**: > > - [Total War and Conflict Deaths Since 1900](#sec-war_deaths_since_1900): 310 million deaths (95% CI: 200 million deaths - 340 million deaths) > - [Average Years of Life Lost per War Death](#sec-war_avg_years_life_lost_per_death): 27 years (95% CI: 20 years - 35 years) > > $$ \begin{gathered} YLL_{war,total} \\ = Deaths_{war,1900} \times YLL_{war} \\ = 310M \times 27 \\ = 8.37B \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Life-Years Lost to War Since 1900** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Average Years of Life Lost per War Death (years) | 0.7138 | Strong driver | > | Total War and Conflict Deaths Since 1900 (deaths) | 0.6835 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Life-Years Lost to War Since 1900 (10,000 simulations) > > **Simulation Results Summary: Total Life-Years Lost to War Since 1900** > > | Statistic | Value | > |:--------------------------------|-------------------------------:| > | Baseline (deterministic) | 8.37 billion | > | Mean (expected value) | 7.42 billion | > | Median (50th percentile) | 7.26 billion | > | Standard Deviation | 1.64 billion | > | 90% Range (5th-95th percentile) | \[4.92 billion, 10.4 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Life-Years Lost to War Since 1900; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Life-Years Lost to War Since 1900 > > *This exceedance probability chart shows the likelihood that Total Life-Years Lost to War Since 1900 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Medical Toolchain Prize Overpay Multiple: 341x > **NOTE:** > > How many times larger the prosecutor’s medical-toolchain prize reserve is than the observed anchor costs listed here. > > **Inputs**: > > - [War Medical Toolchain Prize Budget](#sec-war_medical_toolchain_prize_budget): \$20 trillion (95% CI: \$5 trillion - \$50 trillion) > - [Observed Medical Toolchain Anchor Costs](#sec-medical_toolchain_observed_anchor_costs) 🔢: \$58.6 billion > > $$ \begin{gathered} m_{tool,overpay} \\ = \frac{C_{tool,prize}}{C_{tool,anchors}} \\ = \frac{\$20T}{\$58.6B} \\ = 341 \end{gathered} $$ where: $$ \begin{gathered} C_{tool,anchors} \\ = C_{tool,HGP} + C_{tool,CRISPR} + C_{tool,BRAIN} \\ + C_{tool,PCORnet} + C_{tool,EHR} + C_{tool,OWS} \\ = \$2.7B + \$3.1B + \$4.5B + \$325M + \$30B + \$18B \\ = \$58.6B \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** War Medical Toolchain Prize Overpay Multiple is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 3.412e-10) > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 341x | > | Mean (expected value) | 341x | > | Median (50th percentile) | 341x | > | Standard Deviation | 0x | > | 90% Range (5th-95th percentile) | \[341x, 341x\] | > > #### Exceedance Probability > > **Exceedance note:** War Medical Toolchain Prize Overpay Multiple collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 3.412e-10) > > Approximate deterministic value: **341x** ### QALY Value of Life Lost to War Since 1900: \$1.26 quadrillion > **NOTE:** > > Economic value of life-years destroyed by war since 1900, at \$150K/QALY > > **Inputs**: > > - [Total Life-Years Lost to War Since 1900](#sec-war_life_years_lost_since_1900) 🔢: 8.37 billion life-years > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} V_{war,QALY} \\ = YLL_{war,total} \times Value_{QALY} \\ = 8.37B \times \$150K \\ = \$1260T \end{gathered} $$ where: $$ \begin{gathered} YLL_{war,total} \\ = Deaths_{war,1900} \times YLL_{war} \\ = 310M \times 27 \\ = 8.37B \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for QALY Value of Life Lost to War Since 1900** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Total Life-Years Lost to War Since 1900 (life-years) | 0.7591 | Strong driver | > | Standard Economic Value per QALY (USD/QALY) | 0.6258 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: QALY Value of Life Lost to War Since 1900 (10,000 simulations) > > **Simulation Results Summary: QALY Value of Life Lost to War Since 1900** > > | Statistic | Value | > |:--------------------------------|---------------------------------------:| > | Baseline (deterministic) | \$1.26 quadrillion | > | Mean (expected value) | \$1.11 quadrillion | > | Median (50th percentile) | \$1.07 quadrillion | > | Standard Deviation | \$324 trillion | > | 90% Range (5th-95th percentile) | \[\$644 trillion, \$1.71 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for QALY Value of Life Lost to War Since 1900; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: QALY Value of Life Lost to War Since 1900 > > *This exceedance probability chart shows the likelihood that QALY Value of Life Lost to War Since 1900 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Total Historical Cost of War Since 1900: \$1.48 quadrillion > **NOTE:** > > Total historical sunk cost of war since 1900: military spending (\$170T) + property destruction (\$45T) + environmental (\$5T) + QALY value of lives (\$1.26Q). > > **Inputs**: > > - [Cumulative Military Spending (Fed Era)](#sec-cumulative_military_spending_fed_era): \$170 trillion > - [Cumulative Property Destruction from War Since 1900](#sec-war_property_destruction_since_1900): \$45 trillion (95% CI: \$30 trillion - \$60 trillion) > - [Cumulative Environmental Destruction from War Since 1900](#sec-war_environmental_destruction_since_1900): \$5 trillion (95% CI: \$2 trillion - \$10 trillion) > - [QALY Value of Life Lost to War Since 1900](#sec-war_qaly_value_lost_since_1900) 🔢: \$1.26 quadrillion > > $$ \begin{gathered} C_{war,hist} \\ = Spending_{mil,cum,fed} + D_{property} + D_{env} \\ + V_{war,QALY} \\ = \$170T + \$45T + \$5T + \$1260T \\ = \$1480T \end{gathered} $$ where: $$ \begin{gathered} V_{war,QALY} \\ = YLL_{war,total} \times Value_{QALY} \\ = 8.37B \times \$150K \\ = \$1260T \end{gathered} $$ where: $$ \begin{gathered} YLL_{war,total} \\ = Deaths_{war,1900} \times YLL_{war} \\ = 310M \times 27 \\ = 8.37B \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Total Historical Cost of War Since 1900** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | QALY Value of Life Lost to War Since 1900 (USD) | 0.9990 | Strong driver | > | Cumulative Property Destruction from War Since 1900 (USD) | 0.0267 | Minimal effect | > | Cumulative Environmental Destruction from War Since 1900 (USD) | 0.0058 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Total Historical Cost of War Since 1900 (10,000 simulations) > > **Simulation Results Summary: Total Historical Cost of War Since 1900** > > | Statistic | Value | > |:--------------------------------|---------------------------------------:| > | Baseline (deterministic) | \$1.48 quadrillion | > | Mean (expected value) | \$1.33 quadrillion | > | Median (50th percentile) | \$1.29 quadrillion | > | Standard Deviation | \$324 trillion | > | 90% Range (5th-95th percentile) | \[\$862 trillion, \$1.93 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Historical Cost of War Since 1900; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Total Historical Cost of War Since 1900 > > *This exceedance probability chart shows the likelihood that Total Historical Cost of War Since 1900 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War-Redirect Biological Aging Control Year: 1990 > **NOTE:** > > Counterfactual calendar year when biological aging becomes a treatable risk factor in the aggressive medical-redirect model. Uses the calculated aging pleading cutoff year. > > **Inputs**: > > - [War-Redirect Aging Pleading Cutoff Year](#sec-war_trial_redirect_aging_pleading_cutoff_year) 🔢: 1990 > > $$ Y_{aging,redirect} = Y_{aging,plead} = 1{,}990 = 1{,}990 $$ where: $$ \begin{gathered} Y_{aging,plead} \\ = Y_{disease,plead} + T_{aging,lag} \\ = 1{,}950 + 40 \\ = 1{,}990 \end{gathered} $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War-Redirect Biological Aging Control Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War-Redirect Aging Pleading Cutoff Year (year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War-Redirect Biological Aging Control Year (10,000 simulations) > > **Simulation Results Summary: War-Redirect Biological Aging Control Year** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 1990 | > | Mean (expected value) | 1993 | > | Median (50th percentile) | 1983 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[1962, 2060\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War-Redirect Biological Aging Control Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War-Redirect Biological Aging Control Year > > *This exceedance probability chart shows the likelihood that War-Redirect Biological Aging Control Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War-Redirect Aging Pleading Cutoff Year: 1990 > **NOTE:** > > Aggressive prosecutor pleading cutoff year for presumptive aging-death plaintiffs. Calculated as the disease cutoff plus the geroscience lag assumption. > > **Inputs**: > > - [War-Redirect Disease Pleading Cutoff Year](#sec-war_trial_redirect_disease_pleading_cutoff_year) 🔢: 1950 > - [War-Redirect Aging Lag After Disease Control](#sec-war_trial_redirect_aging_lag_after_disease_control_years): 40 years (95% CI: 10 years - 65 years) > > $$ \begin{gathered} Y_{aging,plead} \\ = Y_{disease,plead} + T_{aging,lag} \\ = 1{,}950 + 40 \\ = 1{,}990 \end{gathered} $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War-Redirect Aging Pleading Cutoff Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War-Redirect Disease Pleading Cutoff Year (year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War-Redirect Aging Pleading Cutoff Year (10,000 simulations) > > **Simulation Results Summary: War-Redirect Aging Pleading Cutoff Year** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 1990 | > | Mean (expected value) | 1993 | > | Median (50th percentile) | 1983 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[1962, 2060\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War-Redirect Aging Pleading Cutoff Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War-Redirect Aging Pleading Cutoff Year > > *This exceedance probability chart shows the likelihood that War-Redirect Aging Pleading Cutoff Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War-Redirect Disease Pleading Cutoff Year: 1950 > **NOTE:** > > Aggressive prosecutor pleading cutoff year for presumptive disease-death plaintiffs. Calculated as the 1900 redirect start year plus medical-toolchain bootstrap years plus the treaty-scale therapeutic queue-clearance years. > > **Inputs**: > > - [War-Redirect Medical Counterfactual Start Year](#sec-war_trial_redirect_start_year): 1900 > - [War-Redirect Medical Toolchain Bootstrap Years](#sec-war_trial_redirect_toolchain_bootstrap_years): 14 years (95% CI: 0 years - 40 years) > - [Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity](#sec-dfda_queue_clearance_years) 🔢: 36 years > > $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War-Redirect Disease Pleading Cutoff Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity (years) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War-Redirect Disease Pleading Cutoff Year (10,000 simulations) > > **Simulation Results Summary: War-Redirect Disease Pleading Cutoff Year** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 1950 | > | Mean (expected value) | 1953 | > | Median (50th percentile) | 1943 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[1922, 2020\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War-Redirect Disease Pleading Cutoff Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War-Redirect Disease Pleading Cutoff Year > > *This exceedance probability chart shows the likelihood that War-Redirect Disease Pleading Cutoff Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Excess Military Spending Above 1900 Freeze: \$135 trillion > **NOTE:** > > Dataset-derived aggregate: cumulative global military spending above a 1900 real-spending freeze, 1900-2024, calculated from knowledge/data/global-military-spending-1900-2024-constant-2023-usd.csv (Correlates of War NMC) as the sum of max(0, annual spending - 1900 baseline) across years. The stricter medical redirect pot, distinct from total cumulative military spending. > > **Inputs**: > > - [1900 Military Spending Freeze Baseline](#sec-war_trial_redirect_1900_freeze_baseline_annual) 📊: \$66.1 billion (95% CI: \$50 billion - \$90 billion) > > $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ > > **Methodology**:^([13](../references.llms.md#ref-correlates-of-war-nmc)) > > *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Excess Military Spending Above 1900 Freeze** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | 1900 Military Spending Freeze Baseline (USD/year) | -0.9996 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Excess Military Spending Above 1900 Freeze (10,000 simulations) > > **Simulation Results Summary: Excess Military Spending Above 1900 Freeze** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$135 trillion | > | Mean (expected value) | \$134 trillion | > | Median (50th percentile) | \$134 trillion | > | Standard Deviation | \$1.25 trillion | > | 90% Range (5th-95th percentile) | \[\$132 trillion, \$136 trillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Excess Military Spending Above 1900 Freeze; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Excess Military Spending Above 1900 Freeze > > *This exceedance probability chart shows the likelihood that Excess Military Spending Above 1900 Freeze will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Excess Military Spending Above 1900 Freeze in Clinical Trial Years: 29,937 years > **NOTE:** > > Clinical-trial years funded by military spending above a 1900 real-spending freeze. This is the literal 1900-freeze medical redirect capacity, not total cumulative military spending. > > **Inputs**: > > - [Excess Military Spending Above 1900 Freeze](#sec-war_trial_redirect_excess_military_spending_above_1900_freeze) 🔢: \$135 trillion > - [Annual Global Government Spending on Clinical Trials](#sec-global_government_clinical_trials_spending_annual) 📊: \$4.5 billion (95% CI: \$3 billion - \$6 billion) > > $$ \begin{gathered} Years_{excess1900 \to trials,gov} \\ = \frac{Spending_{mil,excess1900}}{Spending_{trials,gov}} \\ = \frac{\$135T}{\$4.5B} \\ = 29{,}900 \end{gathered} $$ where: $$ \begin{gathered} Spending_{mil,excess1900} \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - Spending_{mil,1900}\right) \\ = \sum_{t=1900}^{2024} \max\left(0, Spending_{mil,t} - \$66.1B\right) \\ = \$135T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Excess Military Spending Above 1900 Freeze in Clinical Trial Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Annual Global Government Spending on Clinical Trials (USD) | -0.9769 | Strong driver | > | Excess Military Spending Above 1900 Freeze (USD) | 0.0468 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Excess Military Spending Above 1900 Freeze in Clinical Trial Years (10,000 simulations) > > **Simulation Results Summary: Excess Military Spending Above 1900 Freeze in Clinical Trial Years** > > | Statistic | Value | > |:--------------------------------|-------------------:| > | Baseline (deterministic) | 29,937 | > | Mean (expected value) | 31,347 | > | Median (50th percentile) | 30,689 | > | Standard Deviation | 6,210 | > | 90% Range (5th-95th percentile) | \[22,459, 43,917\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Excess Military Spending Above 1900 Freeze in Clinical Trial Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Excess Military Spending Above 1900 Freeze in Clinical Trial Years > > *This exceedance probability chart shows the likelihood that Excess Military Spending Above 1900 Freeze in Clinical Trial Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War-Redirect Infectious Disease Control Year: 1950 > **NOTE:** > > Counterfactual calendar year for practical infectious-disease control in the aggressive medical-redirect model. Uses the calculated disease pleading cutoff year. > > **Inputs**: > > - [War-Redirect Disease Pleading Cutoff Year](#sec-war_trial_redirect_disease_pleading_cutoff_year) 🔢: 1950 > > $$ \begin{gathered} Y_{infectious,redirect} \\ = Y_{disease,plead} \\ = 1{,}950 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War-Redirect Infectious Disease Control Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War-Redirect Disease Pleading Cutoff Year (year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War-Redirect Infectious Disease Control Year (10,000 simulations) > > **Simulation Results Summary: War-Redirect Infectious Disease Control Year** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 1950 | > | Mean (expected value) | 1953 | > | Median (50th percentile) | 1943 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[1922, 2020\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War-Redirect Infectious Disease Control Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War-Redirect Infectious Disease Control Year > > *This exceedance probability chart shows the likelihood that War-Redirect Infectious Disease Control Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Net Trial Budget After Toolchain Reserve: \$150 trillion > **NOTE:** > > Cumulative military spending since the Federal Reserve era after reserving the aggressive prosecutor’s medical-toolchain prize budget. > > **Inputs**: > > - [Cumulative Military Spending (Fed Era)](#sec-cumulative_military_spending_fed_era): \$170 trillion > - [War Medical Toolchain Prize Budget](#sec-war_medical_toolchain_prize_budget): \$20 trillion (95% CI: \$5 trillion - \$50 trillion) > > $$ \begin{gathered} B_{trials,net} \\ = Spending_{mil,cum,fed} - C_{tool,prize} \\ = \$170T - \$20T \\ = \$150T \end{gathered} $$ > > *? Low confidence* > > #### Sensitivity Analysis > > #### Monte Carlo Distribution > > **Monte Carlo note:** War Trial Redirect Net Trial Budget After Toolchain Reserve is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.500e+02) > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$150 trillion | > | Mean (expected value) | \$150 trillion | > | Median (50th percentile) | \$150 trillion | > | Standard Deviation | \$0 | > | 90% Range (5th-95th percentile) | \[\$150 trillion, \$150 trillion\] | > > #### Exceedance Probability > > **Exceedance note:** War Trial Redirect Net Trial Budget After Toolchain Reserve collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.500e+02) > > Approximate deterministic value: **\$150 trillion** ### War-Redirect Major Non-Aging Disease Control Year: 1950 > **NOTE:** > > Counterfactual calendar year for practical control of major non-aging disease burden in the aggressive medical-redirect model. Uses the calculated disease pleading cutoff year. > > **Inputs**: > > - [War-Redirect Disease Pleading Cutoff Year](#sec-war_trial_redirect_disease_pleading_cutoff_year) 🔢: 1950 > > $$ Y_{disease,redirect} = Y_{disease,plead} = 1{,}950 = 1{,}950 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War-Redirect Major Non-Aging Disease Control Year** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War-Redirect Disease Pleading Cutoff Year (year) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War-Redirect Major Non-Aging Disease Control Year (10,000 simulations) > > **Simulation Results Summary: War-Redirect Major Non-Aging Disease Control Year** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 1950 | > | Mean (expected value) | 1953 | > | Median (50th percentile) | 1943 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[1922, 2020\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War-Redirect Major Non-Aging Disease Control Year; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War-Redirect Major Non-Aging Disease Control Year > > *This exceedance probability chart shows the likelihood that War-Redirect Major Non-Aging Disease Control Year will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Patient Slots Funded: 161 billion patient-slots > **NOTE:** > > Patient-slots funded by the net redirected war budget at pragmatic trial cost per patient. Patient-slots are repeated experimental opportunities, not unique people. > > **Inputs**: > > - [War Trial Redirect Net Trial Budget After Toolchain Reserve](#sec-war_trial_redirect_net_trial_budget) 🔢: \$150 trillion > - [Pragmatic Trial Cost per Patient](#sec-dfda_pragmatic_trial_cost_per_patient) 📊: \$929 (95% CI: \$97 - \$3,000) > > $$ \begin{gathered} N_{slots,war} \\ = \frac{B_{trials,net}}{Cost_{pragmatic,pt}} \\ = \frac{\$150T}{\$929} \\ = 161B \end{gathered} $$ where: $$ \begin{gathered} B_{trials,net} \\ = Spending_{mil,cum,fed} - C_{tool,prize} \\ = \$170T - \$20T \\ = \$150T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War Trial Redirect Patient Slots Funded** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Pragmatic Trial Cost per Patient (USD/patient) | -0.6862 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War Trial Redirect Patient Slots Funded (10,000 simulations) > > **Simulation Results Summary: War Trial Redirect Patient Slots Funded** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 161 billion | > | Mean (expected value) | 265 billion | > | Median (50th percentile) | 207 billion | > | Standard Deviation | 204 billion | > | 90% Range (5th-95th percentile) | \[63.6 billion, 647 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War Trial Redirect Patient Slots Funded; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War Trial Redirect Patient Slots Funded > > *This exceedance probability chart shows the likelihood that War Trial Redirect Patient Slots Funded will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Patient Slots Per Living Human: 20.2 patient-slots/person > **NOTE:** > > Patient-slots funded per living human, used to avoid implying hundreds of billions of unique patients. It means repeated trial opportunities across time and indications. > > **Inputs**: > > - [War Trial Redirect Patient Slots Funded](#sec-war_trial_redirect_patient_slots_funded) 🔢: 161 billion patient-slots > - [Global Population in 2024](#sec-global_population_2024) 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people) > > $$ \begin{gathered} N_{slots,pc} \\ = \frac{N_{slots,war}}{Pop_{global}} \\ = \frac{161B}{8B} \\ = 20.2 \end{gathered} $$ where: $$ \begin{gathered} N_{slots,war} \\ = \frac{B_{trials,net}}{Cost_{pragmatic,pt}} \\ = \frac{\$150T}{\$929} \\ = 161B \end{gathered} $$ where: $$ \begin{gathered} B_{trials,net} \\ = Spending_{mil,cum,fed} - C_{tool,prize} \\ = \$170T - \$20T \\ = \$150T \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War Trial Redirect Patient Slots Per Living Human** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War Trial Redirect Patient Slots Funded (patient-slots) | 0.9999 | Strong driver | > | Global Population in 2024 (of people) | -0.0160 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War Trial Redirect Patient Slots Per Living Human (10,000 simulations) > > **Simulation Results Summary: War Trial Redirect Patient Slots Per Living Human** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 20.2 | > | Mean (expected value) | 33.1 | > | Median (50th percentile) | 25.9 | > | Standard Deviation | 25.5 | > | 90% Range (5th-95th percentile) | \[7.99, 81.2\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War Trial Redirect Patient Slots Per Living Human; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War Trial Redirect Patient Slots Per Living Human > > *This exceedance probability chart shows the likelihood that War Trial Redirect Patient Slots Per Living Human will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Post-Cutoff Aging Plaintiffs: 1.78 billion plaintiffs > **NOTE:** > > Aggressive prosecutor aging intake count after the aging cutoff year. This overlaps with the broader disease-death plaintiff class because the annual-deaths parameter covers all disease and aging deaths; it is an intake class, not an additive damages line. > > **Inputs**: > > - [War Trial Redirect Post-Cutoff Aging Years](#sec-war_trial_redirect_post_cutoff_aging_years) 🔢: 35 years > - [Annual Deaths from All Diseases and Aging Globally](#sec-global_annual_deaths_curable_diseases) 📊: 55 million deaths/year (SE: ±5 million deaths/year) > - [Eventually Avoidable Death Percentage](#sec-eventually_avoidable_death_pct): 92.6% (95% CI: 50% - 98%) > > $$ \begin{gathered} N_{plaintiffs,aging} \\ = T_{post,aging} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 35 \times 55M \times 92.6\% \\ = 1.78B \end{gathered} $$ where: $$ T_{post,aging} = Y_{plead,end} - Y_{aging,plead} + 1 $$ where: $$ \begin{gathered} Y_{aging,plead} \\ = Y_{disease,plead} + T_{aging,lag} \\ = 1{,}950 + 40 \\ = 1{,}990 \end{gathered} $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War Trial Redirect Post-Cutoff Aging Plaintiffs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War Trial Redirect Post-Cutoff Aging Years (years) | 0.9771 | Strong driver | > | Eventually Avoidable Death Percentage (percentage) | 0.1118 | Weak driver | > | Annual Deaths from All Diseases and Aging Globally (deaths/year) | 0.0833 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War Trial Redirect Post-Cutoff Aging Plaintiffs (10,000 simulations) > > **Simulation Results Summary: War Trial Redirect Post-Cutoff Aging Plaintiffs** > > | Statistic | Value | > |:--------------------------------|--------------------------------:| > | Baseline (deterministic) | 1.78 billion | > | Mean (expected value) | 1.59 billion | > | Median (50th percentile) | 2.01 billion | > | Standard Deviation | 1.69 billion | > | 90% Range (5th-95th percentile) | \[-1.72 billion, 3.41 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War Trial Redirect Post-Cutoff Aging Plaintiffs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War Trial Redirect Post-Cutoff Aging Plaintiffs > > *This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Aging Plaintiffs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Post-Cutoff Aging Years: 35 years > **NOTE:** > > Inclusive number of years in the aggressive prosecutor aging-death intake window. > > **Inputs**: > > - [War-Redirect Pleading End Year](#sec-war_trial_redirect_pleading_end_year): 2024 > - [War-Redirect Aging Pleading Cutoff Year](#sec-war_trial_redirect_aging_pleading_cutoff_year) 🔢: 1990 > > $$ T_{post,aging} = Y_{plead,end} - Y_{aging,plead} + 1 $$ where: $$ \begin{gathered} Y_{aging,plead} \\ = Y_{disease,plead} + T_{aging,lag} \\ = 1{,}950 + 40 \\ = 1{,}990 \end{gathered} $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War Trial Redirect Post-Cutoff Aging Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War-Redirect Aging Pleading Cutoff Year (year) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War Trial Redirect Post-Cutoff Aging Years (10,000 simulations) > > **Simulation Results Summary: War Trial Redirect Post-Cutoff Aging Years** > > | Statistic | Value | > |:--------------------------------|------------:| > | Baseline (deterministic) | 35 | > | Mean (expected value) | 31.5 | > | Median (50th percentile) | 42 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[-35, 63\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War Trial Redirect Post-Cutoff Aging Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War Trial Redirect Post-Cutoff Aging Years > > *This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Aging Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Post-Cutoff Disease DALYs: 200 billion DALYs > **NOTE:** > > Aggressive prosecutor estimate of post-cutoff avoidable disease DALYs. This measures disease-years, disability, and suffering after the disease cutoff, separate from the death-plaintiff VSL count. > > **Inputs**: > > - [War Trial Redirect Post-Cutoff Disease Years](#sec-war_trial_redirect_post_cutoff_disease_years) 🔢: 75 years > - [Global Annual DALY Burden](#sec-global_annual_daly_burden) 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year) > - [Eventually Avoidable DALY Percentage](#sec-eventually_avoidable_daly_pct): 92.6% (95% CI: 50% - 98%) > > $$ \begin{gathered} DALYs_{post,disease} \\ = T_{post,disease} \times DALYs_{global,ann} \times Pct_{avoid,DALY} \\ = 75 \times 2.88B \times 92.6\% \\ = 200B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War Trial Redirect Post-Cutoff Disease DALYs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War Trial Redirect Post-Cutoff Disease Years (years) | 0.9529 | Strong driver | > | Eventually Avoidable DALY Percentage (percentage) | 0.2429 | Weak driver | > | Global Annual DALY Burden (DALYs/year) | 0.1055 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War Trial Redirect Post-Cutoff Disease DALYs (10,000 simulations) > > **Simulation Results Summary: War Trial Redirect Post-Cutoff Disease DALYs** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 200 billion | > | Mean (expected value) | 190 billion | > | Median (50th percentile) | 212 billion | > | Standard Deviation | 91.2 billion | > | 90% Range (5th-95th percentile) | \[13.5 billion, 289 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War Trial Redirect Post-Cutoff Disease DALYs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War Trial Redirect Post-Cutoff Disease DALYs > > *This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Disease DALYs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Post-Cutoff Disease Plaintiffs: 3.82 billion plaintiffs > **NOTE:** > > Aggressive prosecutor pleading count for post-cutoff disease-death plaintiffs: inclusive years from the disease cutoff through the pleading end year, multiplied by annual disease deaths and the eventually avoidable death share. > > **Inputs**: > > - [War Trial Redirect Post-Cutoff Disease Years](#sec-war_trial_redirect_post_cutoff_disease_years) 🔢: 75 years > - [Annual Deaths from All Diseases and Aging Globally](#sec-global_annual_deaths_curable_diseases) 📊: 55 million deaths/year (SE: ±5 million deaths/year) > - [Eventually Avoidable Death Percentage](#sec-eventually_avoidable_death_pct): 92.6% (95% CI: 50% - 98%) > > $$ \begin{gathered} N_{plaintiffs,disease} \\ = T_{post,disease} \times Deaths_{curable,ann} \times Pct_{avoid,death} \\ = 75 \times 55M \times 92.6\% \\ = 3.82B \end{gathered} $$ where: $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War Trial Redirect Post-Cutoff Disease Plaintiffs** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War Trial Redirect Post-Cutoff Disease Years (years) | 0.9377 | Strong driver | > | Eventually Avoidable Death Percentage (percentage) | 0.2466 | Weak driver | > | Annual Deaths from All Diseases and Aging Globally (deaths/year) | 0.1846 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War Trial Redirect Post-Cutoff Disease Plaintiffs (10,000 simulations) > > **Simulation Results Summary: War Trial Redirect Post-Cutoff Disease Plaintiffs** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 3.82 billion | > | Mean (expected value) | 3.61 billion | > | Median (50th percentile) | 4 billion | > | Standard Deviation | 1.76 billion | > | 90% Range (5th-95th percentile) | \[245 million, 5.68 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War Trial Redirect Post-Cutoff Disease Plaintiffs; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War Trial Redirect Post-Cutoff Disease Plaintiffs > > *This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Disease Plaintiffs will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### War Trial Redirect Post-Cutoff Disease Years: 75 years > **NOTE:** > > Inclusive number of years in the aggressive prosecutor disease-death plaintiff window. > > **Inputs**: > > - [War-Redirect Pleading End Year](#sec-war_trial_redirect_pleading_end_year): 2024 > - [War-Redirect Disease Pleading Cutoff Year](#sec-war_trial_redirect_disease_pleading_cutoff_year) 🔢: 1950 > > $$ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 $$ where: $$ \begin{gathered} Y_{disease,plead} \\ = Y_{redirect,start} + T_{tool,bootstrap} + T_{queue,trial} \\ = 1{,}900 + 14 + 36 \\ = 1{,}950 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ *? Low confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for War Trial Redirect Post-Cutoff Disease Years** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | War-Redirect Disease Pleading Cutoff Year (year) | -1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: War Trial Redirect Post-Cutoff Disease Years (10,000 simulations) > > **Simulation Results Summary: War Trial Redirect Post-Cutoff Disease Years** > > | Statistic | Value | > |:--------------------------------|-----------:| > | Baseline (deterministic) | 75 | > | Mean (expected value) | 71.5 | > | Median (50th percentile) | 82 | > | Standard Deviation | 32.9 | > | 90% Range (5th-95th percentile) | \[5, 103\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for War Trial Redirect Post-Cutoff Disease Years; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: War Trial Redirect Post-Cutoff Disease Years > > *This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Disease Years will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Global Patients Willing to Participate in Clinical Trials: 1.08 billion people > **NOTE:** > > Global chronic disease patients willing to participate in trials (2.4B × 44.8%) > > **Inputs**: > > - [Global Population with Chronic Diseases](#sec-current_disease_patients_global) 📊: 2.4 billion people (95% CI: 2 billion people - 2.8 billion people) > - [Patient Willingness to Participate in Clinical Trials](#sec-patient_willingness_trial_participation_pct) 📊: 44.8% (95% CI: 40% - 50%) > > $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ > > *~ Medium confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Global Patients Willing to Participate in Clinical Trials** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Global Population with Chronic Diseases (people) | 0.8363 | Strong driver | > | Patient Willingness to Participate in Clinical Trials (percentage) | 0.5518 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Global Patients Willing to Participate in Clinical Trials (10,000 simulations) > > **Simulation Results Summary: Global Patients Willing to Participate in Clinical Trials** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | 1.08 billion | > | Mean (expected value) | 1.07 billion | > | Median (50th percentile) | 1.07 billion | > | Standard Deviation | 103 million | > | 90% Range (5th-95th percentile) | \[911 million, 1.25 billion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Global Patients Willing to Participate in Clinical Trials; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Global Patients Willing to Participate in Clinical Trials > > *This exceedance probability chart shows the likelihood that Global Patients Willing to Participate in Clinical Trials will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Disease Cure Fraction (15yr, Full Implementation): 100% > **NOTE:** > > Wishonia disease-cure fraction over 15 years under full implementation. Uses full trial-capacity scaling and applies an upper bound of 100% of untreated disease classes. > > **Inputs**: > > - [Diseases Getting First Treatment Per Year](#sec-new_disease_first_treatments_per_year) 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year) > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > - [Wishonia Military Reallocation Physical Max Share](#sec-wishonia_military_reallocation_physical_max_share) 🔢: 87.6% > - [Maximum Trial Capacity Multiplier (Physical Limit)](#sec-dfda_max_trial_capacity_multiplier_physical) 🔢: 566x > - [Diseases Without Effective Treatment](#sec-diseases_without_effective_treatment) 🔢: 6,650 diseases > > $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ \#### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Wishonia Disease Cure Fraction (15yr, Full Implementation) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 100% | > | Mean (expected value) | 100% | > | Median (50th percentile) | 100% | > | Standard Deviation | 0% | > | 90% Range (5th-95th percentile) | \[100%, 100%\] | > > #### Exceedance Probability > > **Exceedance note:** Wishonia Disease Cure Fraction (15yr, Full Implementation) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **100%** ### Wishonia Disease Cure Fraction (20yr, Full Implementation): 100% > **NOTE:** > > Wishonia disease-cure fraction over 20 years under full implementation. Uses full trial-capacity scaling and applies an upper bound of 100% of untreated disease classes. > > **Inputs**: > > - [Diseases Getting First Treatment Per Year](#sec-new_disease_first_treatments_per_year) 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year) > - [Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding](#sec-dfda_trial_capacity_multiplier) 🔢: 12.3x > - [Wishonia Military Reallocation Physical Max Share](#sec-wishonia_military_reallocation_physical_max_share) 🔢: 87.6% > - [Maximum Trial Capacity Multiplier (Physical Limit)](#sec-dfda_max_trial_capacity_multiplier_physical) 🔢: 566x > - [Diseases Without Effective Treatment](#sec-diseases_without_effective_treatment) 🔢: 6,650 diseases > > $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ \#### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Wishonia Disease Cure Fraction (20yr, Full Implementation) is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 100% | > | Mean (expected value) | 100% | > | Median (50th percentile) | 100% | > | Standard Deviation | 0% | > | 90% Range (5th-95th percentile) | \[100%, 100%\] | > > #### Exceedance Probability > > **Exceedance note:** Wishonia Disease Cure Fraction (20yr, Full Implementation) collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **100%** ### Wishonia Extra HALE Gain at Year 15: 16.7 years > **NOTE:** > > Additional healthy years at year 15 from optimal-governance public-health improvements plus partial realization of longer-run longevity gains. This removes the implicit cap at today’s life expectancy and lets Wishonia exceed it for non-arbitrary reasons. > > **Inputs**: > > - [Wishonia Disease Cure Fraction (15yr, Full Implementation)](#sec-wishonia_disease_cure_fraction_15yr) 🔢: 100% > - [Best-Practice Life Expectancy Gain](#sec-best_practice_life_expectancy_gain) 🔢: 10.7 years > - [Life Extension from Treaty Research Acceleration](#sec-life_extension_years) 📊: 20 years (95% CI: 5 years - 100 years) > - [HALE Longevity Realization Share (Year 15)](#sec-hale_longevity_realization_share_year_15): 30% > > $$ \begin{gathered} \Delta HALE_{wish,extra,15} \\ = f_{cure,15,wish} \times (\Delta LE_{best} \\ + T_{extend} \times \rho_{HALE,15}) \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta LE_{best} \\ = \max\left(LE_{CH}, LE_{SG}\right) - LE_{global} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Extra HALE Gain at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Life Extension from Treaty Research Acceleration (years) | 0.9613 | Strong driver | > | Best-Practice Life Expectancy Gain (years) | 0.2814 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Extra HALE Gain at Year 15 (10,000 simulations) > > **Simulation Results Summary: Wishonia Extra HALE Gain at Year 15** > > | Statistic | Value | > |:--------------------------------|-----------:| > | Baseline (deterministic) | 16.7 | > | Mean (expected value) | 16.6 | > | Median (50th percentile) | 14.9 | > | Standard Deviation | 6.8 | > | 90% Range (5th-95th percentile) | \[10, 29\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Extra HALE Gain at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Extra HALE Gain at Year 15 > > *This exceedance probability chart shows the likelihood that Wishonia Extra HALE Gain at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia HALE Gain at Year 15: 26.8 years > **NOTE:** > > HALE improvement at year 15 under Wishonia Trajectory. It includes closing the current HALE gap, reaching today’s best-practice life expectancy through optimal governance/public health, and a conservative partial realization of longer-run longevity gains. > > **Inputs**: > > - [Wishonia Disease Cure Fraction (15yr, Full Implementation)](#sec-wishonia_disease_cure_fraction_15yr) 🔢: 100% > - [Global Healthy Life Expectancy (HALE)](#sec-global_hale_current) 📊: 63.3 years (SE: ±1.5 years) > - [Global Life Expectancy (2024)](#sec-global_life_expectancy_2024) 📊: 73.4 years (SE: ±2 years) > - [Wishonia Extra HALE Gain at Year 15](#sec-wishonia_extra_hale_gain_year_15) 🔢: 16.7 years > > $$ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \Delta_{HALE} \\ + \Delta HALE_{wish,extra,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,extra,15} \\ = f_{cure,15,wish} \times (\Delta LE_{best} \\ + T_{extend} \times \rho_{HALE,15}) \end{gathered} $$ where: $$ \begin{gathered} \Delta LE_{best} \\ = \max\left(LE_{CH}, LE_{SG}\right) - LE_{global} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia HALE Gain at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Extra HALE Gain at Year 15 (years) | 1.0181 | Strong driver | > | Global Life Expectancy (2024) (years) | 0.2865 | Weak driver | > | Global Healthy Life Expectancy (HALE) (years) | -0.2261 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia HALE Gain at Year 15 (10,000 simulations) > > **Simulation Results Summary: Wishonia HALE Gain at Year 15** > > | Statistic | Value | > |:--------------------------------|---------------:| > | Baseline (deterministic) | 26.8 | > | Mean (expected value) | 26.7 | > | Median (50th percentile) | 24.9 | > | Standard Deviation | 6.68 | > | 90% Range (5th-95th percentile) | \[20.6, 39.2\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia HALE Gain at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia HALE Gain at Year 15 > > *This exceedance probability chart shows the likelihood that Wishonia HALE Gain at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia HALE Value Per Capita: \$4.02 million > **NOTE:** > > Economic value of Wishonia Trajectory HALE gains at year 15 using the standard QALY value. > > **Inputs**: > > - [Wishonia HALE Gain at Year 15](#sec-wishonia_hale_gain_year_15) 🔢: 26.8 years > - [Standard Economic Value per QALY](#sec-standard_economic_qaly_value_usd) 📊: \$150,000 (SE: ±\$30,000) > > $$ \begin{gathered} Value_{HALE,wish} \\ = \Delta HALE_{wish,15} \times Value_{QALY} \\ = 26.8 \times \$150K \\ = \$4.02M \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \Delta_{HALE} \\ + \Delta HALE_{wish,extra,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,extra,15} \\ = f_{cure,15,wish} \times (\Delta LE_{best} \\ + T_{extend} \times \rho_{HALE,15}) \end{gathered} $$ where: $$ \begin{gathered} \Delta LE_{best} \\ = \max\left(LE_{CH}, LE_{SG}\right) - LE_{global} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia HALE Value Per Capita** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia HALE Gain at Year 15 (years) | 0.7938 | Strong driver | > | Standard Economic Value per QALY (USD/QALY) | 0.5729 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia HALE Value Per Capita (10,000 simulations) > > **Simulation Results Summary: Wishonia HALE Value Per Capita** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$4.02 million | > | Mean (expected value) | \$4 million | > | Median (50th percentile) | \$3.78 million | > | Standard Deviation | \$1.27 million | > | 90% Range (5th-95th percentile) | \[\$2.47 million, \$6.29 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia HALE Value Per Capita; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia HALE Value Per Capita > > *This exceedance probability chart shows the likelihood that Wishonia HALE Value Per Capita will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Military Reallocation Physical Max Share: 87.6% > **NOTE:** > > Maximum physically demonstrated military reallocation share, anchored to post-WW2 US demobilization. > > **Inputs**: > > - [Percentage Military Spending Cut After WW2](#sec-post_ww2_military_cut_pct) 🔢: 87.6% > > $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ *✓ High confidence* > > #### Monte Carlo Distribution > > **Monte Carlo note:** Wishonia Military Reallocation Physical Max Share is effectively deterministic across the current sampled inputs, so a histogram would mostly visualize floating-point residue instead of real uncertainty. (range 0.000e+00 \<= tolerance 1.000e-12) > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 87.6% | > | Mean (expected value) | 87.6% | > | Median (50th percentile) | 87.6% | > | Standard Deviation | 0% | > | 90% Range (5th-95th percentile) | \[87.6%, 87.6%\] | > > #### Exceedance Probability > > **Exceedance note:** Wishonia Military Reallocation Physical Max Share collapses to an effectively single value under the current Monte Carlo assumptions, so the exceedance curve would be a near-vertical step function. (range 0.000e+00 \<= tolerance 1.000e-12) > > Approximate deterministic value: **87.6%** ### Wishonia Personal Upside (Income + Health): \$40.5 million > **NOTE:** > > Personal upside under Wishonia Trajectory: lifetime income gain plus valued healthy-life gains. > > **Inputs**: > > - [Wishonia Trajectory Lifetime Income Gain (Per Capita)](#sec-wishonia_trajectory_lifetime_income_gain_per_capita) 🔢: \$36.4 million > - [Wishonia HALE Value Per Capita](#sec-wishonia_hale_value_per_capita) 🔢: \$4.02 million > > $$ \begin{gathered} Upside_{income+health,wish} \\ = \Delta Y_{lifetime,wish} + Value_{HALE,wish} \\ = \$36.4M + \$4.02M \\ = \$40.5M \end{gathered} $$ where: $$ \begin{gathered} \Delta Y_{lifetime,wish} \\ = Y_{cum,wish} - Y_{cum,earth} \\ = \$37.3M - \$904K \\ = \$36.4M \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,wish} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,wish})((1+g_{pc,wish})^{20}-1)}{g_{pc,wish}} \\ + \bar{y}_{wish,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{wish,20} \\ = \frac{GDP_{wish,20}}{Pop_{2045}} \\ = \frac{\$10700T}{9.2B} \\ = \$1.16M \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} Value_{HALE,wish} \\ = \Delta HALE_{wish,15} \times Value_{QALY} \\ = 26.8 \times \$150K \\ = \$4.02M \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \Delta_{HALE} \\ + \Delta HALE_{wish,extra,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,extra,15} \\ = f_{cure,15,wish} \times (\Delta LE_{best} \\ + T_{extend} \times \rho_{HALE,15}) \end{gathered} $$ where: $$ \begin{gathered} \Delta LE_{best} \\ = \max\left(LE_{CH}, LE_{SG}\right) - LE_{global} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Personal Upside (Income + Health)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory Lifetime Income Gain (Per Capita) (USD) | 0.9989 | Strong driver | > | Wishonia HALE Value Per Capita (USD/person) | 0.0449 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Personal Upside (Income + Health) (10,000 simulations) > > **Simulation Results Summary: Wishonia Personal Upside (Income + Health)** > > | Statistic | Value | > |:--------------------------------|----------------------------------:| > | Baseline (deterministic) | \$40.5 million | > | Mean (expected value) | \$46.2 million | > | Median (50th percentile) | \$38.9 million | > | Standard Deviation | \$28.4 million | > | 90% Range (5th-95th percentile) | \[\$17.3 million, \$101 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Personal Upside (Income + Health); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Personal Upside (Income + Health) > > *This exceedance probability chart shows the likelihood that Wishonia Personal Upside (Income + Health) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Projected HALE at Year 15: 90.1 years > **NOTE:** > > Projected global HALE at year 15 under Wishonia Trajectory. Full implementation closes the entire disease gap, pushing HALE toward life expectancy. > > **Inputs**: > > - [Global Healthy Life Expectancy (HALE)](#sec-global_hale_current) 📊: 63.3 years (SE: ±1.5 years) > - [Wishonia HALE Gain at Year 15](#sec-wishonia_hale_gain_year_15) 🔢: 26.8 years > > $$ \begin{gathered} HALE_{wish,15} \\ = HALE_{0} + \Delta HALE_{wish,15} \\ = 63.3 + 26.8 \\ = 90.1 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \Delta_{HALE} \\ + \Delta HALE_{wish,extra,15} \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \Delta HALE_{wish,extra,15} \\ = f_{cure,15,wish} \times (\Delta LE_{best} \\ + T_{extend} \times \rho_{HALE,15}) \end{gathered} $$ where: $$ \begin{gathered} \Delta LE_{best} \\ = \max\left(LE_{CH}, LE_{SG}\right) - LE_{global} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Projected HALE at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia HALE Gain at Year 15 (years) | 1.0218 | Strong driver | > | Global Healthy Life Expectancy (HALE) (years) | 0.2310 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Projected HALE at Year 15 (10,000 simulations) > > **Simulation Results Summary: Wishonia Projected HALE at Year 15** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 90.1 | > | Mean (expected value) | 90 | > | Median (50th percentile) | 88 | > | Standard Deviation | 6.54 | > | 90% Range (5th-95th percentile) | \[84.9, 102\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Projected HALE at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Projected HALE at Year 15 > > *This exceedance probability chart shows the likelihood that Wishonia Projected HALE at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory Average Income at Year 15: \$503,790 > **NOTE:** > > Average income (GDP per capita) at year 15 under the Wishonia Trajectory. > > **Inputs**: > > - [Wishonia Trajectory GDP at Year 15](#sec-wishonia_trajectory_gdp_year_15) 🔢: \$4.48 quadrillion > - [Global Population 2040 (Projected)](#sec-global_population_2040_projected) 📊: 8.9 billion of people > > $$ \begin{gathered} \bar{y}_{wish,15} \\ = \frac{GDP_{wish,15}}{Pop_{2040}} \\ = \frac{\$4480T}{8.9B} \\ = \$504K \end{gathered} $$ where: $$ GDP_{wish,15}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{12} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory Average Income at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory GDP at Year 15 (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory Average Income at Year 15 (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory Average Income at Year 15** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$503,790 | > | Mean (expected value) | \$541,287 | > | Median (50th percentile) | \$476,361 | > | Standard Deviation | \$284,802 | > | 90% Range (5th-95th percentile) | \[\$223,452, \$1.08 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory Average Income at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory Average Income at Year 15 > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory Average Income at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory Average Income at Year 20: \$1.16 million > **NOTE:** > > Average income (GDP per capita) at year 20 under the Wishonia Trajectory. > > **Inputs**: > > - [Wishonia Trajectory GDP at Year 20](#sec-wishonia_trajectory_gdp_year_20) 🔢: \$10.7 quadrillion > - [Global Population 2045 (Projected)](#sec-global_population_2045_projected) 📊: 9.2 billion of people > > $$ \begin{gathered} \bar{y}_{wish,20} \\ = \frac{GDP_{wish,20}}{Pop_{2045}} \\ = \frac{\$10700T}{9.2B} \\ = \$1.16M \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory Average Income at Year 20** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory GDP at Year 20 (USD) | 1.0000 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory Average Income at Year 20 (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory Average Income at Year 20** > > | Statistic | Value | > |:--------------------------------|------------------------------:| > | Baseline (deterministic) | \$1.16 million | > | Mean (expected value) | \$1.32 million | > | Median (50th percentile) | \$1.09 million | > | Standard Deviation | \$885,437 | > | 90% Range (5th-95th percentile) | \[\$429,664, \$3.04 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory Average Income at Year 20; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory Average Income at Year 20 > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory Average Income at Year 20 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory CAGR (20 Years): 25.4% > **NOTE:** > > Compound annual growth rate implied by Wishonia Trajectory GDP trajectory over 20 years. > > **Inputs**: > > - [Wishonia Trajectory GDP at Year 20](#sec-wishonia_trajectory_gdp_year_20) 🔢: \$10.7 quadrillion > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > > $$ \begin{gathered} g_{wish,CAGR} \\ = \left(\frac{GDP_{wish,20}}{GDP_{global}}\right)^{\frac{1}{20}} - 1 \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory CAGR (20 Years)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory GDP at Year 20 (USD) | 0.9246 | Strong driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory CAGR (20 Years) (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory CAGR (20 Years)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 25.4% | > | Mean (expected value) | 25.2% | > | Median (50th percentile) | 25% | > | Standard Deviation | 3.72% | > | 90% Range (5th-95th percentile) | \[19.3%, 31.6%\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory CAGR (20 Years); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory CAGR (20 Years) > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory CAGR (20 Years) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory Cumulative Lifetime Income (Per Capita): \$37.3 million > **NOTE:** > > Cumulative per-capita income over an average remaining lifespan under Wishonia Trajectory. Uses implied per-capita CAGR for years 1-20, then current-trajectory per-capita growth from the year-20 level. Conservative: assumes no further acceleration beyond year 20. > > **Inputs**: > > - [Global Average Income (2025 Baseline)](#sec-global_avg_income_2025) 🔢: \$14,375 > - [Wishonia Trajectory Average Income at Year 20](#sec-wishonia_trajectory_avg_income_year_20) 🔢: \$1.16 million > - [Current Trajectory Average Income at Year 20](#sec-current_trajectory_avg_income_year_20) 🔢: \$20,483 > - [Average Remaining Years (Median Person)](#sec-global_avg_remaining_years) 🔢: 42.9 years > > $$ \begin{gathered} Y_{cum,wish} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,wish})((1+g_{pc,wish})^{20}-1)}{g_{pc,wish}} \\ + \bar{y}_{wish,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory Cumulative Lifetime Income (Per Capita)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory Average Income at Year 20 (USD) | 0.9880 | Strong driver | > | Average Remaining Years (Median Person) (years) | 0.1339 | Weak driver | > | Global Average Income (2025 Baseline) (USD) | -0.0090 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory Cumulative Lifetime Income (Per Capita) (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory Cumulative Lifetime Income (Per Capita)** > > | Statistic | Value | > |:--------------------------------|-----------------------------------:| > | Baseline (deterministic) | \$37.3 million | > | Mean (expected value) | \$43.1 million | > | Median (50th percentile) | \$35.8 million | > | Standard Deviation | \$28.3 million | > | 90% Range (5th-95th percentile) | \[\$14.4 million, \$97.9 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory Cumulative Lifetime Income (Per Capita); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory Cumulative Lifetime Income (Per Capita) > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory Cumulative Lifetime Income (Per Capita) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15): 26.9x > **NOTE:** > > Wishonia Trajectory GDP at year 15 as a multiple of current trajectory GDP at year 15. > > **Inputs**: > > - [Wishonia Trajectory GDP at Year 15](#sec-wishonia_trajectory_gdp_year_15) 🔢: \$4.48 quadrillion > - [Current Trajectory GDP at Year 15](#sec-current_trajectory_gdp_year_15) 🔢: \$167 trillion > > $$ \begin{gathered} k_{wish:base,15} \\ = \frac{GDP_{wish,15}}{GDP_{base,15}} \\ = \frac{\$4480T}{\$167T} \\ = 26.9 \end{gathered} $$ where: $$ GDP_{wish,15}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{12} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,15,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{15}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ GDP_{base,15} = GDP_{global} \times (1 + g_{base})^{15} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory GDP at Year 15 (USD) | 1.0000 | Strong driver | > | Current Trajectory GDP at Year 15 (USD) | -0.0000 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15) (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 26.9x | > | Mean (expected value) | 28.9x | > | Median (50th percentile) | 25.5x | > | Standard Deviation | 15.2x | > | 90% Range (5th-95th percentile) | \[11.9x, 57.7x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15) > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20): 56.7x > **NOTE:** > > Wishonia Trajectory GDP at year 20 as a multiple of current trajectory GDP at year 20. > > **Inputs**: > > - [Wishonia Trajectory GDP at Year 20](#sec-wishonia_trajectory_gdp_year_20) 🔢: \$10.7 quadrillion > - [Current Trajectory GDP at Year 20](#sec-current_trajectory_gdp_year_20) 🔢: \$188 trillion > > $$ \begin{gathered} k_{wish:base,20} \\ = \frac{GDP_{wish,20}}{GDP_{base,20}} \\ = \frac{\$10700T}{\$188T} \\ = 56.7 \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory GDP at Year 20 (USD) | 1.0000 | Strong driver | > | Current Trajectory GDP at Year 20 (USD) | 0.0000 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20) (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20)** > > | Statistic | Value | > |:--------------------------------|--------------:| > | Baseline (deterministic) | 56.7x | > | Mean (expected value) | 64.6x | > | Median (50th percentile) | 53.3x | > | Standard Deviation | 43.2x | > | 90% Range (5th-95th percentile) | \[21x, 148x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20) > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory GDP at Year 15: \$4.48 quadrillion > **NOTE:** > > Projected global GDP at year 15 under the Wishonia Trajectory. Applies all Wishonia policy channels including military reallocation, disease-burden recovery, and Political Dysfunction Tax elimination. 3-year ramp at 50% intensity + 12 years full. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Wishonia Military Reallocation Physical Max Share](#sec-wishonia_military_reallocation_physical_max_share) 🔢: 87.6% > - [GDP Growth Boost at 30% Military Reallocation](#sec-military_redirect_gdp_boost_at_30pct): 5.5% (95% CI: 3.5% - 7.5%) > - [R&D Spillover Multiplier](#sec-rd_spillover_multiplier): 2x (95% CI: 1.5x - 2.5x) > - [Wishonia Disease Cure Fraction (15yr, Full Implementation)](#sec-wishonia_disease_cure_fraction_15yr) 🔢: 100% > - [Disease Burden as % of GDP](#sec-disease_burden_gdp_drag_pct) 📊: 13% > - [Global R&D Misallocation Cost](#sec-political_dysfunction_global_science_opportunity_cost) 📊: \$4 trillion (95% CI: \$2 trillion - \$10 trillion) > - [Global Lead Poisoning Cost](#sec-political_dysfunction_global_lead_opportunity_cost) 📊: \$6 trillion (95% CI: \$4 trillion - \$8 trillion) > - [Global Migration Opportunity Cost](#sec-political_dysfunction_global_migration_opportunity_cost) 📊: \$57 trillion (95% CI: \$5 trillion - \$170 trillion) > - [Economic Multiplier for Healthcare Investment](#sec-economic_multiplier_healthcare_investment) 📊: 4.3x (95% CI: 3x - 6x) > - [Economic Multiplier for Military Spending](#sec-economic_multiplier_military_spending) 📊: 0.6x (95% CI: 0.4x - 0.9x) > > $$ GDP_{wish,15}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{12} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory GDP at Year 15** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | GDP Growth Boost at 30% Military Reallocation (rate) | 0.5596 | Strong driver | > | Global Migration Opportunity Cost (USD) | 0.5236 | Strong driver | > | R&D Spillover Multiplier (x) | 0.3889 | Moderate driver | > | Economic Multiplier for Military Spending (x) | -0.2742 | Weak driver | > | Economic Multiplier for Healthcare Investment (x) | 0.2347 | Weak driver | > | Global R&D Misallocation Cost (USD) | 0.0348 | Minimal effect | > | Global Lead Poisoning Cost (USD) | 0.0228 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory GDP at Year 15 (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory GDP at Year 15** > > | Statistic | Value | > |:---|---:| > | Baseline (deterministic) | \$4.48 quadrillion | > | Mean (expected value) | \$4.82 quadrillion | > | Median (50th percentile) | \$4.24 quadrillion | > | Standard Deviation | \$2.53 quadrillion | > | 90% Range (5th-95th percentile) | \[\$1.99 quadrillion, \$9.61 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory GDP at Year 15; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory GDP at Year 15 > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory GDP at Year 15 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory GDP at Year 20: \$10.7 quadrillion > **NOTE:** > > Projected global GDP at year 20 under the Wishonia Trajectory. Model applies all Wishonia policy channels and redirects the full Political Dysfunction Tax non-health opportunity pool to highest-marginal-value uses. Health recovery is modeled separately through disease burden removal to avoid overlap. Military and non-health reallocation effects are ramped at 50% intensity for the first 3 years, then 100% for years 4-20, reflecting implementation lag. Military reallocation uses a physically demonstrated upper bound (post-WW2 demobilization) rather than an arbitrary policy cap. > > **Inputs**: > > - [Global GDP (2025)](#sec-global_gdp_2025) 📊: \$115 trillion > - [Baseline Global GDP Growth Rate](#sec-gdp_baseline_growth_rate): 2.5% > - [Wishonia Military Reallocation Physical Max Share](#sec-wishonia_military_reallocation_physical_max_share) 🔢: 87.6% > - [GDP Growth Boost at 30% Military Reallocation](#sec-military_redirect_gdp_boost_at_30pct): 5.5% (95% CI: 3.5% - 7.5%) > - [R&D Spillover Multiplier](#sec-rd_spillover_multiplier): 2x (95% CI: 1.5x - 2.5x) > - [Wishonia Disease Cure Fraction (20yr, Full Implementation)](#sec-wishonia_disease_cure_fraction_20yr_full) 🔢: 100% > - [Disease Burden as % of GDP](#sec-disease_burden_gdp_drag_pct) 📊: 13% > - [Global R&D Misallocation Cost](#sec-political_dysfunction_global_science_opportunity_cost) 📊: \$4 trillion (95% CI: \$2 trillion - \$10 trillion) > - [Global Lead Poisoning Cost](#sec-political_dysfunction_global_lead_opportunity_cost) 📊: \$6 trillion (95% CI: \$4 trillion - \$8 trillion) > - [Global Migration Opportunity Cost](#sec-political_dysfunction_global_migration_opportunity_cost) 📊: \$57 trillion (95% CI: \$5 trillion - \$170 trillion) > - [Economic Multiplier for Healthcare Investment](#sec-economic_multiplier_healthcare_investment) 📊: 4.3x (95% CI: 3x - 6x) > - [Economic Multiplier for Military Spending](#sec-economic_multiplier_military_spending) 📊: 0.6x (95% CI: 0.4x - 0.9x) > > $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ > > *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory GDP at Year 20** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | GDP Growth Boost at 30% Military Reallocation (rate) | 0.6163 | Strong driver | > | R&D Spillover Multiplier (x) | 0.4336 | Moderate driver | > | Global Migration Opportunity Cost (USD) | 0.4122 | Moderate driver | > | Economic Multiplier for Military Spending (x) | -0.2142 | Weak driver | > | Economic Multiplier for Healthcare Investment (x) | 0.1814 | Weak driver | > | Global R&D Misallocation Cost (USD) | 0.0268 | Minimal effect | > | Global Lead Poisoning Cost (USD) | 0.0208 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory GDP at Year 20 (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory GDP at Year 20** > > | Statistic | Value | > |:--------------------------------|-----------------------------------------:| > | Baseline (deterministic) | \$10.7 quadrillion | > | Mean (expected value) | \$12.2 quadrillion | > | Median (50th percentile) | \$10 quadrillion | > | Standard Deviation | \$8.15 quadrillion | > | 90% Range (5th-95th percentile) | \[\$3.95 quadrillion, \$28 quadrillion\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory GDP at Year 20; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory GDP at Year 20 > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory GDP at Year 20 will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory Lifetime Income Gain (Per Capita): \$36.4 million > **NOTE:** > > Lifetime per-capita income gain from Wishonia Trajectory vs current trajectory. Cumulative Wishonia income minus cumulative current trajectory income over average remaining lifespan. > > **Inputs**: > > - [Wishonia Trajectory Cumulative Lifetime Income (Per Capita)](#sec-wishonia_trajectory_cumulative_lifetime_income) 🔢: \$37.3 million > - [Current Trajectory Cumulative Lifetime Income (Per Capita)](#sec-current_trajectory_cumulative_lifetime_income) 🔢: \$903,908 > > $$ \begin{gathered} \Delta Y_{lifetime,wish} \\ = Y_{cum,wish} - Y_{cum,earth} \\ = \$37.3M - \$904K \\ = \$36.4M \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,wish} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,wish})((1+g_{pc,wish})^{20}-1)}{g_{pc,wish}} \\ + \bar{y}_{wish,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{wish,20} \\ = \frac{GDP_{wish,20}}{Pop_{2045}} \\ = \frac{\$10700T}{9.2B} \\ = \$1.16M \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory Lifetime Income Gain (Per Capita)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory Cumulative Lifetime Income (Per Capita) (USD) | 1.0003 | Strong driver | > | Current Trajectory Cumulative Lifetime Income (Per Capita) (USD) | -0.0021 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory Lifetime Income Gain (Per Capita) (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory Lifetime Income Gain (Per Capita)** > > | Statistic | Value | > |:--------------------------------|---------------------------------:| > | Baseline (deterministic) | \$36.4 million | > | Mean (expected value) | \$42.2 million | > | Median (50th percentile) | \$34.9 million | > | Standard Deviation | \$28.3 million | > | 90% Range (5th-95th percentile) | \[\$13.5 million, \$97 million\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory Lifetime Income Gain (Per Capita); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory Lifetime Income Gain (Per Capita) > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory Lifetime Income Gain (Per Capita) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory Lifetime Income Multiplier: 41.3x > **NOTE:** > > Ratio of cumulative lifetime income under Wishonia Trajectory vs current trajectory. Income-agnostic: applies as a multiplier to any individual’s lifetime earnings. > > **Inputs**: > > - [Wishonia Trajectory Cumulative Lifetime Income (Per Capita)](#sec-wishonia_trajectory_cumulative_lifetime_income) 🔢: \$37.3 million > - [Current Trajectory Cumulative Lifetime Income (Per Capita)](#sec-current_trajectory_cumulative_lifetime_income) 🔢: \$903,908 > > $$ \begin{gathered} k_{lifetime,wish:earth} \\ = \frac{Y_{cum,wish}}{Y_{cum,earth}} \\ = \frac{\$37.3M}{\$904K} \\ = 41.3 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,wish} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,wish})((1+g_{pc,wish})^{20}-1)}{g_{pc,wish}} \\ + \bar{y}_{wish,20} \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}-20}-1)}{g_{pc,base}} \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{wish,20} \\ = \frac{GDP_{wish,20}}{Pop_{2045}} \\ = \frac{\$10700T}{9.2B} \\ = \$1.16M \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{base,20} \\ = \frac{GDP_{base,20}}{Pop_{2045}} \\ = \frac{\$188T}{9.2B} \\ = \$20.5K \end{gathered} $$ where: $$ GDP_{base,20} = GDP_{global} \times (1 + g_{base})^{20} $$ where: $$ \begin{gathered} T_{remaining} \\ = LE_{global} - Age_{median} \\ = 73.4 - 30.5 \\ = 42.9 \end{gathered} $$ where: $$ \begin{gathered} Y_{cum,earth} \\ = \bar{y}_0 \cdot \frac{(1+g_{pc,base})((1+g_{pc,base})^{T_{remaining}}-1)}{g_{pc,base}} \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory Lifetime Income Multiplier** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory Cumulative Lifetime Income (Per Capita) (USD) | 1.0054 | Strong driver | > | Current Trajectory Cumulative Lifetime Income (Per Capita) (USD) | -0.0991 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory Lifetime Income Multiplier (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory Lifetime Income Multiplier** > > | Statistic | Value | > |:--------------------------------|----------------:| > | Baseline (deterministic) | 41.3x | > | Mean (expected value) | 46.9x | > | Median (50th percentile) | 39x | > | Standard Deviation | 30.5x | > | 90% Range (5th-95th percentile) | \[15.9x, 106x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory Lifetime Income Multiplier; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory Lifetime Income Multiplier > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory Lifetime Income Multiplier will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Median After-Tax Consumable Income, Wishonia (Year 20): \$194,130 > **NOTE:** > > Median after-tax consumable income at year 20 under the Wishonia Trajectory. Claims tied to mechanisms: share erosion excluded (the wishocratic one-person-one-vote mechanism exists precisely to stop the capture that drives the wedge), the military share falls by the already-parameterized physical max reallocation, and the health-burden relief channel applies at Wishonia’s own full-implementation cure fraction. Deliberately UNMODELED: the wishocratic equal-per-person allocation of recovered dysfunction waste is pro-median by arithmetic, but that recovery is already inside the Wishonia GDP trajectory, so modeling its distributional bonus separately would double-count. The bonus is real and omitted; this estimate is therefore a floor. > > **Inputs**: > > - [Wishonia Trajectory Average Income at Year 20](#sec-wishonia_trajectory_avg_income_year_20) 🔢: \$1.16 million > - [Military Share of Global GDP](#sec-global_military_share_of_gdp) 🔢: 2.37% > - [Wishonia Military Reallocation Physical Max Share](#sec-wishonia_military_reallocation_physical_max_share) 🔢: 87.6% > - [Global Median-to-Mean Income Ratio](#sec-global_median_to_mean_income_ratio) 🔢: 0.203:1 > - [Median Income Relief from Full Disease Cure](#sec-median_health_burden_relief_share): 10% (95% CI: 5% - 15%) > - [Wishonia Disease Cure Fraction (20yr, Full Implementation)](#sec-wishonia_disease_cure_fraction_20yr_full) 🔢: 100% > - [Effective Tax Rate on Median Earner](#sec-global_effective_tax_rate_median): 25% > > $$ \begin{gathered} \tilde{m}_{wish,20} \\ = \bar{y}_{wish,20} \times (1 - s_{mil} \times (1-s_{mil,max})) \times \rho_{med} \times (1 \\ + r_{relief} \times f_{cure,20,wish}) \times (1 - \tau_{med}) \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{wish,20} \\ = \frac{GDP_{wish,20}}{Pop_{2045}} \\ = \frac{\$10700T}{9.2B} \\ = \$1.16M \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} s_{mil} \\ = \frac{Spending_{mil}}{GDP_{global}} \\ = \frac{\$2.72T}{\$115T} \\ = 2.37\% \end{gathered} $$ where: $$ \begin{gathered} \rho_{med} \\ = \frac{\tilde{y}_{gallup}}{\bar{y}_{0}} \\ = \frac{\$2.92K}{\$14.4K} \\ = 0.203 \end{gathered} $$ where: $$ \begin{gathered} \bar{y}_{0} \\ = \frac{GDP_{global}}{Pop_{global}} \\ = \frac{\$115T}{8B} \\ = \$14.4K \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Median After-Tax Consumable Income, Wishonia (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory Average Income at Year 20 (USD) | 0.9798 | Strong driver | > | Global Median-to-Mean Income Ratio (ratio) | 0.1708 | Weak driver | > | Median Income Relief from Full Disease Cure (rate) | 0.0340 | Minimal effect | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Median After-Tax Consumable Income, Wishonia (Year 20) (10,000 simulations) > > **Simulation Results Summary: Median After-Tax Consumable Income, Wishonia (Year 20)** > > | Statistic | Value | > |:--------------------------------|------------------------:| > | Baseline (deterministic) | \$194,130 | > | Mean (expected value) | \$221,117 | > | Median (50th percentile) | \$181,135 | > | Standard Deviation | \$150,267 | > | 90% Range (5th-95th percentile) | \[\$70,137, \$507,535\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Median After-Tax Consumable Income, Wishonia (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Median After-Tax Consumable Income, Wishonia (Year 20) > > *This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Wishonia (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ### Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20): 33.2x > **NOTE:** > > Year-20 GDP multiplier from adding non-health dysfunction-capital reallocation on top of the Treaty Trajectory channels. > > **Inputs**: > > - [Wishonia Trajectory GDP at Year 20](#sec-wishonia_trajectory_gdp_year_20) 🔢: \$10.7 quadrillion > - [Treaty Trajectory GDP at Year 20](#sec-treaty_trajectory_gdp_year_20) 🔢: \$322 trillion > > $$ \begin{gathered} k_{wish,full:core,20} \\ = \frac{GDP_{wish,20}}{GDP_{treaty,20}} \\ = \frac{\$10700T}{\$322T} \\ = 33.2 \end{gathered} $$ where: $$ GDP_{wish,20}=GDP_0(1+g_{ramp})^{3}(1+g_{full})^{17} $$ where: $$ s_{mil,max} = Cut_{WW2} = 87.6\% = 87.6\% $$ where: $$ \begin{gathered} Cut_{WW2} \\ = 1 - \frac{Spending_{US,1947}}{Spending_{US,1945}} \\ = 1 - \frac{\$176B}{\$1.42T} \\ = 87.6\% \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,wish} \\ = \min\left(1.0, Treatments_{new,ann} \times min(\text{TRIAL\_CAPACITY\_MULTIPLIER} \times \left(\frac{s_{mil,max}}{0.01}\right), \text{MAX\_TRIAL\_CAPACITY\_MULTIPLIER\_PHYSICAL}) \times \frac{20}{N_{untreated}}\right) \end{gathered} $$ where: $$ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} $$ where: $$ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} $$ where: $$ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} $$ where: $$ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} $$ where: $$ \begin{gathered} k_{capacity,max} \\ = \frac{N_{willing}}{Slots_{curr}} \\ = \frac{1.08B}{1.9M} \\ = 566 \end{gathered} $$ where: $$ \begin{gathered} N_{willing} \\ = N_{patients} \times Pct_{willing} \\ = 2.4B \times 44.8\% \\ = 1.08B \end{gathered} $$ where: $$ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} $$ where: $$ \begin{gathered} GDP_{treaty,20} \\ = GDP_{global} \times (1 + g_{base} + g_{redirect,treaty,20} \\ + g_{peace,treaty,20} + g_{cyber,treaty,20} \\ + g_{health,treaty,20})^{20} \end{gathered} $$ where: $$ \begin{gathered} g_{redirect,treaty,20} \\ = \bar{s}_{treaty,20} \times \Delta g_{30\%} \times m_{spillover} \times 1.67 \\ = 5.8\% \times 5.5\% \times 2 \times 1.67 \\ = 1.06\% \end{gathered} $$ where: $$ \begin{gathered} \bar{s}_{treaty,20} \\ = s_{ratchet} \times 0.409 \\ = 10\% \times 0.409 \\ = 5.8\% \end{gathered} $$ where: $$ \begin{gathered} g_{peace,treaty,20} \\ = \left(\frac{Benefit_{peace,soc}}{GDP_{global}}\right) \times \left(\frac{\bar{s}_{treaty,20}}{Reduce_{treaty}}\right) \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \end{gathered} $$ where: $$ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \end{gathered} $$ where: $$ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} $$ where: $$ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} $$ where: $$ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} $$ where: $$ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} $$ where: $$ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} $$ where: $$ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} $$ where: $$ \begin{gathered} g_{cyber,treaty,20} \\ = \left(\frac{Cost_{cyber}}{GDP_{global}}\right) \times \bar{s}_{treaty,20} \times \varepsilon_{conflict} \end{gathered} $$ where: $$ \begin{gathered} g_{health,treaty,20} \\ = ((1 \\ + f_{cure,20,treaty} \times d_{disease})^{\frac{1}{H_{health,treaty}}}) - 1 \end{gathered} $$ where: $$ \begin{gathered} f_{cure,20,treaty} \\ = \min\left(1.0, (3 \times min(k_{capacity} \times \left(\frac{0.01}{0.01}\right), k_{capacity,max}) + 4 \times min(k_{capacity} \times (min(0.02, s_{ratchet})/0.01), k_{capacity,max}) + 5 \times min(k_{capacity} \times (min(0.05, s_{ratchet})/0.01), k_{capacity,max}) + 8 \times min(k_{capacity} \times \left(\frac{s_{ratchet}}{0.01}\right), k_{capacity,max})) / T_{queue,SQ}\right) \end{gathered} $$ where: $$ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} $$ *✓ High confidence* > > #### Sensitivity Analysis > > **Sensitivity Indices for Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20)** > > Regression-based sensitivity showing which inputs explain the most variance in the output. > > | Input Parameter | Sensitivity Coefficient | Interpretation | > |:---|---:|:---| > | Wishonia Trajectory GDP at Year 20 (USD) | 1.0168 | Strong driver | > | Treaty Trajectory GDP at Year 20 (USD) | -0.2568 | Weak driver | > > *Interpretation*: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs. > > #### Monte Carlo Distribution > > Monte Carlo Distribution: Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20) (10,000 simulations) > > **Simulation Results Summary: Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20)** > > | Statistic | Value | > |:--------------------------------|-----------------:| > | Baseline (deterministic) | 33.2x | > | Mean (expected value) | 37.7x | > | Median (50th percentile) | 31.4x | > | Standard Deviation | 24.7x | > | 90% Range (5th-95th percentile) | \[12.7x, 83.4x\] | > > *The histogram shows 1,000 of the 10,000 Monte Carlo draws for Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.* > > #### Exceedance Probability > > Probability of Exceeding Threshold: Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20) > > *This exceedance probability chart shows the likelihood that Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20) will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.* ## External Data Sources Parameters sourced from peer-reviewed publications, institutional databases, and authoritative reports. ### ADAPTABLE Trial Cost per Patient: \$929 > **NOTE:** > > Cost per patient in ADAPTABLE trial (\$14M PCORI grant / 15,076 patients). Note: This is the direct grant cost; true cost including in-kind may be 10-40% higher. > > **Source**:^([14](../references.llms.md#ref-pragmatic-trials-cost-advantage)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$929, \$1,400\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$929 and \$1,400 (±25%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: ADAPTABLE Trial Cost per Patient > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### ADAPTABLE Trial Total Cost: \$14 million > **NOTE:** > > PCORI grant for ADAPTABLE trial (2016-2019). Note: Direct funding only; total costs including site overhead and in-kind contributions from health systems may be higher. > > **Source**:^([14](../references.llms.md#ref-pragmatic-trials-cost-advantage)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$14 million, \$20 million\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$14 million and \$20 million (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: ADAPTABLE Trial Total Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Agriculture Share of Freshwater Withdrawals: 70% > **NOTE:** > > Agriculture’s share of global freshwater withdrawals, ~70% (Our World in Data, FAO AQUASTAT / World Bank). This is the standard consensus figure but is a soft estimate; recent work argues the true irrigation share could plausibly range ~45-90%. Reported as the widely-cited ~70% with that uncertainty acknowledged. > > **Source**:^([15](../references.llms.md#ref-owid-water-use-stress)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *~ Medium confidence* ### Deaths Directly Attributable to AMR (2019): 1.27 million deaths > **NOTE:** > > Deaths directly attributable to bacterial antimicrobial resistance in 2019, ~1.27 million (95% UI 0.91-1.71 million); ~4.95 million deaths were associated with it (Murray et al., The Lancet 2022). Attributable and associated are not additive. > > **Source**:^([16](../references.llms.md#ref-murray-2022-amr)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[911 thousand deaths, 1.71 million deaths\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 911 thousand deaths and 1.71 million deaths (±31%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Deaths Directly Attributable to AMR (2019) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Terrorism Death Risk (1 in X): 30 million people > **NOTE:** > > Annual probability of being killed by terrorism expressed as ‘1 in X’. An American’s annual odds of dying in a terrorist attack are approximately 1 in 30 million. > > **Source**:^([17](../references.llms.md#ref-chance-of-dying-from-terrorism-1-in-30m)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Antidepressant Trial Exclusion Rate: 86.1% > **NOTE:** > > Mean exclusion rate in antidepressant trials (86.1% of real-world patients excluded) > > **Source**:^([18](../references.llms.md#ref-antidepressant-trial-exclusion-rates)) > > *✓ High confidence* ### Wild Aquatic Animals Killed for Food per Year (low estimate): 1.1 trillion animals > **NOTE:** > > Wild-caught fish killed globally per year, estimated at 1.1-2.2 trillion (average 2000-2019, fishcount.org.uk, derived from FAO capture tonnage and mean body weights). Value shown is the conservative low end; the range is wide because these animals are counted by weight, not headcount. > > **Source**:^([19](../references.llms.md#ref-fishcount-wild-fish)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *~ Medium confidence* ### Average Annual Stock Market Return: 10% > **NOTE:** > > Average annual stock market return (10%) > > **Source**:^([20](../references.llms.md#ref-warren-buffett-career-average-return-20-pct)) > > *✓ High confidence* ### Beef Caloric Conversion Efficiency: 3% > **NOTE:** > > Caloric conversion efficiency of beef, ~3%: only about 3 of every 100 feed calories become beef calories (Cassidy et al. 2013, Environmental Research Letters). The often-quoted ‘~33 feed calories per beef calorie’ is the arithmetic reciprocal of this figure, not a separate measurement. System-wide, 36% of crop calories go to animal feed and only 12% return as human food. > > **Source**:^([21](../references.llms.md#ref-cassidy-2013-nourished)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Bullets Fired per Kill (Iraq/Afghanistan): 250 thousand rounds > **NOTE:** > > Rounds of small-arms ammunition fired per insurgent killed in Iraq and Afghanistan. Based on GAO figures: ~6 billion rounds expended 2002-2005. Calculated by military researcher John Pike of GlobalSecurity.org. > > **Source**:^([22](../references.llms.md#ref-nato-556-rounds-per-kill)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *~ Medium confidence* ### Cost per 5.56mm NATO Round (Bulk): \$0.4 > **NOTE:** > > Cost per round of 5.56x45mm NATO ammunition (military bulk procurement). Based on U.S. military procurement contracts for M855 ball ammunition. Civilian retail floor is ~\$0.37; \$0.40 is a conservative midpoint. > > **Source**:^([23](../references.llms.md#ref-nato-556-ammo-cost)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$0.25, \$0.6\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$0.25 and \$0.6 (±44%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Cost per 5.56mm NATO Round (Bulk) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### CBO Immigration Surge Deficit Reduction, 2024-2034: \$900 billion > **NOTE:** > > CBO (July 2024): the 2021-2026 immigration surge lowers federal deficits, on net, by \$0.9 trillion over 2024-2034 (revenues up \$1.2 trillion, spending up \$0.3 trillion). > > **Source**:^([24](../references.llms.md#ref-cbo-immigration-surge-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence • Updated 2024-07* ### Global Billionaire Count: 2,781 people > **NOTE:** > > Number of billionaires globally (Forbes 2024 count) > > **Source**:^([25](../references.llms.md#ref-forbes-billionaires-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Estimated Annual Global Economic Benefit from Childhood Vaccination Programs: \$15 billion > **NOTE:** > > Estimated annual global economic benefit from childhood vaccination programs (measles, polio, etc.) > > **Source**:^([26](../references.llms.md#ref-childhood-vaccination-economic-benefits)) > > #### Uncertainty Range > > **Technical**: Distribution: Lognormal (SE: \$4.5 billion) > > #### Input Distribution > > Probability Distribution: Estimated Annual Global Economic Benefit from Childhood Vaccination Programs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Return on Investment from Childhood Vaccination Programs: 13:1 > **NOTE:** > > Return on investment from childhood vaccination programs > > **Source**:^([27](../references.llms.md#ref-childhood-vaccination-roi)) > > *✓ High confidence* ### Disability Weight for Untreated Chronic Conditions: 0.35 weight > **NOTE:** > > Disability weight for untreated chronic conditions (WHO Global Burden of Disease) > > **Source**:^([28](../references.llms.md#ref-who-global-health-estimates-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 0.07 weight) > > #### Input Distribution > > Probability Distribution: Disability Weight for Untreated Chronic Conditions > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence • 📊 Peer-reviewed* ### Conventional Retirement Return (After Fees): 6.5% > **NOTE:** > > Average retail after-fee return on conventional retirement portfolios (60/40 stock/bond mix, ~1% advisory fees, ~0.4% fund fees). Used as the opportunity cost comparison: depositors are LOSING money by NOT participating in the Prize Fund. > > #### Uncertainty Range > > **Technical**: 95% CI: \[5%, 8%\] • Distribution: Normal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 5% and 8% (±23%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Conventional Retirement Return (After Fees) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Corporate Analog False Claims Act Treble Multiplier: 3x > **NOTE:** > > Treble-damages multiplier from the False Claims Act, used here as the corporate-defendant analogy for audit and public-money claims in Humanity v. Government. > > **Source**:^([29](../references.llms.md#ref-doj-false-claims-act)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### State Farm Constitutional-Ceiling Exposure Multiplier: 10x > **NOTE:** > > Total exposure multiplier for a 9:1 punitive-to-compensatory ratio, meaning base damages plus nine times base damages. Used as constitutional-ceiling exposure under State Farm v. Campbell, not a typical award. > > **Source**:^([30](../references.llms.md#ref-state-farm-v-campbell-2003)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### CPI Multiplier: 1980 to 2024: 3.8:1 > **NOTE:** > > CPI inflation multiplier from 1980 to 2024 (280.48% cumulative inflation) > > **Source**:^([31](../references.llms.md#ref-bls-cpi-inflation-calculator)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[3.75:1, 3.85:1\] • Distribution: Normal > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 3.75:1 and 3.85:1 (±1%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: CPI Multiplier: 1980 to 2024 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Crowd Decision Accuracy (Millionaire): 91% > **NOTE:** > > Crowd accuracy on Who Wants to Be a Millionaire ask-the-audience lifeline. Studio audience picked the correct answer 91% of the time (Surowiecki 2004). Used as lower bound for wishocratic allocation accuracy. > > **Source**:^([32](../references.llms.md#ref-surowiecki-2004)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Current Active Trials at Any Given Time: 10,000 trials > **NOTE:** > > Current active trials at any given time (3-5 year duration) > > **Source**:^([33](../references.llms.md#ref-clinicaltrials-gov-enrollment-data-2025)) > > *✓ High confidence* ### Current Clinical Trial Participation Rate: 0.06% > **NOTE:** > > Current clinical trial participation rate (0.06% of population) > > **Source**:^([3](../references.llms.md#ref-clinical-trial-patient-participation-rate)) > > *✓ High confidence* ### Global Population with Chronic Diseases: 2.4 billion people > **NOTE:** > > Global population with chronic diseases > > **Source**:^([34](../references.llms.md#ref-disease-prevalence-2-billion)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[2 billion people, 2.8 billion people\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 2 billion people and 2.8 billion people (±17%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Population with Chronic Diseases > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Average Annual New Drug Approvals Globally: 50 drugs/year > **NOTE:** > > Average annual new drug approvals globally > > **Source**:^([5](../references.llms.md#ref-global-new-drug-approvals-50-annually)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[45 drugs/year, 60 drugs/year\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 45 drugs/year and 60 drugs/year (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Average Annual New Drug Approvals Globally > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Current Global Clinical Trials per Year: 3,300 trials/year > **NOTE:** > > Current global clinical trials per year > > **Source**:^([35](../references.llms.md#ref-global-clinical-trials-market-2024)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[2,640 trials/year, 3,960 trials/year\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 2,640 trials/year and 3,960 trials/year (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Current Global Clinical Trials per Year > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Current Trial Abandonment Rate: 40% > **NOTE:** > > Current trial abandonment rate (40% never complete) > > **Source**:^([36](../references.llms.md#ref-clinical-trial-abandonment-rate)) > > *✓ High confidence* ### Annual Global Clinical Trial Participants: 1.9 million patients/year > **NOTE:** > > Annual global clinical trial participants (IQVIA 2022: 1.9M post-COVID normalization) > > **Source**:^([37](../references.llms.md#ref-global-trial-participant-capacity)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[1.5 million patients/year, 2.3 million patients/year\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 1.5 million patients/year and 2.3 million patients/year (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Global Clinical Trial Participants > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Military Sector Lobbying: \$198 million > **NOTE:** > > Annual military sector lobbying spending. OpenSecrets reports the 2025 actual at \$198.0 million, the top of a three-year climb: \$142.9M (2023), \$159.5M (2024), \$198.0M (2025) > > **Source**:^([38](../references.llms.md#ref-lobbying-spend-defense)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$190 million, \$210 million\] > > **What this means**: We’re quite confident in this estimate. The true value likely falls between \$190 million and \$210 million (±5%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Annual Military Sector Lobbying > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed • Updated 2025* ### Aerospace/Defense Net Profit Margin: 4.99% > **NOTE:** > > Net profit margin for Aerospace/Defense industry (79 firms, Damodaran Jan 2026) > > #### Uncertainty Range > > **Technical**: 95% CI: \[4%, 6%\] > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 4% and 6% (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Aerospace/Defense Net Profit Margin > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Allied Military Primes Market Cap: \$132 billion > **NOTE:** > > Combined market capitalization of major allied European military primes (BAE Systems approx \$75.8B + Thales approx \$56.7B), as of June 2026 > > **Source**:^([39](../references.llms.md#ref-companiesmarketcap-eu-primes-2026)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### US Military Primes Market Cap: \$836 billion > **NOTE:** > > Combined market capitalization of the 11 major US military primes at the June 2026 close: RTX \$248.1B, Boeing \$174.7B, Lockheed Martin \$126.5B, General Dynamics \$96.9B, Northrop Grumman \$78.5B, L3Harris \$58.2B, Leidos \$15.4B, Huntington Ingalls \$11.9B, CACI \$11.6B, Booz Allen Hamilton \$9.2B, SAIC \$4.9B > > **Source**:^([40](../references.llms.md#ref-stockanalysis-military-prime-caps-2026)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### 20th-Century Government Democide Total: 262 million deaths > **NOTE:** > > Total people murdered by governments worldwide, 1900-1999 (Rummel’s democide estimate) > > **Source**:^([41](../references.llms.md#ref-rummel-death-by-government)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[200 million deaths, 272 million deaths\] • Distribution: Uniform > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 200 million deaths and 272 million deaths (±14%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: 20th-Century Government Democide Total > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Deworming Cost per DALY: \$55 > **NOTE:** > > Cost per DALY for deworming programs (range \$28-82, midpoint estimate). GiveWell notes this 2011 estimate is outdated and their current methodology focuses on long-term income effects rather than short-term health DALYs. > > **Source**:^([42](../references.llms.md#ref-deworming-cost-per-daly)) > > *? Low confidence* ### Pragmatic Trial Cost per Patient: \$929 > **NOTE:** > > Embedded pragmatic trial cost per patient. Uses ADAPTABLE trial (\$929) as DELIBERATELY CONSERVATIVE central estimate. Ramsberg & Platt (2018) reviewed 108 embedded pragmatic trials; 64 with cost data had median of only \$97/patient - this estimate may overstate costs by 10x. Confidence interval spans meta-analysis median to complex chronic disease trials. > > **Source**:^([14](../references.llms.md#ref-pragmatic-trials-cost-advantage)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$97, \$3,000\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$97 and \$3,000 (±156%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Cost per Patient > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Deaths per Year from Dietary Risks: 11 million deaths > **NOTE:** > > Deaths per year attributable to dietary risk factors, ~11 million (95% UI 10-12 million; 255 million DALYs), from the Global Burden of Disease Study 2017 (Afshin et al., The Lancet 2019). Diet-driven chronic disease is one of the human-health costs the current food system externalizes. > > **Source**:^([43](../references.llms.md#ref-gbd-2017-diet)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[10 million deaths, 12 million deaths\] • Distribution: Normal > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 10 million deaths and 12 million deaths (±9%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Deaths per Year from Dietary Risks > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Disease Burden as % of GDP: 13% > **NOTE:** > > Fraction of GDP currently lost to disease (productivity losses + medical costs diverted from productive use). \$5T productivity loss + \$9.9T direct medical costs = \$14.9T on \$115T GDP = ~13%. As diseases are progressively cured, this drag is recovered as GDP growth. This is the missing factor that makes the treaty trajectory look like a singularity rather than a modest improvement. > > **Source**:^([44](../references.llms.md#ref-disease-economic-burden-109t)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### DOT VSL: \$13.7 million > **NOTE:** > > DOT Value of Statistical Life (2024). Used by federal agencies to evaluate safety regulations and quantify the economic value of mortality risk reductions. > > **Source**:^([45](../references.llms.md#ref-dot-vsl-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Drug Development Cost (1980s): \$194 million > **NOTE:** > > Drug development cost in 1980s (compounded to approval, 1990 dollars) > > **Source**:^([6](../references.llms.md#ref-pre-1962-drug-costs-timeline)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$146 million, \$242 million\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$146 million and \$242 million (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Drug Development Cost (1980s) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Drug Discovery to Approval Timeline: 14 years > **NOTE:** > > Full drug development timeline from discovery to FDA approval. Typical range is 12-15 years based on BIO 2021 and PMC meta-analyses. Breakdown: preclinical 4-6 years + clinical 10.5 years. Using 14 years as central estimate. > > **Source**:^([46](../references.llms.md#ref-bio-clinical-development-2021)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[12 years, 17 years\] > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 12 years and 17 years (±18%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Drug Discovery to Approval Timeline > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Drug Repurposing Success Rate: 30% > **NOTE:** > > Percentage of drugs that gain at least one new indication after initial approval > > **Source**:^([47](../references.llms.md#ref-drug-repurposing-rate)) > > *✓ High confidence* ### Economic Multiplier for Education Investment: 2.1x > **NOTE:** > > Economic multiplier for education investment (2.1x ROI) > > **Source**:^([48](../references.llms.md#ref-education-investment-economic-multiplier)) > > *✓ High confidence* ### Economic Multiplier for Healthcare Investment: 4.3x > **NOTE:** > > Economic multiplier for healthcare investment (4.3x ROI). Literature range 3.0-6.0×. > > **Source**:^([49](../references.llms.md#ref-healthcare-investment-economic-multiplier)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[3x, 6x\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 3x and 6x (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Economic Multiplier for Healthcare Investment > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Economic Multiplier for Infrastructure Investment: 1.6x > **NOTE:** > > Economic multiplier for infrastructure investment (1.6x ROI) > > **Source**:^([50](../references.llms.md#ref-infrastructure-investment-economic-multiplier)) > > *✓ High confidence* ### Economic Multiplier for Military Spending: 0.6x > **NOTE:** > > Economic multiplier for military spending (0.6x ROI). Literature range 0.4-1.0×. > > **Source**:^([51](../references.llms.md#ref-military-spending-economic-multiplier)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.4x, 0.9x\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 0.4x and 0.9x (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Economic Multiplier for Military Spending > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Regulatory Delay for Efficacy Testing Post-Safety Verification: 8.2 years > **NOTE:** > > Regulatory delay for efficacy testing (Phase II/III) post-safety verification. Based on BIO 2021 industry survey. Note: This is for drugs that COMPLETE the pipeline - survivor bias means actual delay for any given disease may be longer if candidates fail and must restart. > > **Source**:^([46](../references.llms.md#ref-bio-clinical-development-2021)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 2 years) > > #### Input Distribution > > Probability Distribution: Regulatory Delay for Efficacy Testing Post-Safety Verification > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence • 📊 Peer-reviewed • Updated 2021* ### EOS Social Value Capture Rate: 2.2% > **NOTE:** > > Fraction of political dysfunction tax value that EOS captures as shareholder returns via portfolio appreciation. Base case from Nordhaus (2004): innovators capture 2.2% of social surplus. Could be lower because governance gains are partly public goods. Could be higher if the thesis is tradable before it is obvious, EOS owns constrained assets first, and later investors reprice those assets after EOS proves the governance case. Skeptical case: 0.5%. Bull case: 5%. > > **Source**:^([52](../references.llms.md#ref-nordhaus2004)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.5%, 5%\] • Distribution: Lognormal (SE: 1%) > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.5% and 5% (±102%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: EOS Social Value Capture Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Expert Decision Accuracy (Millionaire): 65% > **NOTE:** > > Expert accuracy on Who Wants to Be a Millionaire phone-a-friend lifeline. Credentialed expert picked the correct answer 65% of the time (Surowiecki 2004). Used as baseline for conventional fund manager / committee allocation. > > **Source**:^([32](../references.llms.md#ref-surowiecki-2004)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Share of US Farmed Animals on Factory Farms: 99% > **NOTE:** > > Share of US farmed animals living on factory farms, ~99% (Sentience Institute, using EPA CAFO definitions and USDA farm-size census; widely cited incl. by Our World in Data). Per-species: ~75% of cows, ~99% of pigs, \>99.9% of meat chickens. Global share is lower for land vertebrates (~74%) and higher once fish are included (\>90%). > > **Source**:^([53](../references.llms.md#ref-sentience-us-factory-farming)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Land Animals Slaughtered for Food per Year: 83 billion animals > **NOTE:** > > Land animals slaughtered for meat globally per year, ~83 billion in 2022 (UN FAO via Our World in Data). Chickens are the immense majority. Excludes culled male chicks and animals without data, so it undercounts total farmed deaths. Dwarfed in turn by aquatic-animal deaths (see AQUATIC_ANIMALS_KILLED_ANNUAL). > > **Source**:^([54](../references.llms.md#ref-owid-land-animals-slaughtered)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Farmland Freed Under a Shift Off Animal Products: 75% > **NOTE:** > > Reduction in global farmland if the world shifted off meat and dairy while still feeding everyone, more than 75% (~4.1 billion down to ~1 billion hectares; University of Oxford LEAP, reporting Poore & Nemecek 2018, Science). Oxford’s illustrative equivalent: an area the size of the US, China, EU, and Australia combined. A hypothetical full-shift model, not a forecast. > > **Source**:^([55](../references.llms.md#ref-oxford-leap-food-impacts)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### FDA-Approved Drug Products: 20,000 products > **NOTE:** > > Total FDA-approved drug products in the U.S. > > **Source**:^([56](../references.llms.md#ref-fda-approved-products-20k)) > > *✓ High confidence* ### FDA-Approved Unique Active Ingredients: 1,650 compounds > **NOTE:** > > Unique active pharmaceutical ingredients in FDA-approved products (midpoint of 1,300-2,000 range) > > **Source**:^([56](../references.llms.md#ref-fda-approved-products-20k)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[1,300 compounds, 2,000 compounds\] • Distribution: Uniform > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 1,300 compounds and 2,000 compounds (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: FDA-Approved Unique Active Ingredients > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### FDA GRAS Substances: 635 substances > **NOTE:** > > FDA Generally Recognized as Safe (GRAS) substances (midpoint of 570-700 range) > > **Source**:^([57](../references.llms.md#ref-fda-gras-list-count)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[570 substances, 700 substances\] • Distribution: Uniform > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 570 substances and 700 substances (±10%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: FDA GRAS Substances > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### FDA Phase 1 to Approval Timeline: 10.5 years > **NOTE:** > > FDA timeline from Phase 1 start to approval. Derived from BIO 2021 industry survey: Phase 1 (2.3 years) + efficacy lag (8.2 years) = 10.5 years. Consistent with PMC meta-analysis finding 9.1 years median (95% CI: 8.2-10.0). > > **Source**:^([46](../references.llms.md#ref-bio-clinical-development-2021)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[6 years, 12 years\] • Distribution: Gamma (SE: 2 years) > > **What this means**: There’s significant uncertainty here. The true value likely falls between 6 years and 12 years (±29%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The gamma distribution means values follow a specific statistical pattern.* > > #### Input Distribution > > Probability Distribution: FDA Phase 1 to Approval Timeline > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Food Production Share of Global GHG Emissions: 26% > **NOTE:** > > Food production’s share of global greenhouse-gas emissions, ~26% (~13.7 Gt CO2eq; Our World in Data, based on Poore & Nemecek 2018). Uses the ‘food production’ boundary. A wider whole-food-system boundary including retail, transport, packaging, and waste (Crippa et al. 2021, Nature Food) reaches ~34%. Present as boundary choices, not a contradiction. > > **Source**:^([58](../references.llms.md#ref-owid-environmental-impacts-food)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Food System Hidden Costs per Year: \$12 trillion > **NOTE:** > > Hidden (external) costs of the global food and land-use system, ~\$12 trillion/year, against a market value of ~\$10 trillion (FOLU ‘Growing Better’ 2019; modeled by an advocacy coalition, widely cited). Comprises environmental, public-health, and poverty costs; projected to rise to \$16.1 trillion by 2050 under current trends. > > **Source**:^([59](../references.llms.md#ref-folu-growing-better-2019)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *~ Medium confidence* ### Gallup Global Median Per-Capita Income (2013, PPP): \$2,920 > **NOTE:** > > Global median per-capita household income as measured by the Gallup World Poll (131 countries, PPP dollars, published 2013): the only comprehensive survey-based measurement of the middle human’s income that exists. The median-to-mean ratio is DERIVED from this anchor rather than chosen, so the model is calibrated to a measured human. The confidence interval covers the anchor’s vintage (global medians grew after 2013, pushing the true current value above the point estimate) and PPP-vs-market-rate conversion (pushing it below). > > **Source**:^([60](../references.llms.md#ref-gallup-global-median-income-2013)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$2,300, \$3,700\] • Distribution: Normal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$2,300 and \$3,700 (±24%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Gallup Global Median Per-Capita Income (2013, PPP) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### GiveWell Midpoint of Modeled Cost per Life Saved Range: \$4,500 > **NOTE:** > > Midpoint of GiveWell’s cited \$3,500 to \$5,500 modeled cost-per-life-saved range across top charities > > **Source**:^([1](../references.llms.md#ref-givewell-cost-per-life-saved)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### GiveWell Cost per Life Saved (AMF, Highest Top-Charity Estimate): \$5,500 > **NOTE:** > > GiveWell cost per life saved (Against Malaria Foundation). GiveWell estimates ~\$3,000 to ~\$8,000 per death averted across the locations where it funds AMF campaigns (as of December 2023). > > **Source**:^([1](../references.llms.md#ref-givewell-cost-per-life-saved)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$3,000, \$8,000\] • Distribution: Normal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$3,000 and \$8,000 (±45%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: GiveWell Cost per Life Saved (AMF, Highest Top-Charity Estimate) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Givewell Cost per Life Saved (Minimum): \$3,500 > **NOTE:** > > GiveWell cost per life saved (Helen Keller International) > > **Source**:^([1](../references.llms.md#ref-givewell-cost-per-life-saved)) > > *✓ High confidence* ### Annual Deaths from Active Combat Worldwide: 234 thousand deaths/year > **NOTE:** > > Annual deaths from active combat worldwide > > **Source**:^([61](../references.llms.md#ref-acled-active-combat-deaths)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[180 thousand deaths/year, 300 thousand deaths/year\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 180 thousand deaths/year and 300 thousand deaths/year (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Deaths from Active Combat Worldwide > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Deaths from State Violence: 2,700 deaths/year > **NOTE:** > > Annual deaths from state violence > > **Source**:^([62](../references.llms.md#ref-ucdp-state-violence-deaths)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[1,500 deaths/year, 5,000 deaths/year\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 1,500 deaths/year and 5,000 deaths/year (±65%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Deaths from State Violence > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Deaths from Terror Attacks Globally: 8,300 deaths/year > **NOTE:** > > Annual deaths from terror attacks globally > > **Source**:^([63](../references.llms.md#ref-gtd-terror-attack-deaths)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[6,000 deaths/year, 12,000 deaths/year\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 6,000 deaths/year and 12,000 deaths/year (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Deaths from Terror Attacks Globally > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Annual DALY Burden: 2.88 billion DALYs/year > **NOTE:** > > Global annual DALY burden from all diseases and injuries (WHO/IHME Global Burden of Disease 2021). Includes both YLL (years of life lost) and YLD (years lived with disability) from all causes. > > **Source**:^([64](../references.llms.md#ref-ihme-gbd-2021)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 150 million DALYs/year) > > #### Input Distribution > > Probability Distribution: Global Annual DALY Burden > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### Annual Deaths from All Diseases and Aging Globally: 55 million deaths/year > **NOTE:** > > Annual deaths from all diseases and aging globally > > **Source**:^([28](../references.llms.md#ref-who-global-health-estimates-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 5 million deaths/year) > > #### Input Distribution > > Probability Distribution: Annual Deaths from All Diseases and Aging Globally > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Environmental Damage and Restoration Costs from Conflict: \$100 billion > **NOTE:** > > Annual environmental damage and restoration costs from conflict > > **Source**:^([65](../references.llms.md#ref-environmental-cost-of-war)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$70 billion, \$140 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$70 billion and \$140 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Environmental Damage and Restoration Costs from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Infrastructure Damage to Communications from Conflict: \$298 billion > **NOTE:** > > Annual infrastructure damage to communications from conflict > > **Source**:^([65](../references.llms.md#ref-environmental-cost-of-war)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$209 billion, \$418 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$209 billion and \$418 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Infrastructure Damage to Communications from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Infrastructure Damage to Education Facilities from Conflict: \$234 billion > **NOTE:** > > Annual infrastructure damage to education facilities from conflict > > **Source**:^([65](../references.llms.md#ref-environmental-cost-of-war)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$164 billion, \$328 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$164 billion and \$328 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Infrastructure Damage to Education Facilities from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Infrastructure Damage to Energy Systems from Conflict: \$422 billion > **NOTE:** > > Annual infrastructure damage to energy systems from conflict > > **Source**:^([65](../references.llms.md#ref-environmental-cost-of-war)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$295 billion, \$590 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$295 billion and \$590 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Infrastructure Damage to Energy Systems from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Infrastructure Damage to Healthcare Facilities from Conflict: \$166 billion > **NOTE:** > > Annual infrastructure damage to healthcare facilities from conflict > > **Source**:^([65](../references.llms.md#ref-environmental-cost-of-war)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$116 billion, \$232 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$116 billion and \$232 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Infrastructure Damage to Healthcare Facilities from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Infrastructure Damage to Transportation from Conflict: \$487 billion > **NOTE:** > > Annual infrastructure damage to transportation from conflict > > **Source**:^([65](../references.llms.md#ref-environmental-cost-of-war)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$340 billion, \$680 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$340 billion and \$680 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Infrastructure Damage to Transportation from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Infrastructure Damage to Water Systems from Conflict: \$268 billion > **NOTE:** > > Annual infrastructure damage to water systems from conflict > > **Source**:^([65](../references.llms.md#ref-environmental-cost-of-war)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$187 billion, \$375 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$187 billion and \$375 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Infrastructure Damage to Water Systems from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Lost Economic Growth from Military Spending Opportunity Cost: \$2.72 trillion > **NOTE:** > > Annual foregone economic output from military spending vs productive alternatives. This estimate implicitly captures fiscal multiplier differences (military ~0.6x vs healthcare ~4.3x GDP multiplier). Do not add separate GDP multiplier adjustment to avoid double-counting. > > **Source**:^([66](../references.llms.md#ref-disparity-ratio-weapons-vs-cures)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1.9 trillion, \$3.8 trillion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$1.9 trillion and \$3.8 trillion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Lost Economic Growth from Military Spending Opportunity Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Lost Productivity from Conflict Casualties: \$300 billion > **NOTE:** > > Annual lost productivity from conflict casualties > > **Source**:^([67](../references.llms.md#ref-lost-human-capital-war-cost)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$210 billion, \$420 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$210 billion and \$420 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Lost Productivity from Conflict Casualties > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual PTSD and Mental Health Costs from Conflict: \$232 billion > **NOTE:** > > Annual PTSD and mental health costs from conflict > > **Source**:^([68](../references.llms.md#ref-psychological-impact-war-cost)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$162 billion, \$325 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$162 billion and \$325 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual PTSD and Mental Health Costs from Conflict > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Refugee Support Costs: \$150 billion > **NOTE:** > > Annual refugee support costs (108.4M refugees × \$1,384/year) > > **Source**:^([69](../references.llms.md#ref-unhcr-refugee-support-cost)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$105 billion, \$210 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$105 billion and \$210 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Refugee Support Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Trade Disruption Costs from Currency Instability: \$57.4 billion > **NOTE:** > > Annual trade disruption costs from currency instability > > **Source**:^([70](../references.llms.md#ref-world-bank-trade-disruption-conflict)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$40 billion, \$80 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$40 billion and \$80 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Trade Disruption Costs from Currency Instability > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Trade Disruption Costs from Energy Price Volatility: \$125 billion > **NOTE:** > > Annual trade disruption costs from energy price volatility > > **Source**:^([70](../references.llms.md#ref-world-bank-trade-disruption-conflict)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$87 billion, \$175 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$87 billion and \$175 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Trade Disruption Costs from Energy Price Volatility > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Trade Disruption Costs from Shipping Disruptions: \$247 billion > **NOTE:** > > Annual trade disruption costs from shipping disruptions > > **Source**:^([70](../references.llms.md#ref-world-bank-trade-disruption-conflict)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$173 billion, \$346 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$173 billion and \$346 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Trade Disruption Costs from Shipping Disruptions > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Trade Disruption Costs from Supply Chain Disruptions: \$187 billion > **NOTE:** > > Annual trade disruption costs from supply chain disruptions > > **Source**:^([70](../references.llms.md#ref-world-bank-trade-disruption-conflict)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$131 billion, \$262 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$131 billion and \$262 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Trade Disruption Costs from Supply Chain Disruptions > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Veteran Healthcare Costs: \$200 billion > **NOTE:** > > Annual veteran healthcare costs (20-year projected) > > **Source**:^([71](../references.llms.md#ref-veteran-healthcare-cost-projections)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$140 billion, \$280 billion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$140 billion and \$280 billion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Veteran Healthcare Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Days of Chronic Disease Therapy: 1.28 trillion days > **NOTE:** > > Annual days of therapy for chronic conditions globally (diabetes, CVD, respiratory, cancer). IQVIA reports 1.8 trillion total days of therapy in 2019, with 71% for chronic conditions. > > **Source**:^([2](../references.llms.md#ref-iqvia-global-medicines-2024)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[1 trillion days, 1.5 trillion days\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 1 trillion days and 1.5 trillion days (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Days of Chronic Disease Therapy > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Annual Global Spending on Clinical Trials: \$60 billion > **NOTE:** > > Annual global spending on clinical trials (Industry: \$45-60B + Government: \$3-6B + Nonprofits: \$2-5B). Conservative estimate using 15-20% of \$300B total pharma R&D, not inflated market size projections. > > **Source**:^([72](../references.llms.md#ref-industry-clinical-trial-spending-estimate)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$50 billion, \$75 billion\] • Distribution: Lognormal (SE: \$10 billion) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$50 billion and \$75 billion (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Global Spending on Clinical Trials > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Cybercrime Cost CAGR: 15% > **NOTE:** > > Compound annual growth rate of global cybercrime costs. Cybersecurity Ventures: \$3T (2015) -\> \$6T (2021) -\> \$10.5T (2025). AI-enhanced attacks are accelerating this trend. > > **Source**:^([73](../references.llms.md#ref-cybercrime-economy-10-5t)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Cybercrime Costs (2025): \$10.5 trillion > **NOTE:** > > Projected global cybercrime costs in 2025. Includes data theft, productivity loss, IP theft, fraud. More profitable than global trade of all major illegal drugs combined. If measured as a country, would be the 3rd largest economy after US and China. > > **Source**:^([73](../references.llms.md#ref-cybercrime-economy-10-5t)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Daily Deaths from Disease and Aging: 150 thousand deaths/day > **NOTE:** > > Total global deaths per day from all disease and aging (WHO Global Burden of Disease 2024) > > **Source**:^([28](../references.llms.md#ref-who-global-health-estimates-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 7,500 deaths/day) > > #### Input Distribution > > Probability Distribution: Global Daily Deaths from Disease and Aging > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### Global Annual Direct Medical Costs of Disease: \$9.9 trillion > **NOTE:** > > Direct medical costs of disease globally (treatment, hospitalization, medication). Standalone market-cost metric; not included in DALY-based welfare burden to avoid double-counting. > > **Source**:^([44](../references.llms.md#ref-disease-economic-burden-109t)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$7 trillion, \$14 trillion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$7 trillion and \$14 trillion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Annual Direct Medical Costs of Disease > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Annual Productivity Loss from Disease: \$5 trillion > **NOTE:** > > Annual productivity loss from disease globally (absenteeism, reduced output). Standalone market-cost metric; not included in DALY-based welfare burden to avoid double-counting. > > **Source**:^([44](../references.llms.md#ref-disease-economic-burden-109t)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$3.5 trillion, \$7 trillion\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$3.5 trillion and \$7 trillion (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Annual Productivity Loss from Disease > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global GDP (2025): \$115 trillion > **NOTE:** > > Global nominal GDP (2025 estimate). From Political Dysfunction Tax paper citing StatisticsTimes/IMF World Economic Outlook. Used for calculating global opportunity costs as percentage of world economic output. Note: Latest IMF data shows \$117T. > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global GDP per Capita in 1900: \$3,150 > **NOTE:** > > Global GDP per capita in 1900 in constant 2024 USD. Maddison Project: ~\$1,260 in 1990 international dollars, adjusted to 2024 USD (~2.5x). > > **Source**:^([74](../references.llms.md#ref-maddison-project-2020)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: \$500) > > #### Input Distribution > > Probability Distribution: Global GDP per Capita in 1900 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Annual Global Government Spending on Clinical Trials: \$4.5 billion > **NOTE:** > > Annual global government spending on interventional clinical trials (~5-10% of total) > > **Source**:^([75](../references.llms.md#ref-global-government-clinical-trial-spending-estimate)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$3 billion, \$6 billion\] • Distribution: Lognormal (SE: \$1 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$3 billion and \$6 billion (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Global Government Spending on Clinical Trials > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Government Expense Share of GDP: 31.8% > **NOTE:** > > World general government total expense as a share of GDP, using World Bank indicator GC.XPN.TOTL.GD.ZS. The most recent world aggregate in the cited source is 2021. > > **Source**:^([76](../references.llms.md#ref-world-bank-government-expense-pct-gdp)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *~ Medium confidence* ### Global Healthy Life Expectancy (HALE): 63.3 years > **NOTE:** > > Global healthy life expectancy at birth (HALE) from WHO Global Health Observatory, 2019 data (most recent available). HALE measures years lived in full health, adjusting for years lived with disability or disease. > > **Source**:^([28](../references.llms.md#ref-who-global-health-estimates-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 1.5 years) > > #### Input Distribution > > Probability Distribution: Global Healthy Life Expectancy (HALE) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed • Updated 2019* ### Global Household Wealth: \$454 trillion > **NOTE:** > > Total global household wealth (2022/2023 estimate) > > **Source**:^([77](../references.llms.md#ref-cs-global-wealth-report-2023)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Investable Financial Assets: \$305 trillion > **NOTE:** > > Total global financial wealth (2024): equities, bonds, cash/deposits, and investment funds. Excludes real estate and physical assets. This is the addressable capital pool for Prize deposits. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Life Expectancy (2024): 73.4 years > **NOTE:** > > Global life expectancy at birth (2024), WHO global figure. Was previously set to 79 (developed-country treatment access), which the adversarial review flagged: a measured external parameter must carry the measured value; access optimism belongs in explicit forward-looking parameters, not in a present-day data point. > > **Source**:^([28](../references.llms.md#ref-who-global-health-estimates-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 2 years) > > #### Input Distribution > > Probability Distribution: Global Life Expectancy (2024) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed • Updated 2024* ### Remaining Life Expectancy at Age 60 (Global): 21 years > **NOTE:** > > Additional years a person alive at age 60 can expect to live, global both-sexes (WHO life tables: 21.0 years in 2019; 19.6 in COVID-depressed 2021). This is CONDITIONAL remaining life expectancy, not life-expectancy-at-birth minus age: at-birth figures carry child mortality that someone who reached 60 already survived, so subtracting an age from them understates remaining years by roughly 40% at this age. Used for years-of-life-lost per efficacy-lag death. The GBD reference life table would give more (~23 years at 60); WHO period tables are the lower of the two standard choices. > > **Source**:^([78](../references.llms.md#ref-who-gho-life-expectancy-age-60-2021)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[19.6 years, 22 years\] • Distribution: Normal > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 19.6 years and 22 years (±6%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Remaining Life Expectancy at Age 60 (Global) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Median Age (2024): 30.5 years > **NOTE:** > > Global median age in 2024 from UN World Population Prospects 2024 revision. > > **Source**:^([79](../references.llms.md#ref-global-median-age-un-wpp-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Government Medical Research Spending: \$67.5 billion > **NOTE:** > > Global government medical research spending > > **Source**:^([80](../references.llms.md#ref-global-gov-med-research-spending)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$54 billion, \$81 billion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$54 billion and \$81 billion (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Government Medical Research Spending > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Military Spending in 2005 (Constant 2023 USD): \$1.62 trillion > **NOTE:** > > Global military spending in 2005, constant 2023 USD (SIPRI World total). Used as the 20-year reference point for computing the long-horizon real CAGR. > > **Source**:^([81](../references.llms.md#ref-sipri-milex-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Military Spending in 2024: \$2.72 trillion > **NOTE:** > > Global military spending in 2024 > > **Source**:^([82](../references.llms.md#ref-global-military-spending)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Military Spending Real CAGR (10-Year): 3.4% > **NOTE:** > > Real compound annual growth rate of global military spending over the last decade (2014-2024). SIPRI reports 10 consecutive annual increases, with 2024 up 9.4% in real terms. The 10-year CAGR is approximately 3.4% real. > > **Source**:^([81](../references.llms.md#ref-sipri-milex-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Nuclear Weapons Spending: \$92 billion > **NOTE:** > > Annual global spending on nuclear weapons across all nine nuclear-armed states. US: \$51.5B, China: \$11.8B, UK: \$8.1B, Russia: \$8.3B, France: \$6.8B, India: ~\$2.7B, Israel: ~\$1.2B, Pakistan: ~\$1.1B, North Korea: ~\$0.7B. > > **Source**:^([83](../references.llms.md#ref-global-nuclear-weapon-maintenance-100b)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Population in 2024: 8 billion of people > **NOTE:** > > Global population in 2024 > > **Source**:^([84](../references.llms.md#ref-global-population-8-billion)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[7.8 billion of people, 8.2 billion of people\] • Distribution: Lognormal > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 7.8 billion of people and 8.2 billion of people (±2%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Population in 2024 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Population 2040 (Projected): 8.9 billion of people > **NOTE:** > > UN World Population Prospects 2022 median projection for 2040. Interpolated midpoint between ~8.1B (2025) and 9.2B (2045). > > **Source**:^([84](../references.llms.md#ref-global-population-8-billion)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Population 2045 (Projected): 9.2 billion of people > **NOTE:** > > UN World Population Prospects 2022 median projection for 2045. > > **Source**:^([84](../references.llms.md#ref-global-population-8-billion)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Critical Mass Threshold for Social Change: 3.5% > **NOTE:** > > Critical mass threshold for social change (3.5% rule). Chenoweth studied national regime changes; applying to a global treaty adds uncertainty. Lower bound: some movements succeeded at ~1%. Upper bound: entrenched defense-industry opposition and weaker signal from digital signatures vs sustained protest may require up to 10%. > > **Source**:^([85](../references.llms.md#ref-3-5-rule)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[1%, 10%\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 1% and 10% (±129%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Critical Mass Threshold for Social Change > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Registered Voters: 4.13 billion of people > **NOTE:** > > Best current register-based estimate of the number of registered voters worldwide, calculated by summing the latest available country-level electoral-roll counts in International IDEA’s Voter Turnout Database export. Used as the verified-human headcount proxy for the majority-of-humanity coordination target. > > **Source**:^([86](../references.llms.md#ref-international-idea-voter-turnout-database-2026)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Retirement Assets: \$70 trillion > **NOTE:** > > Total global pension and retirement assets (OECD 2024). This is the capital pool that the Prize Fund competes with and could partially absorb. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Gross Savings Rate: 27% > **NOTE:** > > Global gross savings as share of GDP (World Bank, ~27% average 2023-2024) > > **Source**:^([87](../references.llms.md#ref-world-bank-gross-savings-2023)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[24%, 30%\] • Distribution: Normal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 24% and 30% (±11%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Global Gross Savings Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Global Spending on Symptomatic Disease Treatment: \$8.2 trillion > **NOTE:** > > Annual global spending on symptomatic disease treatment > > **Source**:^([44](../references.llms.md#ref-disease-economic-burden-109t)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$6.5 trillion, \$10 trillion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$6.5 trillion and \$10 trillion (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Global Spending on Symptomatic Disease Treatment > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Nuclear Warhead Count: 12,241 warheads > **NOTE:** > > Total global nuclear warhead inventory across nine nuclear-armed states. Includes deployed, reserve, and retired warheads awaiting dismantlement. > > **Source**:^([88](../references.llms.md#ref-world-warheads)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### YLD Proportion of Total DALYs: 0.39 proportion > **NOTE:** > > Proportion of global DALYs that are YLD (years lived with disability) vs YLL (years of life lost). From GBD 2021: 1.13B YLD out of 2.88B total DALYs = 39%. > > **Source**:^([64](../references.llms.md#ref-ihme-gbd-2021)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 0.03 proportion) > > #### Input Distribution > > Probability Distribution: YLD Proportion of Total DALYs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### Government-Controlling Sectors Top-5 Market Cap: \$16.7 trillion > **NOTE:** > > Combined market capitalization of the top-5 US public lobbying spenders in each of the four other government-controlling sectors: pharmaceuticals \$1.79T, technology \$13.28T, insurance \$0.39T, oil and gas \$1.25T. Caveats: Meta (Zuckerberg 60.8% voting) and Alphabet (Page and Brin 52.3%) cannot be majority-acquired; Ellison owns 40.6% of Oracle; the largest insurance lobbyists are mutuals with no shares; trade associations (PhRMA, AHIP, SIFMA, API) are not acquirable > > **Source**:^([89](../references.llms.md#ref-opensecrets-top-lobbying-industries-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$15 trillion, \$18 trillion\] > > **What this means**: We’re quite confident in this estimate. The true value likely falls between \$15 trillion and \$18 trillion (±9%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Government-Controlling Sectors Top-5 Market Cap > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Home Bias Return Drag: 0.8% > **NOTE:** > > Return drag from home bias in fragmented national pension systems. 70+ countries each overweight domestic assets, missing global diversification. IMF and Vanguard studies estimate 0.3-1.5% annual return cost. Wishocratic allocation is inherently global, eliminating this drag. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.3%, 1.5%\] • Distribution: Normal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.3% and 1.5% (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Home Bias Return Drag > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Human Interactome Targeted by Drugs: 12% > **NOTE:** > > Percentage of human interactome (protein-protein interactions) targeted by drugs > > **Source**:^([90](../references.llms.md#ref-clinical-trials-puzzle-interactome)) > > *✓ High confidence* ### ICD-10 Total Codes: 14,000 codes > **NOTE:** > > Total ICD-10 diagnostic codes for human diseases and conditions > > **Source**:^([91](../references.llms.md#ref-icd-10-code-count)) > > *✓ High confidence* ### IRS Annual Operating Budget: \$12.3 billion > **NOTE:** > > IRS enacted annual appropriation, FY2024 (CRS IF12647, Table 1). > > **Source**:^([92](../references.llms.md#ref-crs-irs-appropriations-fy2025)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence • Updated 2024-04* ### US Average IQ Loss from Leaded Gasoline: 2.6 IQ points > **NOTE:** > > Average IQ points lost per person from childhood leaded-gasoline exposure (McFarland et al. 2022: 824M cumulative points across the 2015 US population, average 2.6/person, 5.9 for the worst-hit 1966-1970 birth cohorts). Used as the era-average workforce IQ deficit: lower in the 1930s, higher by the 1980s. > > **Source**:^([93](../references.llms.md#ref-mcfarland-lead-iq-2022)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[1.5 IQ points, 5.9 IQ points\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 1.5 IQ points and 5.9 IQ points (±85%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Average IQ Loss from Leaded Gasoline > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Life Extension from Treaty Research Acceleration: 20 years > **NOTE:** > > Expected years of life extension from 1% treaty research acceleration (25x trial capacity). Bounds: 0 (complete failure) to ~150 (accident-limited lifespan minus current). Lognormal distribution allows for breakthrough scenarios. > > **Source**:^([94](../references.llms.md#ref-longevity-escape-velocity)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[5 years, 100 years\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 5 years and 100 years (±238%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Life Extension from Treaty Research Acceleration > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Livestock Share of Agricultural Land: 80% > **NOTE:** > > Livestock’s share of global agricultural land, combining grazing land and cropland grown for animal feed, ~80% (Our World in Data, FAOSTAT). Poore & Nemecek (2018) report ~83% of farmland by a slightly different boundary. Defensible range ~77-83%. The striking point is the mismatch: this majority of farmland returns only ~18% of calories. > > **Source**:^([95](../references.llms.md#ref-owid-land-for-agriculture)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Livestock Share of Global Calories: 18% > **NOTE:** > > Share of the world’s calories provided by meat, aquaculture, eggs, and dairy, ~18% (Poore & Nemecek 2018, Science, meta-analysis of ~38,700 farms across 119 countries). Our World in Data reports ~17% by FAOSTAT. Pair with LIVESTOCK_SHARE_OF_AGRICULTURAL_LAND: the majority of farmland for a small share of the calories. > > **Source**:^([96](../references.llms.md#ref-poore-nemecek-2018)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Livestock Share of Global Protein: 37% > **NOTE:** > > Share of the world’s protein provided by meat, aquaculture, eggs, and dairy, ~37% (Poore & Nemecek 2018, Science). Our World in Data reports ~38% by FAOSTAT. Delivered using ~83% of farmland and producing the majority of food’s emissions. > > **Source**:^([96](../references.llms.md#ref-poore-nemecek-2018)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Maximum Annual Lobbyist Salary Range: \$2 million > **NOTE:** > > Maximum annual lobbyist salary range > > **Source**:^([97](../references.llms.md#ref-lobbyist-statistics-dc)) > > *✓ High confidence* ### Minimum Annual Lobbyist Salary Range: \$500,000 > **NOTE:** > > Minimum annual lobbyist salary range > > **Source**:^([97](../references.llms.md#ref-lobbyist-statistics-dc)) > > *✓ High confidence* ### Medical QALY Threshold: \$100,000 > **NOTE:** > > Medical cost-effectiveness QALY threshold. Standard threshold for evaluating whether health interventions are cost-effective. Interventions below \$100K/QALY are generally considered cost-effective. > > **Source**:^([98](../references.llms.md#ref-qaly-threshold-history)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Medical Toolchain BRAIN Initiative Planned Budget: \$4.5 billion > **NOTE:** > > Planned NIH BRAIN Initiative commitment described in the BRAIN 2025 scientific vision. > > **Source**:^([99](../references.llms.md#ref-nih-brain-initiative-planned-budget)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Medical Toolchain NIH CRISPR Funding, FY2011-FY2018: \$3.1 billion > **NOTE:** > > Rounded NIH CRISPR-related research funding for FY2011-FY2018 from CRS Table 1. > > **Source**:^([100](../references.llms.md#ref-crs-crispr-nih-funding-2011-2018)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Medical Toolchain Human Genome Project Cost: \$2.7 billion > **NOTE:** > > Approximate Human Genome Project cost used as an observed medical-toolchain anchor. > > **Source**:^([101](../references.llms.md#ref-human-genome-and-genetic-editing)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *~ Medium confidence* ### Medical Toolchain HITECH EHR Incentive Estimated Spending: \$30 billion > **NOTE:** > > GAO estimate of Medicare and Medicaid EHR incentive program spending from 2011 through 2019. > > **Source**:^([102](../references.llms.md#ref-hitech-ehr-incentive-estimated-spending)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Medical Toolchain Operation Warp Speed Potential Vaccine Awards: \$18 billion > **NOTE:** > > GAO-reported total potential estimated value of Operation Warp Speed vaccine candidate awards. > > **Source**:^([103](../references.llms.md#ref-operation-warp-speed-potential-vaccine-awards)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Medical Toolchain PCORnet Infrastructure Funding Anchor: \$325 million > **NOTE:** > > Rounded PCORI-funded PCORnet Studies amount from the Q4 2025 PCORnet dashboard. Used as a network-scale pragmatic-trial infrastructure anchor. > > **Source**:^([104](../references.llms.md#ref-pcornet-infrastructure-funding)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *~ Medium confidence* ### GDP Growth Effect per National IQ Point: 0.11% > **NOTE:** > > Jones & Schneider (2006) BACE estimate: one national-IQ point is associated with a persistent 0.11 percentage-point higher annual GDP per capita growth rate (significant in 99.8% of 1,330 growth regressions). Wide CI reflects the contested cross-country IQ data underlying the estimate and the authors’ own caveat that transitory vs steady-state growth cannot be distinguished. > > **Source**:^([105](../references.llms.md#ref-jones-schneider-iq-growth-2006)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.04%, 0.18%\] • Distribution: Normal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.04% and 0.18% (±64%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: GDP Growth Effect per National IQ Point > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Diseases Getting First Treatment Per Year: 15 diseases/year > **NOTE:** > > Number of diseases that receive their FIRST effective treatment each year under current system. ~9 rare diseases/year (based on 40 years of ODA: 350 with treatment ÷ 40 years), plus ~5-10 common diseases. Note: FDA approves ~50 drugs/year, but most are for diseases that already have treatments. > > **Source**:^([106](../references.llms.md#ref-diseases-getting-first-treatment-annually)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[8 diseases/year, 30 diseases/year\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 8 diseases/year and 30 diseases/year (±73%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Diseases Getting First Treatment Per Year > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### NIH Annual Budget: \$47 billion > **NOTE:** > > NIH annual budget (FY2024/2025) > > **Source**:^([107](../references.llms.md#ref-nih-budget-fy2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$45 billion, \$50 billion\] > > **What this means**: We’re quite confident in this estimate. The true value likely falls between \$45 billion and \$50 billion (±5%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: NIH Annual Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### NIH Clinical Trials Spending Percentage: 3.3% > **NOTE:** > > Percentage of NIH budget spent on clinical trials (3.3%) > > **Source**:^([108](../references.llms.md#ref-nih-clinical-trials-spending-pct-3-3)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[2%, 5%\] • Distribution: Beta > > **What this means**: There’s significant uncertainty here. The true value likely falls between 2% and 5% (±45%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: NIH Clinical Trials Spending Percentage > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### NIH Standard Research Cost per QALY: \$50,000 > **NOTE:** > > Typical cost per QALY for standard NIH-funded medical research portfolio. Reflects the inefficiency of traditional RCTs and basic research-heavy allocation. See confidence_interval for range; ICER uses higher thresholds for value-based pricing. > > **Source**:^([109](../references.llms.md#ref-standard-medical-research-roi)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$20,000, \$100,000\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$20,000 and \$100,000 (±80%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: NIH Standard Research Cost per QALY > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Nuclear Winter Warhead Threshold: 100 warheads > **NOTE:** > > Approximate number of warheads needed to trigger a regional-scale nuclear winter sufficient to collapse the global food system. A 100-warhead regional exchange (Robock/Toon 2007, extended by Xia et al. 2022) injects ~5 Tg of soot into the stratosphere, drops global temperatures ~1.8C for a decade, shortens growing seasons worldwide, and kills ~2 billion people from famine. Civilization as the median human experiences it does not survive. Xia 2022 shows total agricultural collapse (~5B deaths) at ~4,400 warheads; this parameter uses the lower threshold for median-human civilizational collapse. > > **Source**:^([110](../references.llms.md#ref-nuke-winter-150tg)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[50 warheads, 300 warheads\] • Distribution: Uniform > > **What this means**: This estimate is highly uncertain. The true value likely falls between 50 warheads and 300 warheads (±125%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Nuclear Winter Warhead Threshold > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Oxford RECOVERY Trial Duration: 3 months > **NOTE:** > > Oxford RECOVERY trial duration (found life-saving treatment in 3 months) > > **Source**:^([8](../references.llms.md#ref-recovery-trial-82x-cost-reduction)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Patient Willingness to Participate in Clinical Trials: 44.8% > **NOTE:** > > Patient willingness to participate in drug trials (44.8% in surveys, 88% when actually approached) > > **Source**:^([111](../references.llms.md#ref-patient-willingness-clinical-trials)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[40%, 50%\] • Distribution: Normal (SE: 2.5%) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 40% and 50% (±11%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Patient Willingness to Participate in Clinical Trials > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Pentagon Unaccounted Funds: \$2.46 trillion > **NOTE:** > > Funds the Department of Defense has failed to account for across seven consecutive failed audits > > **Source**:^([112](../references.llms.md#ref-pentagon-unaccounted-2-5t)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Pharma Drug Development Cost (Current System): \$2.6 billion > **NOTE:** > > Average cost to develop one drug in current system > > **Source**:^([113](../references.llms.md#ref-drug-development-cost)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1.5 billion, \$4 billion\] • Distribution: Lognormal (SE: \$500 million) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$1.5 billion and \$4 billion (±48%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pharma Drug Development Cost (Current System) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### Pharma Average Drug Revenue (Current System): \$6.7 billion > **NOTE:** > > Median lifetime revenue per successful drug (study of 361 FDA-approved drugs 1995-2014, median follow-up 13.2 years) > > **Source**:^([114](../references.llms.md#ref-pharma-drug-revenue-average)) > > *✓ High confidence • 📊 Peer-reviewed* ### Annual Life-Years Saved by Pharmaceuticals: 149 million life-years > **NOTE:** > > Annual life-years saved by pharmaceutical innovations globally. Lichtenberg (2019, NBER WP 25483) found that drugs launched after 1981 saved 148.7M life-years in 2013 across 22 countries using 3-way fixed-effects regression (disease-country-year). 95% CI \[79.4M, 239.8M\] propagated from Table 2 regression standard errors (β₀₋₁₁=-0.031±0.008, β₁₂₊=-0.057±0.013). > > **Source**:^([10](../references.llms.md#ref-lichtenberg-life-years-saved-2019)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[79.4 million life-years, 240 million life-years\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 79.4 million life-years and 240 million life-years (±54%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Annual Life-Years Saved by Pharmaceuticals > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Pharmaceutical Net Profit Margin: 18.5% > **NOTE:** > > Net profit margin for Drugs (Pharmaceutical) industry (228 firms, Damodaran Jan 2026) > > #### Uncertainty Range > > **Technical**: 95% CI: \[15%, 22%\] > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 15% and 22% (±19%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Pharmaceutical Net Profit Margin > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Pharma ROI (Current System): 1.2% > **NOTE:** > > ROI for pharma R&D (2022 historic low from Deloitte study of top 20 pharma companies, down from 6.8% in 2021, recovered to 5.9% in 2024) > > **Source**:^([115](../references.llms.md#ref-pharma-roi-current)) > > *✓ High confidence • 📊 Peer-reviewed* ### Pharma Drug Success Rate (Current System): 10% > **NOTE:** > > Percentage of drugs that reach market in current system > > **Source**:^([116](../references.llms.md#ref-drug-trial-success-rate-12-pct)) > > *✓ High confidence • 📊 Peer-reviewed* ### Phase I-Passed Compounds Globally: 7,500 compounds > **NOTE:** > > Investigational compounds that have passed Phase I globally (midpoint of 5,000-10,000 range) > > **Source**:^([46](../references.llms.md#ref-bio-clinical-development-2021)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[5,000 compounds, 10,000 compounds\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between 5,000 compounds and 10,000 compounds (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Phase I-Passed Compounds Globally > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Phase I Safety Trial Duration: 2.3 years > **NOTE:** > > Phase I safety trial duration > > **Source**:^([46](../references.llms.md#ref-bio-clinical-development-2021)) > > *✓ High confidence • 📊 Peer-reviewed • Updated 2021* ### Phase 2/3 Share of Clinical Trial Costs: 69% > **NOTE:** > > Percentage of total clinical trial spending on Phase 2/3 efficacy testing (Phase 2: 24% + Phase 3: 45%) > > **Source**:^([35](../references.llms.md#ref-global-clinical-trials-market-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 5%) > > #### Input Distribution > > Probability Distribution: Phase 2/3 Share of Clinical Trial Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Phase 3 Trial Total Cost (Minimum): \$20 million > **NOTE:** > > Phase 3 trial total cost (minimum) > > **Source**:^([117](../references.llms.md#ref-phase-3-cost-per-trial-range)) > > *✓ High confidence* ### Pragmatic Trial Median Cost per Patient (PMC Review): \$97 > **NOTE:** > > Median cost per patient in embedded pragmatic clinical trials (Ramsberg & Platt 2018: 108 trials reviewed, 64 with cost data). IQR: \$19-\$478 (2015 USD). > > **Source**:^([118](../references.llms.md#ref-pmc-pragmatic-trial-cost)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$19, \$478\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$19 and \$478 (±237%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Median Cost per Patient (PMC Review) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Fossil Fuel Subsidies: \$1.3 trillion > **NOTE:** > > Global explicit fossil fuel subsidies (governments undercharging for energy supply costs). IMF 2022 estimate. These subsidies actively encourage consumption of negative-externality goods, working against climate goals. Note: IMF implicit subsidies (externalities) are much larger (~\$7T). > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1.1 trillion, \$1.5 trillion\] • Distribution: Normal (SE: \$100 billion) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$1.1 trillion and \$1.5 trillion (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Global Fossil Fuel Subsidies > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Health Opportunity Cost: \$34 trillion > **NOTE:** > > Annual opportunity cost of slow-motion regulatory environment for health innovation. Murphy-Topel (2006) valued cancer cure at \$50T (inflation-adjusted ~\$100T in 2025). Longevity dividend of 1 extra year = \$38T globally. PCTs could accelerate cures by 10+ years; NPV of 10-year delay at 3% discount = ~\$25T. Conservative estimate: \$34T annually in lives lost and healthspan denied. > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$20 trillion, \$80 trillion\] • Distribution: Lognormal (SE: \$15 trillion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$20 trillion and \$80 trillion (±88%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Health Opportunity Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Global Lead Poisoning Cost: \$6 trillion > **NOTE:** > > Global cost of lead exposure: World Bank/Lancet estimate. 765 million IQ points lost annually, 5.5 million premature CVD deaths. Cost to eliminate lead from paint, spices, batteries is trivial compared to damage. This is an arbitrage opportunity of immense scale that governance has failed to execute. > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$4 trillion, \$8 trillion\] • Distribution: Normal (SE: \$1 trillion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$4 trillion and \$8 trillion (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Global Lead Poisoning Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Global Migration Opportunity Cost: \$57 trillion > **NOTE:** > > Unrealized output from migration restrictions. Clemens (2011) estimated eliminating labor mobility barriers could increase global GDP by 50-150%. At \$115T global GDP, Clemens lower bound = \$57T; upper bound = \$170T. The estimate is controversial: critics argue it assumes full global labor mobility and ignores fiscal and social adjustment costs. Skeptical lower bound: ~\$5T (partial reforms only). Even 5% workforce mobility would generate trillions, exceeding all foreign aid ever given. This is the largest and most uncertain single component of the dysfunction tax. > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$5 trillion, \$170 trillion\] • Distribution: Lognormal (SE: \$30 trillion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$5 trillion and \$170 trillion (±145%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Migration Opportunity Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Global R&D Misallocation Cost: \$4 trillion > **NOTE:** > > Annual opportunity cost from outcome-blind research allocation. Human Genome Project: \$3.8B cost, \$796B-1T impact (141:1 ROI). Fusion DEMO plant: \$5-10B could solve energy/climate permanently. Current system funds inputs (grants, process) rather than outcomes (cures, working reactors). Reallocating \$200B from military waste at 20x multiplier = \$4T foregone growth. > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$2 trillion, \$10 trillion\] • Distribution: Lognormal (SE: \$2 trillion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$2 trillion and \$10 trillion (±100%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global R&D Misallocation Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Political Success Probability: 1% > **NOTE:** > > Estimated probability of treaty ratification and sustained implementation. Central estimate 1% is conservative. This assumes 99% chance of failure. > > **Source**:^([119](../references.llms.md#ref-icbl-ottawa-treaty)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.1%, 10%\] • Distribution: Beta (SE: 2%) > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.1% and 10% (±495%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Political Success Probability > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Post-Office Career Value (per politician): \$10 million > **NOTE:** > > Net present value of post-office career premium for average congressperson (10 years x \$1M/year premium). Based on documented cases: Gephardt \$7M/year, Daschle \$2M+/year. > > **Source**:^([120](../references.llms.md#ref-opensecrets-revolving-door)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$5 million, \$20 million\] > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$5 million and \$20 million (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Post-Office Career Value (per politician) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Post-1962 Drug Approval Reduction: 70% > **NOTE:** > > Reduction in new drug approvals after 1962 Kefauver-Harris Amendment (70% drop from 43→17 drugs/year) > > **Source**:^([121](../references.llms.md#ref-post-1962-drug-approval-drop)) > > *✓ High confidence • Updated 1962-1970* ### Pre-1962 Drug Development Cost (1980 Dollars): \$6.5 million > **NOTE:** > > Average drug development cost before 1962 FDA efficacy regulations, adjusted to 1980 dollars (Baily 1972) > > **Source**:^([7](../references.llms.md#ref-pre-1962-drug-costs-baily-1972)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$5.2 million, \$7.8 million\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$5.2 million and \$7.8 million (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pre-1962 Drug Development Cost (1980 Dollars) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### Pre-1962 Drug Development Cost (2024 Dollars): \$24.7 million > **NOTE:** > > Pre-1962 drug development cost adjusted to 2024 dollars (\$6.5M × 3.80 = \$24.7M, CPI-adjusted from Baily 1972) > > **Source**:^([7](../references.llms.md#ref-pre-1962-drug-costs-baily-1972)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$19.5 million, \$30 million\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$19.5 million and \$30 million (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pre-1962 Drug Development Cost (2024 Dollars) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### Pre-1962 Physician Count (Unverified): 144 thousand physicians > **NOTE:** > > Estimated physicians conducting real-world efficacy trials pre-1962 (unverified estimate) > > **Source**:^([122](../references.llms.md#ref-pre-1962-physician-trials)) > > *? Low confidence* ### Total Number of Rare Diseases Globally: 7,000 diseases > **NOTE:** > > Total number of rare diseases globally > > **Source**:^([123](../references.llms.md#ref-95-pct-diseases-no-treatment)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[6,000 diseases, 10,000 diseases\] • Distribution: Normal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 6,000 diseases and 10,000 diseases (±29%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Total Number of Rare Diseases Globally > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Recovery Trial Cost per Patient: \$500 > **NOTE:** > > RECOVERY trial cost per patient. Note: RECOVERY was an outlier - hospital-based during COVID emergency, minimal extra procedures, existing NHS infrastructure, streamlined consent. Replicating this globally will be harder. > > **Source**:^([124](../references.llms.md#ref-recovery-cost-500)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$400, \$2,500\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$400 and \$2,500 (±210%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Recovery Trial Cost per Patient > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### RECOVERY Trial Global Lives Saved: 1 million lives > **NOTE:** > > Estimated lives saved globally by RECOVERY trial’s dexamethasone discovery. NHS England estimate (March 2021). Based on Águas et al. Nature Communications 2021 methodology applying RECOVERY trial mortality reductions (36% ventilated, 18% oxygen) to global COVID hospitalizations. Wide uncertainty range reflects extrapolation assumptions. > > **Source**:^([125](../references.llms.md#ref-recovery-trial-1m-lives-saved)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[500 thousand lives, 2 million lives\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 500 thousand lives and 2 million lives (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: RECOVERY Trial Global Lives Saved > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### RECOVERY Trial Total Cost: \$20 million > **NOTE:** > > Total cost of UK RECOVERY trial. Enrolled tens of thousands of patients across multiple treatment arms. Discovered dexamethasone reduces COVID mortality by ~1/3 in severe cases. > > **Source**:^([8](../references.llms.md#ref-recovery-trial-82x-cost-reduction)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$15 million, \$25 million\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$15 million and \$25 million (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: RECOVERY Trial Total Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Mean Age of Preventable Death from Post-Safety Efficacy Delay: 62 years > **NOTE:** > > Mean age of preventable death from post-safety efficacy testing regulatory delay (Phase 2-4) > > **Source**:^([28](../references.llms.md#ref-who-global-health-estimates-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: 3 years) > > #### Input Distribution > > Probability Distribution: Mean Age of Preventable Death from Post-Safety Efficacy Delay > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence • 📊 Peer-reviewed* ### Pre-Death Suffering Period During Post-Safety Efficacy Delay: 6 years > **NOTE:** > > Pre-death suffering period during post-safety efficacy testing delay (average years lived with untreated condition while awaiting Phase 2-4 completion) > > **Source**:^([28](../references.llms.md#ref-who-global-health-estimates-2024)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[4 years, 9 years\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 4 years and 9 years (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pre-Death Suffering Period During Post-Safety Efficacy Delay > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence • 📊 Peer-reviewed* ### September 11 Deaths: 2,977 people > **NOTE:** > > Total deaths in the September 11, 2001 attacks. 2,977 victims (excluding 19 hijackers). Used as a reference point for scale comparisons. > > **Source**:^([126](../references.llms.md#ref-september-11-memorial)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Global Listed Equity Market Capitalization: \$115 trillion > **NOTE:** > > Approximate global market capitalization of listed domestic companies. The 2024 World Bank series is about \$115 trillion; the interval allows for market movement, coverage differences, and listed-company definition differences. > > **Source**:^([127](../references.llms.md#ref-world-bank-market-cap-listed-companies)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$90 trillion, \$140 trillion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$90 trillion and \$140 trillion (±22%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global Listed Equity Market Capitalization > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### SE Bot LLM Cost Per Post: \$0.006 > **NOTE:** > > Model inference cost to draft one correction reply. Central case assumes roughly 1,000 input tokens plus 300 output tokens using a Sonnet-class model with some prompt caching or batch routing. The interval spans Haiku-class routing, Sonnet batch discounts, longer replies, and retries. > > **Source**:^([128](../references.llms.md#ref-anthropic-claude-pricing-2026)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$0.002, \$0.03\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$0.002 and \$0.03 (±233%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: SE Bot LLM Cost Per Post > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Singapore Life Expectancy: 84.1 years > **NOTE:** > > Singapore life expectancy at birth. 6.6 years LONGER than US (84.1 vs 77.5) despite government spending at less than half the rate. > > **Source**:^([129](../references.llms.md#ref-who-life-expectancy)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Return on Investment from Smallpox Eradication Campaign: 280:1 > **NOTE:** > > Return on investment from smallpox eradication campaign > > **Source**:^([130](../references.llms.md#ref-smallpox-eradication-roi)) > > *✓ High confidence* ### Standard Economic Value per QALY: \$150,000 > **NOTE:** > > Standard economic value per QALY > > **Source**:^([131](../references.llms.md#ref-qaly-value)) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: \$30,000) > > #### Input Distribution > > Probability Distribution: Standard Economic Value per QALY > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Annual Cost of Sugar Subsidies per Person: \$10 > **NOTE:** > > Annual cost of sugar subsidies per person > > **Source**:^([132](../references.llms.md#ref-sugar-subsidies-cost)) > > *✓ High confidence* ### Switzerland’s Defense Spending as Percentage of GDP: 0.7% > **NOTE:** > > Switzerland’s defense spending as percentage of GDP (0.7%) > > **Source**:^([133](../references.llms.md#ref-swiss-military-budget-0-7-pct-gdp)) > > *✓ High confidence* ### Switzerland GDP per Capita: \$93,000 > **NOTE:** > > Switzerland GDP per capita > > **Source**:^([134](../references.llms.md#ref-swiss-vs-us-gdp-per-capita)) > > *✓ High confidence* ### Switzerland Life Expectancy: 84 years > **NOTE:** > > Switzerland life expectancy at birth. 6.5 years LONGER than US (84.0 vs 77.5) despite lower government spending as % of GDP. > > **Source**:^([129](../references.llms.md#ref-who-life-expectancy)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Deaths from 9/11 Terrorist Attacks: 2,996 deaths > **NOTE:** > > Deaths from 9/11 terrorist attacks > > **Source**:^([17](../references.llms.md#ref-chance-of-dying-from-terrorism-1-in-30m)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Thalidomide Cases Worldwide: 15,000 cases > **NOTE:** > > Total thalidomide birth defect cases worldwide (1957-1962) > > **Source**:^([135](../references.llms.md#ref-thalidomide-scandal)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[10,000 cases, 20,000 cases\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 10,000 cases and 20,000 cases (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Thalidomide Cases Worldwide > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Thalidomide Disability Weight: 0.4:1 > **NOTE:** > > Disability weight for thalidomide survivors (limb deformities, organ damage) > > **Source**:^([136](../references.llms.md#ref-thalidomide-survivors-health)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.32:1, 0.48:1\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 0.32:1 and 0.48:1 (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Thalidomide Disability Weight > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Thalidomide Mortality Rate: 40% > **NOTE:** > > Mortality rate for thalidomide-affected infants (died within first year) > > **Source**:^([135](../references.llms.md#ref-thalidomide-scandal)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[35%, 45%\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 35% and 45% (±13%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Thalidomide Mortality Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Thalidomide Survivor Lifespan: 60 years > **NOTE:** > > Average lifespan for thalidomide survivors > > **Source**:^([136](../references.llms.md#ref-thalidomide-survivors-health)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[50 years, 70 years\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 50 years and 70 years (±17%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Thalidomide Survivor Lifespan > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### US Population Share 1960: 6% > **NOTE:** > > US share of world population in 1960 > > **Source**:^([137](../references.llms.md#ref-us-census-world-population-1960)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[5.5%, 6.5%\] • Distribution: Lognormal > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 5.5% and 6.5% (±8%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Population Share 1960 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Phase 3 Cost per Patient: \$41,000 > **NOTE:** > > Phase 3 cost per patient (median from FDA study) > > **Source**:^([138](../references.llms.md#ref-trial-costs-fda-study)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$20,000, \$120,000\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$20,000 and \$120,000 (±122%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Phase 3 Cost per Patient > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Treatment Disability Reduction: 0.25 weight > **NOTE:** > > Average disability weight reduction from pharmaceutical treatment. Untreated chronic disease averages 0.35 disability weight, treated disease averages 0.10, difference is 0.25. > > **Source**:^([139](../references.llms.md#ref-gbd-disability-weights)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.15 weight, 0.35 weight\] • Distribution: Normal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 0.15 weight and 0.35 weight (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Treatment Disability Reduction > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence • 📊 Peer-reviewed* ### US Alzheimer’s Annual Cost: \$355 billion > **NOTE:** > > Annual US cost of Alzheimer’s disease (direct and indirect) > > **Source**:^([140](../references.llms.md#ref-disease-cost-alzheimers-1300b)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$302 billion, \$408 billion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$302 billion and \$408 billion (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Alzheimer’s Annual Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### US Cancer Annual Cost: \$208 billion > **NOTE:** > > Annual US cost of cancer (direct and indirect) > > **Source**:^([141](../references.llms.md#ref-disease-cost-cancer-1800b)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$177 billion, \$239 billion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$177 billion and \$239 billion (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Cancer Annual Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### US Annual Chronic Disease Spending: \$4.1 trillion > **NOTE:** > > US annual chronic disease spending > > **Source**:^([142](../references.llms.md#ref-us-chronic-disease-spending)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$3.3 trillion, \$5 trillion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$3.3 trillion and \$5 trillion (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Annual Chronic Disease Spending > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### US Diabetes Annual Cost: \$327 billion > **NOTE:** > > Annual US cost of diabetes (direct and indirect) > > **Source**:^([143](../references.llms.md#ref-disease-cost-diabetes-1500b)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$278 billion, \$376 billion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$278 billion and \$376 billion (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Diabetes Annual Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### US Federal Spending (FY2024): \$6.8 trillion > **NOTE:** > > US federal government spending in FY2024. CBO reports outlays of \$6.8T (23.9% of GDP). Includes mandatory spending, discretionary spending, and net interest (\$888B). > > **Source**:^([144](../references.llms.md#ref-cbo-long-term-budget-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### US Federal Discretionary Spending (FY2024): \$1.7 trillion > **NOTE:** > > US federal discretionary spending in FY2024. Approximately \$886B defense + ~\$814B non-defense discretionary = ~\$1.7T. Used as denominator for discretionary efficiency rating (Cat 1 waste items are discretionary/fungible). > > **Source**:^([144](../references.llms.md#ref-cbo-long-term-budget-2024)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### US GDP (2024): \$28.8 trillion > **NOTE:** > > US GDP in 2024 dollars for calculating policy costs as percentage of GDP. > > **Source**:^([145](../references.llms.md#ref-worldbank-gdp)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Agricultural Subsidies Deadweight Loss: \$75 billion > **NOTE:** > > Deadweight loss from US agricultural subsidies. Direct subsidies ~\$30B/yr but create larger distortions: overproduction, environmental damage, benefits concentrated in large farms (top 10% receive 78% of subsidies). Total welfare loss ~\$75B. Textbook example of capture; very high economist consensus. \[CATEGORY 1: Direct Spending\] > > **Source**:^([146](../references.llms.md#ref-ewg-farm-subsidies)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$50 billion, \$120 billion\] • Distribution: Lognormal (SE: \$25 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$50 billion and \$120 billion (±47%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Agricultural Subsidies Deadweight Loss > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Corporate Welfare Waste: \$181 billion > **NOTE:** > > Direct US federal corporate welfare: subsidies to agriculture (\$16.4B), green energy tax credits, semiconductor aid, aviation support. Agricultural subsidies are highly regressive (top 10% receive 63%). Cato Institute forensic tally. \[CATEGORY 1: Direct Spending\] > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$150 billion, \$220 billion\] • Distribution: Normal (SE: \$20 billion) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$150 billion and \$220 billion (±19%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Corporate Welfare Waste > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Drug War Cost: \$90 billion > **NOTE:** > > Annual cost of drug war: ~\$41B federal drug control budget, ~\$10B state/local enforcement, ~\$40B incarceration and lost productivity. After 50+ years and \$1T+ spent, drug use is higher than ever. \[CATEGORY 1: Direct Spending\] > > **Source**:^([147](../references.llms.md#ref-drugpolicyalliance2021)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$60 billion, \$150 billion\] • Distribution: Lognormal (SE: \$30 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$60 billion and \$150 billion (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Drug War Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Fossil Fuel Subsidies (Explicit): \$50 billion > **NOTE:** > > US explicit fossil fuel subsidies (direct payments, tax breaks). IMF estimates US total subsidies at \$649B but ~92% is implicit (externalities). This figure includes only explicit subsidies (~\$50B) for defensibility. \[CATEGORY 1: Direct Spending\] > > **Source**:^([148](../references.llms.md#ref-imf-fossilfuel2023)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$30 billion, \$80 billion\] • Distribution: Lognormal (SE: \$15 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$30 billion and \$80 billion (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Fossil Fuel Subsidies (Explicit) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Healthcare System Inefficiency: \$1.2 trillion > **NOTE:** > > US healthcare spending inefficiency. US spends ~\$4.5T/yr (18% GDP) vs 9-11% in comparable OECD countries with similar/better outcomes. Papanicolas et al. (2018 JAMA) and multiple studies document \$1-1.5T in excess spending from administrative complexity, high prices, and poor care coordination. Very high economist consensus. \[CATEGORY 4: System Inefficiency\] > > **Source**:^([149](../references.llms.md#ref-papanicolas2018)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1 trillion, \$1.5 trillion\] • Distribution: Normal (SE: \$150 billion) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$1 trillion and \$1.5 trillion (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Healthcare System Inefficiency > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Housing/Zoning Restrictions Cost: \$1.4 trillion > **NOTE:** > > GDP loss from housing/zoning restrictions. Original Hsieh-Moretti (2019 AEJ:Macro) estimate of 36% GDP growth reduction was substantially revised by Greaney (2023). Current \$1.4T represents a moderate estimate; revised lower bound implies ~\$500B. \[CATEGORY 3: GDP Loss\] > > **Source**:^([150](../references.llms.md#ref-hsieh-moretti2019)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$500 billion, \$2 trillion\] • Distribution: Lognormal (SE: \$300 billion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$500 billion and \$2 trillion (±54%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Housing/Zoning Restrictions Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Regulatory Red Tape Waste: \$580 billion > **NOTE:** > > Deadweight loss from US regulatory red tape (procedural friction without safety benefits). Competitive Enterprise Institute estimates total regulatory burden at \$2.15T; European studies find red tape costs 0.1-4% of GDP. Conservative estimate: ~2% of US GDP = \$580B. \[CATEGORY 2: Compliance Burden\] > > **Source**:^([11](../references.llms.md#ref-political-dysfunction-tax-paper-2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$290 billion, \$1 trillion\] • Distribution: Lognormal (SE: \$200 billion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$290 billion and \$1 trillion (±61%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Regulatory Red Tape Waste > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Tariff Cost (GDP Loss): \$160 billion > **NOTE:** > > Annual GDP reduction from US tariffs and retaliation. Yale Budget Lab estimates 0.6% smaller GDP in long run, equivalent to \$160B annually. Trade barriers reduce efficiency and raise consumer prices. \[CATEGORY 3: GDP Loss\] > > **Source**:^([151](../references.llms.md#ref-yalebudgetlab2025)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$90 billion, \$250 billion\] • Distribution: Normal (SE: \$50 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$90 billion and \$250 billion (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Tariff Cost (GDP Loss) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Tax Compliance Waste: \$546 billion > **NOTE:** > > Annual cost of US tax code compliance: 7.9 billion hours of lost productivity (\$413B) plus \$133B in out-of-pocket costs. Equals nearly 2% of GDP. Could be largely eliminated with simplified tax code or return-free filing. \[CATEGORY 2: Compliance Burden\] > > **Source**:^([152](../references.llms.md#ref-taxfoundation2024-compliance)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$450 billion, \$650 billion\] • Distribution: Normal (SE: \$50 billion) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$450 billion and \$650 billion (±18%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Tax Compliance Waste > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### US Heart Disease Annual Cost: \$363 billion > **NOTE:** > > Annual US cost of heart disease and stroke (direct and indirect) > > **Source**:^([153](../references.llms.md#ref-disease-cost-heart-disease-2100b)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$309 billion, \$417 billion\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$309 billion and \$417 billion (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Heart Disease Annual Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • 📊 Peer-reviewed* ### US Military Spending in 1939 (Constant 2024 Dollars): \$29 billion > **NOTE:** > > US military spending in 1939 (pre-WW2 baseline) in constant 2024 dollars > > **Source**:^([154](../references.llms.md#ref-us-military-spending-historical-constant-dollars)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### US Military Spending at WW2 Peak (Constant 2024 Dollars): \$1.42 trillion > **NOTE:** > > US military spending at WW2 peak (1945) in constant 2024 dollars > > **Source**:^([154](../references.llms.md#ref-us-military-spending-historical-constant-dollars)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### US Military Spending in 1947 (Constant 2024 Dollars): \$176 billion > **NOTE:** > > US military spending in 1947 (post-WW2 trough, 2 years after peak) in constant 2024 dollars > > **Source**:^([154](../references.llms.md#ref-us-military-spending-historical-constant-dollars)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### US Military Spending in 2024: \$886 billion > **NOTE:** > > US military spending in 2024 in constant dollars > > **Source**:^([154](../references.llms.md#ref-us-military-spending-historical-constant-dollars)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### US Military Spending as Percentage of GDP: 3.5% > **NOTE:** > > US military spending as percentage of GDP (2024) > > **Source**:^([155](../references.llms.md#ref-us-military-budget-3-5-pct-gdp)) > > *✓ High confidence* ### US Population in 2024: 335 million people > **NOTE:** > > US population in 2024 > > **Source**:^([156](../references.llms.md#ref-us-voter-population)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[330 million people, 340 million people\] • Distribution: Lognormal > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 330 million people and 340 million people (±1%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: US Population in 2024 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Senators for Treaty Ratification: 67 senators > **NOTE:** > > Senators needed for treaty ratification (2/3 majority per Article II, Section 2) > > **Source**:^([157](../references.llms.md#ref-us-senate-treaties)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Average Smuggler Fee per US Border Crossing: \$6,937 > **NOTE:** > > Average fee Mexican migrants paid smugglers to enter the United States (EMIF Norte survey of 20,000+ migrants, second half of 2022). Reported street prices ran higher after 2023; the interval covers the range. This is the market-revealed willingness to pay for entry. > > **Source**:^([158](../references.llms.md#ref-emif-coyote-fees-2022)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$4,000, \$14,000\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$4,000 and \$14,000 (±72%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Average Smuggler Fee per US Border Crossing > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence • Updated 2023-04* ### US Federal Campaign Spending (2024): \$20 billion > **NOTE:** > > Total US federal election spending in 2024 cycle including presidential, congressional, party committees, and PACs. Source: FEC Statistical Summary 2024. > > **Source**:^([159](../references.llms.md#ref-fec-2024-summary)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$18 billion, \$22 billion\] > > **What this means**: We’re quite confident in this estimate. The true value likely falls between \$18 billion and \$22 billion (±10%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: US Federal Campaign Spending (2024) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### US Total Lobbying (2024): \$4.4 billion > **NOTE:** > > Total US federal lobbying expenditure in 2024 (record year). Source: OpenSecrets. > > **Source**:^([160](../references.llms.md#ref-opensecrets-lobbying-2024)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$3.74 billion, \$5.06 billion\] > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$3.74 billion and \$5.06 billion (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: US Total Lobbying (2024) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Probability of Decisive Vote (US): 1 in 60 million > **NOTE:** > > Probability of a single vote being decisive in a US presidential election. Gelman, Silver, and Edlin (2012) estimate roughly 1 in 60 million on average, varying by state from 1 in 10 million (swing states) to 1 in 1 billion (safe states). > > **Source**:^([161](../references.llms.md#ref-odds-of-decisive-vote)) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *✓ High confidence* ### Valley of Death Attrition Rate: 40% > **NOTE:** > > Percentage of promising Phase 1-passed compounds abandoned primarily due to Phase 2/3 cost barriers (not scientific failure). Conservative estimate: many rare disease, natural compound, and low-margin drugs never tested. > > **Source**:^([162](../references.llms.md#ref-valley-of-death-attrition)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[25%, 55%\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between 25% and 55% (±38%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Valley of Death Attrition Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *~ Medium confidence* ### Value of Statistical Life: \$10 million > **NOTE:** > > Value of Statistical Life (conservative estimate) > > **Source**:^([163](../references.llms.md#ref-dot-vsl-13-6m)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$5 million, \$15 million\] • Distribution: Gamma (SE: \$3 million) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$5 million and \$15 million (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The gamma distribution means values follow a specific statistical pattern.* > > #### Input Distribution > > Probability Distribution: Value of Statistical Life > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Venture Capital Gross Return: 17% > **NOTE:** > > Venture capital / private equity gross return (before 2-and-20 fees). Cambridge Associates US VC index 25-year pooled gross IRR. The Prize Fund charges zero fees, so gross return is the correct baseline. Lockup premium is already embedded: VC/PE IS illiquid. > > #### Uncertainty Range > > **Technical**: 95% CI: \[13%, 22%\] • Distribution: Normal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 13% and 22% (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Venture Capital Gross Return > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *✓ High confidence* ### Vitamin A Supplementation Cost per DALY: \$37 > **NOTE:** > > Cost per DALY for vitamin A supplementation programs (India: \$23-50; Africa: \$40-255; wide variation by region and baseline VAD prevalence). Using India midpoint as conservative estimate. > > **Source**:^([164](../references.llms.md#ref-vitamin-a-cost-per-daly)) > > *~ Medium confidence* ### 1900 Military Spending Freeze Baseline: \$66.1 billion > **NOTE:** > > Global military spending in 1900 in constant 2023 USD, from the Correlates of War National Material Capabilities (NMC) dataset (knowledge/data/global-military-spending-1900-2024-constant-2023-usd.csv, COW_NMC source). Used as the annual real spending cap in the 1900-freeze counterfactual. > > **Source**:^([13](../references.llms.md#ref-correlates-of-war-nmc)) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$50 billion, \$90 billion\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$50 billion and \$90 billion (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: 1900 Military Spending Freeze Baseline > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *? Low confidence* ### Return on Investment from Water Fluoridation Programs: 23:1 > **NOTE:** > > Return on investment from water fluoridation programs > > **Source**:^([165](../references.llms.md#ref-clean-water-sanitation-roi)) > > *✓ High confidence* ### Cost-Effectiveness Threshold (\$50,000/QALY): \$50,000 > **NOTE:** > > Cost-effectiveness threshold widely used in US health economics (\$50,000/QALY, from 1980s dialysis costs) > > **Source**:^([166](../references.llms.md#ref-who-cost-effectiveness-threshold)) > > *✓ High confidence* ## Core Definitions Fundamental parameters and constants used throughout the analysis. ### ADAPTABLE Trial Patients Enrolled: 15,076 patients > **NOTE:** > > Patients enrolled in ADAPTABLE trial (PCORnet 2016-2019). Enrolled across 40 clinical sites. Precise count from trial completion records. > > *Core definition* ### Algorithmic Monetary Authority Annual Operating Cost: \$7.5 million > **NOTE:** > > All-in annual operating cost of the Algorithmic Monetary Authority: basket monitoring, rule execution, continuous public verification, and the humans who hold the pager. Sized as a small monitoring operation; the rule itself is one formula and requires no committee. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$3 million, \$20 million\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$3 million and \$20 million (±113%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Algorithmic Monetary Authority Annual Operating Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Aligned Election Commission Annual Operating Cost: \$7 million > **NOTE:** > > All-in annual operating cost of the Aligned Election Commission: alignment-score computation, public data pipelines, and fund routing. Sized as a small data-engineering operation; the most similar existing system is the NRA’s politician scorecard, which runs on a budget that rounds to zero. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$2 million, \$20 million\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$2 million and \$20 million (±129%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Aligned Election Commission Annual Operating Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Annual Working Hours: 2,000 hours/year > **NOTE:** > > Standard annual working hours globally. Approximately 40 hours/week x 50 weeks. ILO estimates range from 1,800-2,200 across countries; 2,000 is conventional. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Approved Drug-Disease Pairings: 1,750 pairings > **NOTE:** > > Unique approved drug-disease pairings (FDA-approved uses, midpoint of 1,500-2,000 range) > > #### Uncertainty Range > > **Technical**: 95% CI: \[1,500 pairings, 2,000 pairings\] • Distribution: Uniform > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 1,500 pairings and 2,000 pairings (±14%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Approved Drug-Disease Pairings > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### All-In Cost per Settlement Transaction: \$0.0003 > **NOTE:** > > All-in cost per settlement (three hundredths of a cent): compute, storage, security audits, and the mostly-AI workforce, amortized per transaction. Anchored to the most similar systems that exist: card networks clear transactions for internal costs in this range, and public blockchain rails settle transfers for fractions of a cent. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$0.0001, \$0.001\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$0.0001 and \$0.001 (±150%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: All-In Cost per Settlement Transaction > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Automated Revenue Service Annual Transaction Volume: 500 billion transactions > **NOTE:** > > Design capacity: annual final-consumption settlements processed by the protocol, set well above current US card-network transaction volume. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Average Life Extension per Beneficiary: 12 years > **NOTE:** > > Average years of life extension per person saved by pharmaceutical interventions. Assumption used to convert life-years saved to approximate lives saved. Based on Lichtenberg’s methodology where life-years are calculated from Years of Life Lost (YLL) reductions. > > #### Uncertainty Range > > **Technical**: 95% CI: \[8 years, 18 years\] • Distribution: Triangular > > **What this means**: There’s significant uncertainty here. The true value likely falls between 8 years and 18 years (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The triangular distribution means values cluster around a most-likely point but can range higher or lower.* > > #### Input Distribution > > Probability Distribution: Average Life Extension per Beneficiary > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Celebrity and Influencer Endorsements: \$15 million > **NOTE:** > > Celebrity and influencer endorsements > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$10.5 million, \$19.5 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$10.5 million and \$19.5 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Celebrity and Influencer Endorsements > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Community Organizing and Ambassador Program Budget: \$30 million > **NOTE:** > > Community organizing and ambassador program budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$21 million, \$39 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$21 million and \$39 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Community Organizing and Ambassador Program Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Contingency Fund for Unexpected Costs: \$50 million > **NOTE:** > > Contingency fund for unexpected costs > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$30 million, \$80 million\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$30 million and \$80 million (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Contingency Fund for Unexpected Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Defense Industry Conversion Program: \$50 million > **NOTE:** > > Defense industry conversion program > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$40 million, \$70 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$40 million and \$70 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Defense Industry Conversion Program > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Budget for Co-Opting Defense Industry Lobbyists: \$50 million > **NOTE:** > > Budget for co-opting defense industry lobbyists > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$35 million, \$65 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$35 million and \$65 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Budget for Co-Opting Defense Industry Lobbyists > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Healthcare Industry Alignment and Partnerships: \$35 million > **NOTE:** > > Healthcare industry alignment and partnerships > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$24.5 million, \$45.5 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$24.5 million and \$45.5 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Healthcare Industry Alignment and Partnerships > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Campaign Operational Infrastructure: \$20 million > **NOTE:** > > Campaign operational infrastructure > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$14 million, \$26 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$14 million and \$26 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Campaign Operational Infrastructure > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### AI-Assisted Legal Work Budget: \$50 million > **NOTE:** > > AI-assisted legal work budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$35 million, \$65 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$35 million and \$65 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: AI-Assisted Legal Work Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Legal Defense Fund: \$20 million > **NOTE:** > > Legal defense fund > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$14 million, \$26 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$14 million and \$26 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Legal Defense Fund > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Legal Drafting and Compliance Work: \$60 million > **NOTE:** > > Legal drafting and compliance work > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$50 million, \$80 million\] • Distribution: Lognormal > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$50 million and \$80 million (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Legal Drafting and Compliance Work > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### EU Lobbying Campaign Budget: \$40 million > **NOTE:** > > EU lobbying campaign budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$28 million, \$52 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$28 million and \$52 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: EU Lobbying Campaign Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### G20 Countries Lobbying Budget: \$35 million > **NOTE:** > > G20 countries lobbying budget > > *Core definition* ### US Lobbying Campaign Budget: \$50 million > **NOTE:** > > US lobbying campaign budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$35 million, \$65 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$35 million and \$65 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: US Lobbying Campaign Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Maximum Mass Media Campaign Budget: \$1 billion > **NOTE:** > > Maximum mass media campaign budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$700 million, \$1.3 billion\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$700 million and \$1.3 billion (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Maximum Mass Media Campaign Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Minimum Mass Media Campaign Budget: \$500 million > **NOTE:** > > Minimum mass media campaign budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$350 million, \$650 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$350 million and \$650 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Minimum Mass Media Campaign Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Opposition Research and Rapid Response: \$25 million > **NOTE:** > > Opposition research and rapid response > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$17.5 million, \$32.5 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$17.5 million and \$32.5 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Opposition Research and Rapid Response > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Phase 1 Campaign Budget: \$200 million > **NOTE:** > > Phase 1 campaign budget (Foundation, Year 1) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$140 million, \$260 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$140 million and \$260 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Phase 1 Campaign Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Phase 2 Campaign Budget: \$500 million > **NOTE:** > > Phase 2 campaign budget (Scale & Momentum, Years 2-3) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$350 million, \$650 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$350 million and \$650 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Phase 2 Campaign Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Pilot Program Testing in Small Countries: \$30 million > **NOTE:** > > Pilot program testing in small countries > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$21 million, \$39 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$21 million and \$39 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Pilot Program Testing in Small Countries > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Voting Platform and Technology Development: \$35 million > **NOTE:** > > Voting platform and technology development > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$25 million, \$50 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$25 million and \$50 million (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Voting Platform and Technology Development > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Regulatory Compliance and Navigation: \$20 million > **NOTE:** > > Regulatory compliance and navigation > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$14 million, \$26 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$14 million and \$26 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Regulatory Compliance and Navigation > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Scaling Preparation and Blueprints: \$30 million > **NOTE:** > > Scaling preparation and blueprints > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$21 million, \$39 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$21 million and \$39 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Scaling Preparation and Blueprints > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Campaign Core Team Staff Budget: \$40 million > **NOTE:** > > Campaign core team staff budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$28 million, \$52 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$28 million and \$52 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Campaign Core Team Staff Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Super PAC Campaign Expenditures: \$30 million > **NOTE:** > > Super PAC campaign expenditures > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$21 million, \$39 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$21 million and \$39 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Super PAC Campaign Expenditures > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Tech Industry Partnerships and Infrastructure: \$25 million > **NOTE:** > > Tech industry partnerships and infrastructure > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$17.5 million, \$32.5 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$17.5 million and \$32.5 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Tech Industry Partnerships and Infrastructure > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Post-Victory Treaty Implementation Support: \$40 million > **NOTE:** > > Post-victory treaty implementation support > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$30 million, \$55 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$30 million and \$55 million (±31%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Post-Victory Treaty Implementation Support > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Viral Marketing Content Creation Budget: \$40 million > **NOTE:** > > Viral marketing content creation budget > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$28 million, \$52 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$28 million and \$52 million (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Viral Marketing Content Creation Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Dismissal Rate: 90% > **NOTE:** > > Probability someone dismisses the idea without engaging (the ‘institutionalization rate’) > > #### Uncertainty Range > > **Technical**: 95% CI: \[80%, 97%\] • Distribution: Beta > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 80% and 97% (±9%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Dismissal Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Effective R: 0.15:1 > **NOTE:** > > Effective reproduction number per cascade generation: fraction of viewers who share (5%) x average forwards per sharer (3). CI spans pessimistic (2% x 2 = 0.04) to optimistic (10% x 8 = 0.80). > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.04:1, 0.8:1\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.04:1 and 0.8:1 (±253%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Effective R > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Model Horizon: 3 years > **NOTE:** > > Conservative upper bound for cascade propagation (social media cascades propagate in weeks; 3 years allows for slower channels and multiple cascade waves) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Implementer Orbit Size: 1,000 people > **NOTE:** > > Information-orbit size per implementer: people whose recommendation would reach them (staff, advisors, active social media feeds, professional contacts). Lower bound: Dunbar’s 150; upper: corporate C-suite intake funnel. > > #### Uncertainty Range > > **Technical**: 95% CI: \[150 people, 5,000 people\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 150 people and 5,000 people (±242%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Implementer Orbit Size > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Initial Audience: 50,000 people > **NOTE:** > > Conservative initial audience size (readers, website visitors, conference attendees) > > #### Uncertainty Range > > **Technical**: 95% CI: \[10,000 people, 500 thousand people\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 10,000 people and 500 thousand people (±490%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Initial Audience > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### World Leader Count: 195 countries > **NOTE:** > > Number of sovereign heads of state/government > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Childhood Vaccination Cost per DALY (Estimated): \$30 > **NOTE:** > > Estimated cost per DALY for US childhood vaccination programs. Note: US cost-effectiveness studies primarily use cost per QALY (Quality-Adjusted Life Year) rather than cost per DALY. This estimate is derived from program costs and benefits for comparison purposes only. > > *Core definition* ### Concentrated Interest Sector Market Cap: \$5 trillion > **NOTE:** > > Estimated combined market capitalization of concentrated interest opposition (defense, fossil fuel, etc.) > > *Core definition* ### Corporate Damages Drugs Never Developed Deaths: 300 million deaths > **NOTE:** > > Aggressive prosecutor pleading estimate for deaths from drugs never developed because regulatory cost and misallocated trial capacity suppressed development. Based on the Humanity v. Government exclusion note that drugs never developed may double or triple Count Two; this uses the high end of that pleading range. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Court of Humanity Build Cost: \$30 million > **NOTE:** > > One-time cost to build the Court of Humanity. Range reflects digital-first institutional design (no physical courtrooms, no detention, AI-assisted evidence triage, cryptographic provenance, stratified random jury infrastructure). Lower bound: minimal viable institution. Upper bound: fully-staffed initial operations, roughly 27% of one year of ICC operating budget (the ICC funds physical courtrooms, detention, and 425+ staff). > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$10 million, \$50 million\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$10 million and \$50 million (±67%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Court of Humanity Build Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Cumulative Military Spending (Fed Era): \$170 trillion > **NOTE:** > > Cumulative global military spending since 1913 (Fed era) in constant 2024 dollars. Built from: SIPRI 1988-2024 (\$65-72T), Cold War 1946-1987 (\$50-70T reconstructed), WWI+WWII+interwar (\$33T from Harrison). Range: \$150-190T. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Decentralized Census Bureau Annual Operating Cost: \$15 million > **NOTE:** > > All-in annual operating cost of the sensor array: continuously computing, cross-checking, and integrity-auditing the statistics the machine steers by (median after-tax income aggregates, healthy-life-expectancy estimation from dFDA outcome data, data-poisoning detection). Sized as a mid-sized analytics operation; roughly 1% of what the decennial census costs per year amortized. The citizen count itself is a free byproduct of the identity layer. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$5 million, \$40 million\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$5 million and \$40 million (±117%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Decentralized Census Bureau Annual Operating Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Percentage of Budget Military Sector Keeps Under 1% Treaty: 99% > **NOTE:** > > Percentage of budget the military sector keeps under 1% treaty > > *Core definition* ### Acquisition Premium Multiplier: 1.8x > **NOTE:** > > Planning multiplier for acquisition premium, execution friction, disclosure timing, and large-position accumulation costs in a counsel-led control campaign > > #### Uncertainty Range > > **Technical**: 95% CI: \[1.5x, 3x\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between 1.5x and 3x (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Acquisition Premium Multiplier > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Control Fraction: 0.501:1 > **NOTE:** > > Fraction of shares required for board control (50% + 1 share) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Destructive Economy Base Year: 2025 > **NOTE:** > > Base year for destructive economy projections. All threshold timelines are measured from this year. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Reference Annual Pragmatic Trial Funding: \$21.8 billion > **NOTE:** > > Reference annual funding level used for direct-funding comparisons. Source-agnostic: funds could come from treaty reallocation, philanthropy, or public appropriation, and are modeled as funding available for pragmatic clinical trials rather than funding owed to any one organization. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Years to Reach Full Pragmatic Trial Platform Adoption: 5 years > **NOTE:** > > Years to reach full pragmatic trial platform adoption > > *Core definition* ### Pragmatic Trial Platform Core Framework Annual OPEX: \$18.9 million > **NOTE:** > > Pragmatic trial platform core framework annual opex (midpoint of \$11-26.5M) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$11 million, \$26.5 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$11 million and \$26.5 million (±41%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Platform Core Framework Annual OPEX > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Pragmatic Trial Platform Core Framework Build Cost: \$40 million > **NOTE:** > > Pragmatic trial platform core framework build cost > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$25 million, \$65 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$25 million and \$65 million (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Platform Core Framework Build Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Stage 1 Observational Analysis Cost per Patient: \$0.1 > **NOTE:** > > Order-of-magnitude estimate for Stage 1 observational signal detection (PIS calculation). Validated by FDA Sentinel benchmark (~\$1/patient/year for similar drug safety analysis at 100M+ scale). True cost varies with scale and complexity; exact value less important than order-of-magnitude difference vs pragmatic trials (~\$500-929/patient) and traditional Phase 3 (~\$41,000/patient). > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$0.03, \$1\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$0.03 and \$1 (±485%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Stage 1 Observational Analysis Cost per Patient > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Pragmatic Trial Platform Community Support Costs: \$2 million > **NOTE:** > > Pragmatic trial platform community support costs > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1 million, \$3 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$1 million and \$3 million (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Platform Community Support Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Pragmatic Trial Platform Infrastructure Costs: \$8 million > **NOTE:** > > Pragmatic trial platform infrastructure costs (cloud, security) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$5 million, \$12 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$5 million and \$12 million (±44%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Platform Infrastructure Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Pragmatic Trial Platform Maintenance Costs: \$15 million > **NOTE:** > > Pragmatic trial platform maintenance costs > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$10 million, \$22 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$10 million and \$22 million (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Platform Maintenance Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Pragmatic Trial Platform Regulatory Coordination Costs: \$5 million > **NOTE:** > > Pragmatic trial platform regulatory coordination costs > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$3 million, \$8 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$3 million and \$8 million (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Platform Regulatory Coordination Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Pragmatic Trial Platform Staff Costs: \$10 million > **NOTE:** > > Pragmatic trial platform staff costs (minimal, AI-assisted) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$7 million, \$15 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$7 million and \$15 million (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Pragmatic Trial Platform Staff Costs > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Target Pragmatic Trial Cost per Patient: \$1,000 > **NOTE:** > > Target pragmatic trial cost per patient in USD > > *Core definition* ### Pragmatic Trial Platform One-Time Build Cost (Maximum): \$46 million > **NOTE:** > > Pragmatic trial platform one-time build cost (high estimate) > > *Core definition* ### DIH Broader Initiatives Annual OPEX: \$21.1 million > **NOTE:** > > DIH broader initiatives annual opex (medium case) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$14 million, \$32 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$14 million and \$32 million (±43%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: DIH Broader Initiatives Annual OPEX > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### DIH Broader Initiatives Upfront Cost: \$230 million > **NOTE:** > > DIH broader initiatives upfront cost (medium case) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$150 million, \$350 million\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$150 million and \$350 million (±44%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: DIH Broader Initiatives Upfront Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### EOS Stage 1 HII Terminal Value: \$330 million > **NOTE:** > > Modeled terminal stage value if the first EOS governance campaign redirects Huntington Ingalls Industries lobbying and creates a credible activist-governance proof point. This is not probability-weighted expected value; the calculator applies probability separately. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$100 million, \$1 billion\] • Distribution: Lognormal (SE: \$250 million) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$100 million and \$1 billion (±136%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: EOS Stage 1 HII Terminal Value > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### EOS Stage 2 Defense-Primes Terminal Value: \$7 billion > **NOTE:** > > Modeled terminal stage value if EOS extends the governance campaign from HII to the major U.S. military prime contractors and redirects sector lobbying toward shareholder-positive policy. This is not probability-weighted expected value; the calculator applies probability separately. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1.5 billion, \$30 billion\] • Distribution: Lognormal (SE: \$6 billion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$1.5 billion and \$30 billion (±204%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: EOS Stage 2 Defense-Primes Terminal Value > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### EOS Stage 3 Lobbying-Sectors Terminal Value: \$452 billion > **NOTE:** > > Modeled terminal stage value if EOS applies the same governance pressure across major lobbying-heavy sectors whose current policy positions destroy shareholder value. This is not probability-weighted expected value; the calculator applies probability separately. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$100 billion, \$2 trillion\] • Distribution: Lognormal (SE: \$400 billion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$100 billion and \$2 trillion (±210%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: EOS Stage 3 Lobbying-Sectors Terminal Value > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Eventually Avoidable DALY Percentage: 92.6% > **NOTE:** > > Percentage of DALYs that are eventually avoidable with sufficient biomedical research. Uses same methodology as EVENTUALLY_AVOIDABLE_DEATH_PCT. Most non-fatal chronic conditions (arthritis, depression, chronic pain) are also addressable through research, so the percentage is similar to deaths. > > #### Uncertainty Range > > **Technical**: 95% CI: \[50%, 98%\] • Distribution: Beta > > **What this means**: There’s significant uncertainty here. The true value likely falls between 50% and 98% (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Eventually Avoidable DALY Percentage > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Eventually Avoidable Death Percentage: 92.6% > **NOTE:** > > Percentage of deaths that are eventually avoidable with sufficient biomedical research and technological advancement. Central estimate ~92% based on ~7.9% fundamentally unavoidable (primarily accidents). Wide uncertainty reflects debate over: (1) aging as addressable vs. fundamental, (2) asymptotic difficulty of last diseases, (3) multifactorial disease complexity. > > #### Uncertainty Range > > **Technical**: 95% CI: \[50%, 98%\] • Distribution: Beta > > **What this means**: There’s significant uncertainty here. The true value likely falls between 50% and 98% (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Eventually Avoidable Death Percentage > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Minimum Investment for Family Offices: \$5 million > **NOTE:** > > Minimum investment for family offices > > *Core definition* ### Fundamentally Unavoidable Death Percentage: 7.37% > **NOTE:** > > Percentage of deaths that are fundamentally unavoidable even with perfect biotechnology (primarily accidents). Calculated as Σ(disease_burden × (1 - max_cure_potential)) across all disease categories. > > *Core definition* ### Baseline Global GDP Growth Rate: 2.5% > **NOTE:** > > Status-quo baseline annual global GDP growth rate. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Activation Reward per Verified Participant: \$5 > **NOTE:** > > Planning midpoint for the direct cash incentive required to make a successful verified recruit materially worth sharing at global scale. Intended as a research-backed blended reward across referrer and recruit, not as the long-dated PRIZE claim value. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$2, \$10\] • Distribution: Normal (SE: \$1.5) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$2 and \$10 (±80%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Activation Reward per Verified Participant > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Global Coordination Platform and Operations Cost: \$4 billion > **NOTE:** > > Fixed cost to run a global activation campaign toward majority-of-humanity participation: platform buildout, localization, customer support, compliance, payout operations, fraud response, and regional launch infrastructure. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$2 billion, \$8 billion\] • Distribution: Normal (SE: \$1.5 billion) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$2 billion and \$8 billion (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Global Coordination Platform and Operations Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Verification and Payment Cost per Participant: \$1.5 > **NOTE:** > > Planning midpoint for non-reward variable cost per successful verified participant: identity verification, payment rails, fraud checks, support, and completion friction. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1, \$3\] • Distribution: Normal (SE: \$0.5) > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$1 and \$3 (±67%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Verification and Payment Cost per Participant > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Effective Tax Rate on Median Earner: 25% > **NOTE:** > > Effective combined tax rate (direct plus indirect) on the global median earner. Applied IDENTICALLY across all scenarios so it shifts levels without affecting any cross-scenario comparison: there is no pro-treaty thumb on this scale. The ‘after-tax’ in the metric name is honesty about levels, not a modeling lever. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Median Share Erosion Rate (Annual): 0% > **NOTE:** > > Annual erosion of the median’s share of mean income (positive = the middle person’s slice shrinks). Best guess ZERO: the GLOBAL median-to-mean ratio ROSE 1990-2019 via between-country convergence (a billion people in Asia got real jobs), so the measured global sign favors the median; the US wedge is the extreme, not the world. The range spans continued convergence (-0.5%/yr, the share keeps growing) to the US-extreme construction applied globally (+0.78%/yr; EPI: productivity +90.2% vs typical pay +33.0%, 1979-2025). Applied IDENTICALLY to the status-quo and treaty branches, so it cancels out of every treaty-vs-current multiplier; Wishonia excludes it (the wishocratic mechanism exists to stop share capture). v1 set this to 0.5%/yr shrinkage as skeptic armor; that was deliberate conservatism, replaced by deliberate accuracy plus deliberate uncertainty. > > #### Uncertainty Range > > **Technical**: 95% CI: \[-0.5%, 0.78%\] • Distribution: Normal > > **What this means**: We’re quite confident in this estimate. The true value likely falls between -0.5% and 0.78% (±0%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > *Core definition* ### Global-to-US Political Cost Ratio: 5:1 > **NOTE:** > > Ratio of global to US political reform costs. Based on discretionary spending ratio (~9x) discounted by ~50% for less transparent/expensive non-US political systems. Range 3-8 reflects uncertainty about non-US political dynamics and hidden influence channels. > > #### Uncertainty Range > > **Technical**: 95% CI: \[3:1, 8:1\] • Distribution: Lognormal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 3:1 and 8:1 (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Global-to-US Political Cost Ratio > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### HALE Longevity Realization Share (Year 15): 30% > **NOTE:** > > Share of longer-run life-extension gains that have plausibly materialized into healthy years by year 15. Calibrated to the repo’s conservative disease-eradication helper, which implies that only a minority of eventual longevity gains are realized within the first 15 years even under rapid research acceleration. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Human Laughs per Day (Average Adult): 17 laughs > **NOTE:** > > Folkloric estimate of average adult laughter rate. Widely cited as approximately 17 laughs per day; primary sources are diffuse and the true value varies enormously across individuals, ages, and cultures. Used here as a planning constant for the quantitative-case argument in the shirt paper. Children laugh substantially more (~10x), so the value here is conservative for blended human population. > > #### Uncertainty Range > > **Technical**: 95% CI: \[5 laughs, 50 laughs\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 5 laughs and 50 laughs (±132%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Human Laughs per Day (Average Adult) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### IAB Mechanism Annual Cost (High Estimate): \$750 million > **NOTE:** > > Estimated annual cost of the IAB mechanism (high-end estimate including regulatory defense) > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$160 million, \$750 million\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$160 million and \$750 million (±39%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: IAB Mechanism Annual Cost (High Estimate) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### IAB Political Incentive Funding Percentage: 10% > **NOTE:** > > Percentage of treaty funding allocated to Incentive Alignment Bond mechanism for political incentives (independent expenditures/PACs, post-office fellowships, Public Good Score infrastructure) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Priced-Entry Annual Volume: 2 million entrants > **NOTE:** > > Modeled annual legal entries under uncapped priced entry. Anchored to the most similar thing that already happened: the 2021-2026 US immigration surge that the CBO measured ran at roughly this rate, uninvited and unpriced. > > #### Uncertainty Range > > **Technical**: 95% CI: \[1 million entrants, 5 million entrants\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 1 million entrants and 5 million entrants (±100%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Priced-Entry Annual Volume > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Activist Stake Fraction: 0.05:1 > **NOTE:** > > Activist equity stake assumed sufficient to win board influence when combined with index-fund votes, rather than buying outright control. Grounded in activist-investing precedent: Engine No. 1 won three ExxonMobil board seats with 0.02%, and Carl Icahn typically operates with 1-10% positions. 5% is a deliberately conservative central case; the real floor is far lower, because institutional investors hold 70-85% of every prime (the Big Three index managers alone hold roughly 20-30%, per the primes’ own proxy statements) and supply the votes once shown the financial case. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.01:1, 0.1:1\] > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.01:1 and 0.1:1 (±90%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Activist Stake Fraction > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Minimum Investment for Institutional Investors: \$10 million > **NOTE:** > > Minimum investment for institutional investors > > *Core definition* ### Leaded Gasoline Era Duration: 73 years > **NOTE:** > > Duration of the US leaded-gasoline era: first commercial TEL sale (February 1923) to the completed on-road ban (January 1, 1996). > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Maximum Bond Investment for Lobbyist Incentives: \$20 million > **NOTE:** > > Maximum bond investment for lobbyist incentives > > *Core definition* ### P(Success \| Court of Humanity Funded): 0.1:1 > **NOTE:** > > Probability of treaty passage given Court of Humanity operational. Lowest direct causal link: court rulings are non-binding but create political pressure. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.03:1, 0.25:1\] > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.03:1 and 0.25:1 (±110%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: P(Success \| Court of Humanity Funded) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### P(Success \| dFDA Funded): 0.7:1 > **NOTE:** > > Probability that a funded dFDA produces sufficient cures to create political momentum for the treaty. High because clinical trials mechanically produce treatments; the platform reduces cost per trial. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.4:1, 0.9:1\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between 0.4:1 and 0.9:1 (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: P(Success \| dFDA Funded) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### IAB Admin Cost Rate: 0.05:1 > **NOTE:** > > Annual administrative cost of IABs as fraction of principal (legal, compliance, reporting). The principal itself is investor capital returned with interest, not EOS expenditure. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### P(Success \| IABs Funded): 0.6:1 > **NOTE:** > > Probability of treaty passage given full IAB issuance. High because lobbying is a proven mechanism and IABs align politician incentives directly. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.3:1, 0.85:1\] > > **What this means**: There’s significant uncertainty here. The true value likely falls between 0.3:1 and 0.85:1 (±46%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: P(Success \| IABs Funded) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### P(Success \| Loving Takeover Funded): 0.95:1 > **NOTE:** > > Probability of treaty passage given full funding of the Loving Takeover (mechanical: money buys shares, shares buy board control, board redirects lobbying) > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.8:1, 0.99:1\] > > **What this means**: We’re quite confident in this estimate. The true value likely falls between 0.8:1 and 0.99:1 (±10%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: P(Success \| Loving Takeover Funded) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Mechanism Opportunity Cost Rate: 0.02:1 > **NOTE:** > > Annual opportunity cost rate for capital deployment mechanisms (forgone excess return vs market). Defense stocks historically return near-market, so excess opportunity cost is low. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0:1, 0.05:1\] • Distribution: Fixed > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0:1 and 0.05:1 (±125%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The fixed distribution means values follow a specific statistical pattern.* > > *Core definition* ### P(Success \| Referendum Funded): 0.3:1 > **NOTE:** > > Probability of treaty passage given a successful global referendum demonstrating majority support. Non-binding but creates political pressure. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.1:1, 0.55:1\] > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.1:1 and 0.55:1 (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: P(Success \| Referendum Funded) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### P(Success \| Treaty Campaign Funded): 0.4:1 > **NOTE:** > > Probability of treaty passage given full funding of the lobbying + referendum campaign (\$1B). Depends on political will after lobbying and public pressure. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.15:1, 0.7:1\] > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.15:1 and 0.7:1 (±69%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: P(Success \| Treaty Campaign Funded) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Median Income Relief from Full Disease Cure: 10% > **NOTE:** > > Fraction by which curing ALL currently untreatable disease would raise the median person’s consumable income, beyond the mean-income effect already inside the GDP trajectories. Mechanism: out-of-pocket health spending and sick-day wage losses fall disproportionately on people at and below the median (WHO: about half a billion people pushed into extreme poverty by health costs, roughly 2 billion facing catastrophic or impoverishing health spending). Scaled by each scenario’s cure fraction, so partial cures give partial relief. The range reflects genuine uncertainty about how much of that burden the queue’s early cures relieve. > > #### Uncertainty Range > > **Technical**: 95% CI: \[5%, 15%\] • Distribution: Normal > > **What this means**: There’s significant uncertainty here. The true value likely falls between 5% and 15% (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Median Income Relief from Full Disease Cure > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### GDP Growth Boost at 30% Military Reallocation: 5.5% > **NOTE:** > > Historical calibration target: 30% military reallocation maps to ~5.5 percentage points annual GDP growth boost. > > #### Uncertainty Range > > **Technical**: 95% CI: \[3.5%, 7.5%\] • Distribution: Normal (SE: 1%) > > **What this means**: There’s significant uncertainty here. The true value likely falls between 3.5% and 7.5% (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: GDP Growth Boost at 30% Military Reallocation > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Standard Discount Rate for NPV Analysis: 3% > **NOTE:** > > Standard discount rate for NPV analysis (3% annual, social discount rate) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Standard Time Horizon for NPV Analysis: 10 years > **NOTE:** > > Standard time horizon for NPV analysis > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Peace Dividend Conflict Elasticity: 1:1 > **NOTE:** > > Conflict reduction elasticity: how much conflict costs decrease per 1% military spending cut. ε=0: no effect (spending cuts don’t reduce conflict). ε=0.5: moderate linkage (conservative). ε=1.0: proportional (baseline assumption). ε\>1.0: shared enemy amplification (redirecting to disease creates unity). > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.25:1, 1.5:1\] • Distribution: Beta > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.25:1 and 1.5:1 (±62%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Peace Dividend Conflict Elasticity > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Direct Fiscal Savings from 1% Military Spending Reduction: \$27.2 billion > **NOTE:** > > Direct fiscal savings from 1% military spending reduction (high confidence) > > *Core definition* ### Assumed Long-Term Real Return for Investment Compounding: 13% > **NOTE:** > > Illustrative long-term real return rate used to compound the peace dividend stream into a future value at year 80. 13% is the approximate long-run real CAGR of the Nasdaq-100 index since its 1985 inception: accessible via passive ETFs (QQQ), grounded in 40+ years of data, and does not require assuming unique skill (Buffett) or access (VC/private markets). Chosen as more optimistic than 60/40 portfolio (5%) or S&P 500 (7%) because growth-tilted founder-led companies have historically outperformed the broad market. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Pharma Phase 2/3 Cost Barrier Per Drug: \$1.56 billion > **NOTE:** > > Average Phase 2/3 efficacy testing cost per drug that pharma must fund (~60% of total drug development cost) > > #### Uncertainty Range > > **Technical**: Distribution: Normal (SE: \$200 million) > > #### Input Distribution > > Probability Distribution: Pharma Phase 2/3 Cost Barrier Per Drug > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Prize Pool Participation Rate: 1% > **NOTE:** > > Fraction of global investable financial assets that flow into the prize pool. 1% central estimate parallels the 1% Treaty ask: 1% of your weapons money, 1% of your savings. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.1%, 10%\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.1% and 10% (±495%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Prize Pool Participation Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### QALYs per COVID Death Averted: 5 QALYs/death > **NOTE:** > > Average QALYs gained per COVID death averted. Conservative estimate reflecting older age distribution of COVID mortality. See confidence_interval for range. > > #### Uncertainty Range > > **Technical**: 95% CI: \[3 QALYs/death, 10 QALYs/death\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 3 QALYs/death and 10 QALYs/death (±70%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: QALYs per COVID Death Averted > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### R&D Spillover Multiplier: 2x > **NOTE:** > > R&D spillover multiplier: each \$1 in directed medical research produces \$2 in adjacent sector GDP growth (biotech, AI, computing, materials science, manufacturing). Conservative estimate; military R&D spillover produced the internet, GPS, jet engines. Medical R&D spillover already produced CRISPR, mRNA platforms, AI protein folding. > > #### Uncertainty Range > > **Technical**: 95% CI: \[1.5x, 2.5x\] • Distribution: Normal (SE: 0.25x) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 1.5x and 2.5x (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: R&D Spillover Multiplier > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Safe Compounds Available for Testing: 9,500 compounds > **NOTE:** > > Total safe compounds available for repurposing (FDA-approved + GRAS substances, midpoint of 7,000-12,000 range) > > #### Uncertainty Range > > **Technical**: 95% CI: \[7,000 compounds, 12,000 compounds\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between 7,000 compounds and 12,000 compounds (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Safe Compounds Available for Testing > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Scale Compression Factor: -2.5% > **NOTE:** > > Diminishing-returns drag as the venture market expands ~15x (current global VC ~\$300B/yr; Prize Fund deploys ~\$4.7T/yr). More capital chasing deals compresses returns. Partially offset by market expansion (every viable idea gets funded, oligopolies face real competition). Point estimate is moderate; CI spans optimistic to pessimistic. > > #### Uncertainty Range > > **Technical**: 95% CI: \[-5%, -1%\] • Distribution: Normal > > **What this means**: This estimate is highly uncertain. The true value likely falls between -5% and -1% (±80%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Scale Compression Factor > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Equity Capture Share of Public Value: 20% > **NOTE:** > > Share of a large public-value gain assumed to be capitalized into listed equities through lower conflict risk, lower supply-chain risk, and higher expected real output. This is a valuation scenario, not an observed pass-through estimate. > > #### Uncertainty Range > > **Technical**: 95% CI: \[5%, 40%\] • Distribution: Beta > > **What this means**: This estimate is highly uncertain. The true value likely falls between 5% and 40% (±88%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Equity Capture Share of Public Value > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Observer Belief Change Rate: 0.5% > **NOTE:** > > Fraction of observers (non-target readers) who durably update their belief after reading a correction exchange. Roozenbeek et al. (2022) supports the general prebunking mechanism, and Pennycook et al. (2021) supports accuracy prompts. This parameter is lower than those intervention effects because a public reply is shorter, unsolicited, and usually viewed while skimming. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.05%, 3%\] • Distribution: Beta > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.05% and 3% (±295%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Observer Belief Change Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Observer Multiplier: 20 people per post > **NOTE:** > > Average unique readers of a correction reply besides the target. Munger (2017) is the closest field evidence for public bot replies, while Vosoughi et al. (2018) supports the broader claim that false-news cascades can reach large audiences. Neither paper directly measures observers per correction reply, so this remains a low-confidence reach assumption. > > #### Uncertainty Range > > **Technical**: 95% CI: \[2 people per post, 200 people per post\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 2 people per post and 200 people per post (±495%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Observer Multiplier > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Special Education Outcome Attribution Fraction: 0.01% > **NOTE:** > > Fraction of a modeled public-policy outcome attributable to outbound Special Education running for one year. This is a scenario assumption, not an empirical estimate. The mechanical model produces modeled belief updates, but the conversion from belief updates to treaty passage depends on targeting, repeated exposure, elite pickup, platform enforcement, and whether the bot reaches marginal decision-makers. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.0001%, 1%\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.0001% and 1% (±5000%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Special Education Outcome Attribution Fraction > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### SE Bot Platform Overhead Per Post: \$0.002 > **NOTE:** > > Non-model marginal overhead per attempted correction: search, ranking, duplicate filtering, policy checks, posting, retries, and failed attempts. This is a planning assumption because platform access terms and rate limits vary by platform. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$0.0005, \$0.02\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$0.0005 and \$0.02 (±488%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: SE Bot Platform Overhead Per Post > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Reference AUM for SE Bot Portfolio Case: \$1 trillion > **NOTE:** > > Reference institutional portfolio size for the trillion-dollar AUM sensitivity case. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Relevant Posts Per Day (Global): 100 thousand posts/day > **NOTE:** > > Correctable posts per day across all platforms globally on defense spending, war, and disease funding topics. Defined as posts from accounts with 100+ followers containing an identifiable logical fallacy (not merely mentioning the topic). This is a planning assumption, not a platform-reported count, and should be replaced with live measurement before making an operating budget. > > #### Uncertainty Range > > **Technical**: 95% CI: \[5,000 posts/day, 1 million posts/day\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 5,000 posts/day and 1 million posts/day (±498%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Relevant Posts Per Day (Global) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Target Belief Change Rate: 2% > **NOTE:** > > Fraction of reply targets who durably update their stated belief after receiving a correction. Wood and Porter (2019) support the narrower claim that corrections can reduce false beliefs without routine factual backfire. This 2% value is a calibrated durable-change assumption for public social replies, reduced from lab and survey settings because the message is unsolicited and political. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.2%, 10%\] • Distribution: Beta > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.2% and 10% (±245%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Target Belief Change Rate > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Sharing Time: 0.5 minutes > **NOTE:** > > Time to copy, paste, and send the recruitment message. 30 seconds. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Shirt Cascade Probability Given Seed: 25% > **NOTE:** > > Subjective probability that the seed program triggers a viral cascade to majority-of-humanity participation, conditional on the seed threshold being met. Deliberately conservative: even at 25% the expected-value math beats every conventional foundation intervention. Sensitivity range covers skeptic and base-case scenarios. > > #### Uncertainty Range > > **Technical**: 95% CI: \[5%, 60%\] • Distribution: Beta > > **What this means**: This estimate is highly uncertain. The true value likely falls between 5% and 60% (±110%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The beta distribution means values are bounded and can skew toward one end.* > > #### Input Distribution > > Probability Distribution: Shirt Cascade Probability Given Seed > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Shirt Seed Cost per Wearer: \$50 > **NOTE:** > > Blended cost per seed wearer: printed shirt, small honorarium, and campaign admin. Includes a mix of professionally-printed shirts for influencers and bulk-print runs for university chapters, athletes, and micro-celebrities. Excludes top-tier celebrity placements (handled through separate sponsorship; see Getting Started celebrity layer). > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$10, \$200\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$10 and \$200 (±190%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Shirt Seed Cost per Wearer > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Shirt Seed Wearers Threshold: 1 million of people > **NOTE:** > > Planning estimate for the number of visible humans who must wear the End-War-and-Disease message before the social-proof barrier breaks and imitation becomes spontaneous. Sized between the ALS Ice Bucket (~17M participants) and Livestrong (~87M bracelets) cascade trigger points, discounted for the lower-friction permanent-marker version. > > #### Uncertainty Range > > **Technical**: 95% CI: \[100 thousand of people, 5 million of people\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 100 thousand of people and 5 million of people (±245%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Shirt Seed Wearers Threshold > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Shirt Wearing Friction Cost: \$5 > **NOTE:** > > Perceived social friction cost of wearing a political message in public, expressed in dollar-equivalent terms. Sets the minimum per-wearer expected Earth Optimization Prize payout required to make participation rational at scale. Anchored to the GLOBAL_COORDINATION_ACTIVATION_REWARD_PER_VERIFIED_PARTICIPANT midpoint, which serves the same role for the verified-vote action. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$1, \$25\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$1 and \$25 (±240%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Shirt Wearing Friction Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Universal Right to Try with Evidence Implementation Cost: \$65 million > **NOTE:** > > Total implementation cost of adopting Universal Right to Try with Evidence in all 50 states: a central \$15 million campaign estimate covering legislation or amendment in all 50 states plus \$50 million for the shared registry’s first ten years. The model bill requires participating centers to fund continued registry operation after year ten. This launch-cost numerator excludes patient or payer spending on treatment delivery, trial-site services, and permitted study costs. The wide interval represents campaign and infrastructure cost uncertainty without separate scenario parameters. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$25 million, \$200 million\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$25 million and \$200 million (±135%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Universal Right to Try with Evidence Implementation Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Universal Right to Try with Evidence Treatment Discovery Multiplier: 5.48x > **NOTE:** > > Conditional multiplier on the worldwide first-treatment discovery rate after all 50 states adopt and a mature pooled pragmatic-trial system operates under applicable federal authorization. The 5.48x central calibration reproduces the prior model’s 82.2 versus 15 first treatments per year; it is an assumption, not an observed effect estimate. This single input incorporates patient or payer funding of treatment delivery, trial-site services, and permitted study costs, newly viable post-Phase-1 treatment-condition pairs, evaluable protocol quality, candidate supply, and scientific success. Its range describes productivity of an operating system, not the separate probability that advocacy achieves full adoption and implementation. > > #### Uncertainty Range > > **Technical**: 95% CI: \[1.1x, 15x\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 1.1x and 15x (±127%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Universal Right to Try with Evidence Treatment Discovery Multiplier > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Tested Drug-Disease Relationships: 32,500 relationships > **NOTE:** > > Estimated drug-disease relationships actually tested (approved uses + repurposed + failed trials, midpoint of 15,000-50,000 range) > > #### Uncertainty Range > > **Technical**: 95% CI: \[15,000 relationships, 50,000 relationships\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 15,000 relationships and 50,000 relationships (±54%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Tested Drug-Disease Relationships > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Transparent Securities Commission Annual Operating Cost: \$20 million > **NOTE:** > > All-in annual operating cost of the Transparent Securities Commission: standardized disclosure schema plus continuous fraud-pattern detection across every issuer on the ledger. Anchored to the most similar existing systems: the real-time fraud-detection operations card networks already run at market scale. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$8 million, \$50 million\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$8 million and \$50 million (±105%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Transparent Securities Commission Annual Operating Cost > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance: \$650 million > **NOTE:** > > Political lobbying campaign: direct lobbying (US/EU/G20), Super PACs, opposition research, staff, legal/compliance. Budget exceeds combined pharma (\$300M/year) and military-industrial complex (\$150M/year) lobbying to ensure competitive positioning. Referendum relies on grassroots mobilization and earned media, while lobbying requires matching or exceeding opposition spending for political viability. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$325 million, \$1.3 billion\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$325 million and \$1.3 billion (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Reserve Fund / Contingency Buffer: \$100 million > **NOTE:** > > Reserve fund / contingency buffer (10% of total campaign cost). Using industry standard 10% for complex campaigns with potential for unforeseen legal challenges, opposition response, or regulatory delays. Conservative lower bound of \$20M (2%) reflects transparent budget allocation and predictable referendum/lobbying costs. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$20 million, \$150 million\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$20 million and \$150 million (±65%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Reserve Fund / Contingency Buffer > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Treaty Campaign Duration: 4 years > **NOTE:** > > Treaty campaign duration (3-5 year range, using midpoint) > > #### Uncertainty Range > > **Technical**: 95% CI: \[3 years, 5 years\] • Distribution: Triangular > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 3 years and 5 years (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The triangular distribution means values cluster around a most-likely point but can range higher or lower.* > > #### Input Distribution > > Probability Distribution: Treaty Campaign Duration > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Viral Referendum Budget: \$250 million > **NOTE:** > > Viral referendum budget for 280M verified votes (base: \$250M realistic with \$0.50/vote avg, range: \$150M optimistic \$0.20/vote to \$410M worst-case \$1.05/vote). Components: platform (\$35M), verification infrastructure (280M × friction × \$0.18-0.20), tiered referral payments (varies by virality and marginal cost curve per diffusion theory), marketing seed (\$5-15M). Based on PayPal referral economics (\$18-36 inflation-adjusted) and biometric verification pricing (\$0.15-0.25 at 300M+ scale). > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$150 million, \$410 million\] > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$150 million and \$410 million (±52%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > #### Input Distribution > > Probability Distribution: Viral Referendum Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Treaty Health Recovery Annualization Horizon: 20 years > **NOTE:** > > Annualization horizon for the treaty health recovery GDP-drag term. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Treaty Ratchet Terminal Redirect Share: 10% > **NOTE:** > > Terminal share of military spending redirected under the IAB-ratchet take-hold schedule (1% for 3 years, 2% for 4, 5% for 5, terminal thereafter). The single ratchet knob: every treaty-trajectory parameter binds it, so setting it to 0.01 switches ratcheting off everywhere (the treaty stays at its initial 1% forever) and every treaty number degrades to its flat-1% bound. Uncertainty spans never-expands (0.01, the 95% lower bound) to overshooting the schedule (0.19): expansion is driven by bondholder lobbying incentives, which do not stop at 10% if they work at all. > > #### Uncertainty Range > > **Technical**: 95% CI: \[1%, 19%\] • Distribution: Normal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 1% and 19% (±90%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Treaty Ratchet Terminal Redirect Share > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### 1% Reduction in Military Spending/War Costs from Treaty: 1% > **NOTE:** > > 1% reduction in military spending/war costs from treaty > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Trial-Relevant Diseases: 1,000 diseases > **NOTE:** > > Consolidated count of trial-relevant diseases worth targeting (after grouping ICD-10 codes) > > #### Uncertainty Range > > **Technical**: 95% CI: \[800 diseases, 1,200 diseases\] • Distribution: Uniform > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 800 diseases and 1,200 diseases (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Trial-Relevant Diseases > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Universal Security Administration All-In Annual Cost per Citizen: \$0.3 > **NOTE:** > > All-in annual cost per citizen served: sybil-resistant identity, 365 daily deposits, and the mostly-AI workforce. Anchored to the most similar system that exists: India’s national biometric identity system serves 1.4 billion people at costs in this range per person per year, and payment rails move deposits for fractions of a cent. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$0.1, \$1\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$0.1 and \$1 (±150%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Universal Security Administration All-In Annual Cost per Citizen > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### US Congress Members: 535 members > **NOTE:** > > Total members of US Congress (100 senators + 435 representatives) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### First-Principles Defense: Coast Guard: \$14 billion > **NOTE:** > > US Coast Guard (actual budget ~\$14B). Maritime homeland security and coastal defense. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$12 billion, \$16 billion\] • Distribution: Normal (SE: \$1.5 billion) > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between \$12 billion and \$16 billion (±14%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: First-Principles Defense: Coast Guard > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### First-Principles Defense: Cyber Defense: \$15 billion > **NOTE:** > > Cyber defense of critical infrastructure: the primary modern attack vector that crosses oceans in milliseconds and is not stopped by geography. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$10 billion, \$25 billion\] • Distribution: Normal (SE: \$4 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$10 billion and \$25 billion (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: First-Principles Defense: Cyber Defense > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### First-Principles Defense: Homeland Air & Missile Defense: \$35 billion > **NOTE:** > > Continental air and missile defense plus early warning (NORAD, ground-based midcourse defense, interceptor aircraft). Defensive only; no expeditionary or power-projection air power. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$25 billion, \$50 billion\] • Distribution: Normal (SE: \$7 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$25 billion and \$50 billion (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: First-Principles Defense: Homeland Air & Missile Defense > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### First-Principles Defense: Mobilization Hedge: \$60 billion > **NOTE:** > > Mobilization hedge: defense R&D, a maintained industrial base, and a professional cadre force. The WWII lesson is to maintain the capacity to scale, not a standing empire: a peer buildup gives years of warning, and the US went from the 17th-ranked army in 1939 to victory in four years. This is the cheapest insurance and the most neglected. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$40 billion, \$100 billion\] • Distribution: Normal (SE: \$15 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$40 billion and \$100 billion (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: First-Principles Defense: Mobilization Hedge > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### First-Principles Defense: National Guard: \$30 billion > **NOTE:** > > National Guard / territorial land defense: a citizen-soldier reserve (the Switzerland model) for homeland defense and disaster response. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$24 billion, \$40 billion\] • Distribution: Normal (SE: \$4 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$24 billion and \$40 billion (±27%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: First-Principles Defense: National Guard > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### First-Principles Defense: Nuclear Second Strike: \$30 billion > **NOTE:** > > Survivable nuclear second strike: an SSBN-centric minimum credible deterrent (~100-200 survivable warheads on ballistic-missile submarines). The one threat geography does not neutralize. Drops the first-strike-attractive ICBM silos and the bomber leg. The UK and France each sustain continuous-at-sea deterrents within total defense budgets under \$80B. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$20 billion, \$50 billion\] • Distribution: Normal (SE: \$8 billion) > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$20 billion and \$50 billion (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: First-Principles Defense: Nuclear Second Strike > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Overlap Discount Factor: 1:1 > **NOTE:** > > Overlap discount factor between US government waste categories. Set to 1.0 (no discount). Categories are treated as additive, recognizing that any overlap is offset by excluded categories (state/local inefficiency, implicit subsidies, behavioral effects). > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Political Effort Multiplier (US): 0.7x > **NOTE:** > > Fraction of campaign + lobbying spending needed to achieve policy reform. Accounts for efficiency gains from coordination, message clarity, and public interest alignment. Range 0.4-1.2 reflects uncertainty about political dynamics. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0.4x, 1.2x\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0.4x and 1.2x (±57%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Political Effort Multiplier (US) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Switzerland-US Life Expectancy Gap: 6.5 years > **NOTE:** > > Life expectancy gap: Switzerland vs US. Switzerland achieves 6.5 extra years of life while spending 3% LESS of GDP on government. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### US-Switzerland Spending Gap: 300% > **NOTE:** > > Government spending gap: US spends 3 percentage points MORE of GDP than Switzerland yet achieves worse outcomes. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds: 10% > **NOTE:** > > Percentage of captured dividend funding VICTORY Incentive Alignment Bonds (10%) > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### Average Years of Life Lost per War Death: 27 years > **NOTE:** > > Average years of life lost per war/conflict death. Based on avg age at death ~28 (soldiers ~23, civilians older) vs mid-century life expectancy ~55. > > #### Uncertainty Range > > **Technical**: 95% CI: \[20 years, 35 years\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between 20 years and 35 years (±28%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Average Years of Life Lost per War Death > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Child Share of War Deaths Since 1900: 33% > **NOTE:** > > Estimated share of war deaths since 1900 that were children under 18. Constructed from category-weighted estimates: combat ~3%, civilian ~35%, genocide ~33%, famine ~60%. Conservative aggregate ~33%. Sources: de Waal 2017 (famine child mortality), APA 2001 (civilian child share). > > #### Uncertainty Range > > **Technical**: 95% CI: \[25%, 40%\] • Distribution: Uniform > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 25% and 40% (±23%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Child Share of War Deaths Since 1900 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Peace Growth Boost (8 Channels, Overlap-Corrected): 2.6% > **NOTE:** > > Stacked annual growth boost from 8 non-overlapping war channels. Ch1: productive reallocation 0.8-1.5pp (budget + innovation merged). Ch2: preserved capital 0.2-0.4pp. Ch3: population 0.2-0.4pp. Ch4: no trade drag 0.1-0.3pp. Ch5: no environmental damage 0.1-0.2pp. Ch6: no Cold War isolation 0.1-0.3pp. Ch7: better institutions 0.1-0.3pp. Ch8: open scientific collaboration 0.05-0.15pp. Low 1.65pp, mid 2.6pp, high 3.55pp. > > #### Uncertainty Range > > **Technical**: 95% CI: \[1.65%, 3.55%\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between 1.65% and 3.55% (±37%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Peace Growth Boost (8 Channels, Overlap-Corrected) > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Total War and Conflict Deaths Since 1900: 310 million deaths > **NOTE:** > > Total deaths from wars, conflicts, genocides, and policy-induced famines since 1900. Built from non-overlapping categories: Rummel democide 264M (incl 21st century) + battle deaths 39M + collateral civilian deaths 30M - overlap adjustment 25M = 308M, rounded to 310M. Range: White low 200M to Rummel-high-plus-military 340M. > > #### Uncertainty Range > > **Technical**: 95% CI: \[200 million deaths, 340 million deaths\] • Distribution: Uniform > > **What this means**: This estimate has moderate uncertainty. The true value likely falls between 200 million deaths and 340 million deaths (±23%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Total War and Conflict Deaths Since 1900 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Cumulative Environmental Destruction from War Since 1900: \$5 trillion > **NOTE:** > > Cumulative environmental destruction from wars since 1900 (2024 USD). Nuclear testing, Agent Orange, Gulf War oil fires, DU contamination, Zone Rouge, military CO2 emissions, land mines. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$2 trillion, \$10 trillion\] • Distribution: Lognormal > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$2 trillion and \$10 trillion (±80%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).* > > #### Input Distribution > > Probability Distribution: Cumulative Environmental Destruction from War Since 1900 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### War Medical Toolchain Prize Budget: \$20 trillion > **NOTE:** > > Aggressive prosecutor reserve for buying the missing medical toolchain: prizes, diagnostics, EHRs, sequencing, AI, factories, surveillance, and pragmatic-trial infrastructure before counting remaining money as direct trial capacity. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$5 trillion, \$50 trillion\] • Distribution: Triangular > > **What this means**: This estimate is highly uncertain. The true value likely falls between \$5 trillion and \$50 trillion (±112%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The triangular distribution means values cluster around a most-likely point but can range higher or lower.* > > #### Input Distribution > > Probability Distribution: War Medical Toolchain Prize Budget > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Cumulative Property Destruction from War Since 1900: \$45 trillion > **NOTE:** > > Cumulative property and infrastructure destruction from major wars since 1900 (2024 USD). WWI ~\$5T, WWII ~\$23T, Korea ~\$0.5T, Vietnam ~\$1T, post-9/11 ~\$8T, other ~\$7.5T. > > #### Uncertainty Range > > **Technical**: 95% CI: \[\$30 trillion, \$60 trillion\] • Distribution: Uniform > > **What this means**: There’s significant uncertainty here. The true value likely falls between \$30 trillion and \$60 trillion (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The uniform distribution means any value in the range is equally likely.* > > #### Input Distribution > > Probability Distribution: Cumulative Property Destruction from War Since 1900 > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### War-Redirect Aging Lag After Disease Control: 40 years > **NOTE:** > > Additional lag after broad disease-control capacity before biological aging becomes a treatable risk factor in the aggressive prosecutor model. Fermi rationale: aging research requires the molecular biology toolchain (DNA structure, oncogenes, telomere biology, cellular senescence) which itself builds on the disease-control infrastructure. With investment proportional to funding and prizes accelerating iteration, the hallmarks-of-aging framework (telomeres, senolytics, mTOR) likely emerges ~15-20 years faster than the historical timeline – but geroscience is genuinely downstream of molecular biology and cannot be fully parallelized with disease-control research. 40 years is the central estimate; confidence interval (10-65) is wide because this is the most speculative component of the model. > > #### Uncertainty Range > > **Technical**: 95% CI: \[10 years, 65 years\] • Distribution: Triangular > > **What this means**: This estimate is highly uncertain. The true value likely falls between 10 years and 65 years (±69%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The triangular distribution means values cluster around a most-likely point but can range higher or lower.* > > #### Input Distribution > > Probability Distribution: War-Redirect Aging Lag After Disease Control > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### War-Redirect Pleading End Year: 2024 > **NOTE:** > > End year for the aggressive prosecutor post-cutoff plaintiff count. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### War-Redirect Medical Counterfactual Start Year: 1900 > **NOTE:** > > Start year for the aggressive prosecutor medical redirect counterfactual. > > #### Uncertainty Range > > **Technical**: Distribution: Fixed > > *Core definition* ### War-Redirect Medical Toolchain Bootstrap Years: 14 years > **NOTE:** > > Estimated minimum physical build time for the medical toolchain even with 10x capital: diagnostics, cell culture, manufacturing scale-up, trained researchers, surveillance, and trial infrastructure. Fermi rationale: (1) scientific breakthroughs are treated as largely proportional to investment – more money attracts talent from finance and other high-paying sectors, more researchers mean more parallel experiments and more shots on goal, so conceptual barriers are not treated as exogenous; (2) even so, a physical floor exists – you cannot train a molecular biologist in a year or build a penicillin factory overnight regardless of capital; (3) prize-based and market-incentive funding is assumed, not NIH grant-style funding – prizes pay for results, not process, yielding roughly 5-10x more useful output per dollar, making this estimate conservative relative to current NIH efficiency as a baseline; (4) Operation Warp Speed compressed a 10-15 year vaccine timeline to 9 months with advance purchase commitments – ~15x acceleration – suggesting the physical floor is around 12-18 months for known-science applications. 14 years reflects the harder case of building infrastructure for unknown-science applications from a 1900 starting point. Confidence interval (0-40) reflects genuine uncertainty; the central estimate is not reverse-engineered to hit any target year. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0 years, 40 years\] • Distribution: Triangular > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0 years and 40 years (±143%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The triangular distribution means values cluster around a most-likely point but can range higher or lower.* > > #### Input Distribution > > Probability Distribution: War-Redirect Medical Toolchain Bootstrap Years > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* ### Wishocratic Crowd Allocation Alpha: 0.5% > **NOTE:** > > Allocation alpha from wishocratic sector- and manager-level capital routing. Crowds route capital across sectors and managers at least as well as cap-weighted indices or committee allocators. SPIVA shows 88% of active large-cap managers underperform their benchmark over 15 years; Preqin shows top-quartile vs bottom-quartile VC manager dispersion of 5-15%. The 0.5% central value assumes only that RAPPA avoids the bottom half of manager dispersion at the allocation level, not that it finds the top quartile. This is not a claim that crowds beat experts at picking individual companies; power-law outlier selection is the one thing crowds are empirically worse at than specialists. > > #### Uncertainty Range > > **Technical**: 95% CI: \[0%, 1.5%\] • Distribution: Normal > > **What this means**: This estimate is highly uncertain. The true value likely falls between 0% and 1.5% (±150%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results. > > *The normal distribution means values cluster around the center with equal chances of being higher or lower.* > > #### Input Distribution > > Probability Distribution: Wishocratic Crowd Allocation Alpha > > *This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.* > > *Core definition* 1\. GiveWell. GiveWell cost per life saved for top charities (2024). *GiveWell: Top Charities* *General range: \$3,000-\$5,500 per life saved (GiveWell top charities) Helen Keller International (Vitamin A): \$3,500 average (2022-2024); varies \$1,000-\$8,500 by country Against Malaria Foundation: \$5,500 per life saved; GiveWell AMF page: "We estimate (as of December 2023) that it costs  \$3,000 to  \$8,000 to avert a death in locations where GiveWell funds campaigns." New Incentives (vaccination incentives): \$4,500 per life saved Malaria Consortium (seasonal malaria chemoprevention):  \$3,500 per life saved VAS program details:  \$2 to provide vitamin A supplements to child for one year Note: Figures accurate for 2024. Helen Keller VAS program has wide country variation (\$1K-\$8.5K) but \$3,500 is accurate average. Among most cost-effective interventions globally Additional sources: https://www.givewell.org/charities/top-charities \| https://www.givewell.org/charities/helen-keller-international \| https://www.givewell.org/charities/amf \| https://ourworldindata.org/cost-effectiveness* . 2\. IQVIA Institute for Human Data Science. The global use of medicines 2024: Outlook to 2028. *IQVIA Institute Report* (2024) *Global days of therapy reached 1.8 trillion in 2019 (234 defined daily doses per person). Diabetes, respiratory, CVD, and cancer account for 71 percent of medicine use. Projected to reach 3.8 trillion DDDs by 2028.* 3\. ACS CAN. Clinical trial patient participation rate. *ACS CAN: Barriers to Clinical Trial Enrollment* *Only 3-5% of adult cancer patients in US receive treatment within clinical trials About 5% of American adults have ever participated in any clinical trial Oncology: 2-3% of all oncology patients participate Contrast: 50-60% enrollment for pediatric cancer trials (\<15 years old) Note:  20% of cancer trials fail due to insufficient enrollment; 11% of research sites enroll zero patients Additional sources: https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer \| https://hints.cancer.gov/docs/Briefs/HINTS_Brief_48.pdf* . 4\. Orphanet Journal of Rare Diseases (2024). Rare disease treatment gap. *Orphanet Journal of Rare Diseases (2024)* (2024) *Most patients wait 5 to 10 years to get an accurate diagnosis - and only about 5% of rare diseases have an FDA-approved treatment. Over the 40 years of the ODA, 6,340 orphan drug designations were granted, representing drug development for 1,079 rare diseases out of 7,000-10,000 known rare conditions.* 5\. C&EN. Annual number of new drugs approved globally:  50. *C&EN* (2025) *50 new drugs approved annually Additional sources: https://cen.acs.org/pharmaceuticals/50-new-drugs-received-FDA/103/i2 \| https://www.fda.gov/drugs/development-approval-process-drugs/novel-drug-approvals-fda* . 6\. Think by Numbers. Pre-1962 drug development costs and timeline (think by numbers). *Think by Numbers: How Many Lives Does FDA Save?* (1962) *Historical estimates (1970-1985): USD \$226M fully capitalized (2011 prices) 1980s drugs:  \$65M after-tax R&D (1990 dollars),  \$194M compounded to approval (1990 dollars) Modern comparison: \$2-3B costs, 7-12 years (dramatic increase from pre-1962) Context: 1962 regulatory clampdown reduced new treatment production by 70%, dramatically increasing development timelines and costs Note: Secondary source; less reliable than Congressional testimony Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ \| https://en.wikipedia.org/wiki/Cost_of_drug_development \| https://www.statnews.com/2018/10/01/changing-1962-law-slash-drug-prices/* . 7\. Baily, M. N. Pre-1962 drug development costs (baily 1972). *Baily (1972)* (1972) *Pre-1962: Average cost per new chemical entity (NCE) was \$6.5 million (1980 dollars) Inflation-adjusted to 2024 dollars: \$6.5M (1980) ≈ \$22.5M (2024), using CPI multiplier of 3.46× Real cost increase (inflation-adjusted): \$22.5M (pre-1962) → \$2,600M (2024) = 116× increase Note: This represents the most comprehensive academic estimate of pre-1962 drug development costs based on empirical industry data Additional sources: https://samizdathealth.org/wp-content/uploads/2020/12/hlthaff.1.2.6.pdf* . 8\. Manhattan Institute. RECOVERY trial 82× cost reduction. *Manhattan Institute: Slow Costly Trials* *RECOVERY trial:  \$500 per patient (\$20M for 48,000 patients = \$417/patient) Typical clinical trial:  \$41,000 median per-patient cost Cost reduction:  80-82× cheaper (\$41,000 ÷ \$500 ≈ 82×) Efficiency: \$50 per patient per answer (10 therapeutics tested, 4 effective) Dexamethasone estimated to save \>630,000 lives Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs \| https://pmc.ncbi.nlm.nih.gov/articles/PMC9293394/* . 9\. Applied Clinical Trials. Industry vs. Government clinical trial spending split (90/10). *Applied Clinical Trials* *Industry funds  90% of interventional drug/device trials. Industry spending  \$32B vs Govt  \$3.6B (2008 data). Additional sources: https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market \| https://www.tctmd.com/news/price-knowledge-industry-sponsored-studies-era-evidence-based-medicine* . 10\. Lichtenberg, F. R. [How many life-years have new drugs saved? A three-way fixed-effects analysis of 66 diseases in 27 countries, 2000-2013](https://doi.org/10.1093/inthealth/ihz003). *International Health* **11**, 403–416 (2019) *Using 3-way fixed-effects methodology (disease-country-year) across 66 diseases in 22 countries, this study estimates that drugs launched after 1981 saved 148.7 million life-years in 2013 alone. The regression coefficients for drug launches 0-11 years prior (beta=-0.031, SE=0.008) and 12+ years prior (beta=-0.057, SE=0.013) on years of life lost are highly significant (p\<0.0001). Confidence interval for life-years saved: 79.4M-239.8M (95 percent CI) based on propagated standard errors from Table 2.* 11\. Sinn, M. P. *The Political Dysfunction Tax*. (2025) doi:[10.5281/zenodo.18603840](https://doi.org/10.5281/zenodo.18603840) *Quantifying the gap between current global governance and theoretical maximum welfare, estimating a 31-53% efficiency score and \$101 trillion in annual opportunity costs.* 12\. Composite estimate based on Orphanet. Average time to cure under current system. *Queue-based calculation:  7,000 diseases without effective treatment ÷  15 diseases getting first treatment per year =  467 years for the average disease to receive a cure under the status quo system. This is consistent with the fact that only 5% of rare diseases have treatments after 40+ years of the Orphan Drug Act. Well-funded diseases may take 30-50 years; underfunded diseases 100-500+ years; and neglected diseases effectively never within human planning horizons.* 13\. Correlates of War Project. [National material capabilities (NMC) dataset](https://correlatesofwar.org/data-sets/national-material-capabilities/). (2017). 14\. NIH Common Fund. NIH pragmatic trials: Minimal funding despite 30x cost advantage. *NIH Common Fund: HCS Research Collaboratory* (2025) *The NIH Pragmatic Trials Collaboratory funds trials at \$500K for planning phase, \$1M/year for implementation-a tiny fraction of NIH’s budget. The ADAPTABLE trial cost \$14 million for 15,076 patients (= \$929/patient) versus \$420 million for a similar traditional RCT (30x cheaper), yet pragmatic trials remain severely underfunded. PCORnet infrastructure enables real-world trials embedded in healthcare systems, but receives minimal support compared to basic research funding. Additional sources: https://commonfund.nih.gov/hcscollaboratory \| https://pcornet.org/wp-content/uploads/2025/08/ADAPTABLE_Lay_Summary_21JUL2025.pdf \| https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604499/* . 15\. Ritchie, H. & Roser, M. [Water use and stress](https://ourworldindata.org/water-use-stress). (2018). 16\. Antimicrobial Resistance Collaborators. [Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis](https://pubmed.ncbi.nlm.nih.gov/35065702/). *The Lancet* **399**, 629–655 (2022). 17\. Cato Institute. Chance of dying from terrorism statistic. *Cato Institute: Terrorism and Immigration Risk Analysis* *Chance of American dying in foreign-born terrorist attack: 1 in 3.6 million per year (1975-2015) Including 9/11 deaths; annual murder rate is 253x higher than terrorism death rate More likely to die from lightning strike than foreign terrorism Note: Comprehensive 41-year study shows terrorism risk is extremely low compared to everyday dangers Additional sources: https://www.cato.org/policy-analysis/terrorism-immigration-risk-analysis \| https://www.nbcnews.com/news/us-news/you-re-more-likely-die-choking-be-killed-foreign-terrorists-n715141* . 18\. NIH. Antidepressant clinical trial exclusion rates. *Zimmerman et al.* (2015) *Mean exclusion rate: 86.1% across 158 antidepressant efficacy trials (range: 44.4% to 99.8%) More than 82% of real-world depression patients would be ineligible for antidepressant registration trials Exclusion rates increased over time: 91.4% (2010-2014) vs. 83.8% (1995-2009) Most common exclusions: comorbid psychiatric disorders, age restrictions, insufficient depression severity, medical conditions Emergency psychiatry patients: only 3.3% eligible (96.7% excluded) when applying 9 common exclusion criteria Only a minority of depressed patients seen in clinical practice are likely to be eligible for most AETs Note: Generalizability of antidepressant trials has decreased over time, with increasingly stringent exclusion criteria eliminating patients who would actually use the drugs in clinical practice Additional sources: https://pubmed.ncbi.nlm.nih.gov/26276679/ \| https://pubmed.ncbi.nlm.nih.gov/26164052/ \| https://www.wolterskluwer.com/en/news/antidepressant-trials-exclude-most-real-world-patients-with-depression* . 19\. fishcount.org.uk. [Fish count estimates](https://fishcount.org.uk/fish-count-estimates-2). (2019). 20\. CNBC. Warren buffett’s career average investment return. *CNBC* (2025) *Berkshire’s compounded annual return from 1965 through 2024 was 19.9%, nearly double the 10.4% recorded by the S&P 500. Berkshire shares skyrocketed 5,502,284% compared to the S&P 500’s 39,054% rise during that period. Additional sources: https://www.cnbc.com/2025/05/05/warren-buffetts-return-tally-after-60-years-5502284percent.html \| https://www.slickcharts.com/berkshire-hathaway/returns* . 21\. Cassidy, E. S., West, P. C., Gerber, J. S. & Foley, J. A. [Redefining agricultural yields: From tonnes to people nourished per hectare](https://doi.org/10.1088/1748-9326/8/3/034015). *Environmental Research Letters* **8**, 034015 (2013). 22\. Pike, J. [U.s. Forces fire 250,000 rounds for every insurgent killed](https://jonathanturley.org/2011/01/10/gao-u-s-has-fired-250000-rounds-for-every-insurgent-killed/). (2011) *The General Accounting Office reports that US forces used 1.8 billion rounds of small-arms ammunition per year, a level that more than doubled in five years. An estimated 250,000 rounds were fired for every insurgent killed in Iraq and Afghanistan.* 23\. U.S. Department of Defense. [5.56mm NATO ammunition bulk procurement pricing](https://www.bulkcheapammo.com/rifle-ammo/556-ammo). (2024) *The cost of 5.56mm NATO ammunition at military bulk procurement rates is approximately \$0.40 per round, based on Lake City Army Ammunition Plant production and commercial market floor prices for mil-spec M855 ammunition.* 24\. Congressional Budget Office. *Effects of the Immigration Surge on the Federal Budget and the Economy*. (2024). 25\. Forbes. [Forbes world’s billionaires list 2024](https://www.forbes.com/billionaires/). (2024) *Forbes identified a record 2,781 billionaires worldwide with combined net worth of \$14.2 trillion, 141 more than 2023. Bernard Arnault (LVMH) topped the list at \$233 billion.* 26\. CDC MMWR. Childhood vaccination economic benefits. *CDC MMWR* (1994) *US programs (1994-2023): \$540B direct savings, \$2.7T societal savings ( \$18B/year direct,  \$90B/year societal) Global (2001-2020): \$820B value for 10 diseases in 73 countries ( \$41B/year) ROI: \$11 return per \$1 invested Measles vaccination alone saved 93.7M lives (61% of 154M total) over 50 years (1974-2024) Additional sources: https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm \| https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00850-X/fulltext* . 27\. CDC. Childhood vaccination (US) ROI. *CDC* (2017). 28\. World Health Organization. WHO global health estimates 2024. *World Health Organization* (2024) *Comprehensive mortality and morbidity data by cause, age, sex, country, and year Global mortality:  55-60 million deaths annually Lives saved by modern medicine (vaccines, cardiovascular drugs, oncology):  12M annually (conservative aggregate) Leading causes of death: Cardiovascular disease (17.9M), Cancer (10.3M), Respiratory disease (4.0M) Note: Baseline data for regulatory mortality analysis. Conservative estimate of pharmaceutical impact based on WHO immunization data (4.5M/year from vaccines) + cardiovascular interventions (3.3M/year) + oncology (1.5M/year) + other therapies. Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates* . 29\. U.S. Department of Justice. [The false claims act](https://www.justice.gov/civil/false-claims-act). (2025). 30\. United States Supreme Court. [State farm mutual automobile insurance co. V. Campbell, 538 u.s. 408](https://www.govinfo.gov/app/details/USREPORTS-538/USREPORTS-538-408). (2003). 31\. U.S. Bureau of Labor Statistics. [CPI inflation calculator](https://www.bls.gov/data/inflation_calculator.htm). (2024) *CPI-U (1980): 82.4 CPI-U (2024): 313.5 Inflation multiplier (1980-2024): 3.80× Cumulative inflation: 280.48% Average annual inflation rate: 3.08% Note: Official U.S. government inflation data using Consumer Price Index for All Urban Consumers (CPI-U). Additional sources: https://www.bls.gov/data/inflation_calculator.htm* . 32\. James Surowiecki. *[The Wisdom of Crowds](https://archive.org/details/wisdomofcrowds0000suro)*. (Surowiecki, 2004). *Explores the aggregation of information in groups, arguing that decisions are often better than could have been made by any single member of the group. The opening anecdote relates Francis Galton’s surprise that the crowd at a county fair accurately guessed the weight of an ox when the median of their individual guesses was taken. The three conditions for a group to be intelligent are diversity, independence, and decentralization. Additional sources: https://archive.org/details/wisdomofcrowds0000suro \| https://en.wikipedia.org/wiki/The_Wisdom_of_Crowds \| https://www.amazon.com/Wisdom-Crowds-James-Surowiecki/dp/0385721706* . 33\. ClinicalTrials.gov API v2 direct analysis. ClinicalTrials.gov cumulative enrollment data (2025). *Direct analysis via ClinicalTrials.gov API v2* *Analysis of 100,000 active/recruiting/completed trials on ClinicalTrials.gov (as of January 2025) shows cumulative enrollment of 12.2 million participants: Phase 1 (722k), Phase 2 (2.2M), Phase 3 (6.5M), Phase 4 (2.7M). Median participants per trial: Phase 1 (33), Phase 2 (60), Phase 3 (237), Phase 4 (90). Additional sources: https://clinicaltrials.gov/data-api/api* . 34\. ScienceDaily. Global prevalence of chronic disease. *ScienceDaily: GBD 2015 Study* (2015) *2.3 billion individuals had more than five ailments (2013) Chronic conditions caused 74% of all deaths worldwide (2019), up from 67% (2010) Approximately 1 in 3 adults suffer from multiple chronic conditions (MCCs) Risk factor exposures: 2B exposed to biomass fuel, 1B to air pollution, 1B smokers Projected economic cost: \$47 trillion by 2030 Note: 2.3B with 5+ ailments is more accurate than "2B with chronic disease." One-third of all adults globally have multiple chronic conditions Additional sources: https://www.sciencedaily.com/releases/2015/06/150608081753.htm \| https://pmc.ncbi.nlm.nih.gov/articles/PMC10830426/ \| https://pmc.ncbi.nlm.nih.gov/articles/PMC6214883/* . 35\. Research and Markets. Global clinical trials market 2024. *Research and Markets* (2024) *Global clinical trials market valued at approximately \$83 billion in 2024, with projections to reach \$83-132 billion by 2030. Additional sources: https://www.globenewswire.com/news-release/2024/04/19/2866012/0/en/Global-Clinical-Trials-Market-Research-Report-2024-An-83-16-Billion-Market-by-2030-AI-Machine-Learning-and-Blockchain-will-Transform-the-Clinical-Trials-Landscape.html \| https://www.precedenceresearch.com/clinical-trials-market* . 36\. Williams, R. J., Tse, T., DiPiazza, K. & Zarin, D. A. [Terminated trials in the ClinicalTrials.gov results database: Evaluation of availability of primary outcome data and reasons for termination](https://doi.org/10.1371/journal.pone.0127242). *PLOS One* **10**, e0127242 (2015) *Approximately 12% of trials with results posted on the ClinicalTrials.gov results database (905/7,646) were terminated. Primary reasons: insufficient accrual (57% of non-data-driven terminations), business/strategic reasons, and efficacy/toxicity findings (21% data-driven terminations).* 37\. IQVIA Report. Global trial capacity. *IQVIA Report: Clinical Trial Subjects Number Drops Due to Decline in COVID-19 Enrollment* *1.9M participants annually (2022, post-COVID normalization from 4M peak in 2021) Additional sources: https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/* . 38\. OpenSecrets. Defense sector lobbying summary. *OpenSecrets* (2025) *Military sector federal lobbying totaled \$198,009,793 in 2025, up from \$159.5 million in 2024 and \$142.9 million in 2023. Additional sources: https://www.opensecrets.org/federal-lobbying/sectors/summary?id=D* . 39\. Companies Market Cap. [BAE systems and thales market capitalization](https://companiesmarketcap.com/bae-systems/marketcap/). (2026) *BAE Systems market capitalization approx \$75.80B and Thales approx \$56.68B as of June 2026, combined approx \$132.5B for the two major allied European military primes. Additional sources: https://companiesmarketcap.com/thales/marketcap/* . 40\. Stock Analysis. [Military prime contractor market capitalization and float statistics](https://stockanalysis.com/stocks/). (2026) *Combined market capitalization of 11 US military primes approx \$835.8B at the 2026-06-11 close: RTX \$248.07B, Boeing \$174.71B, Lockheed Martin \$126.51B, General Dynamics \$96.90B, Northrop Grumman \$78.48B, L3Harris \$58.16B, Leidos \$15.36B, Huntington Ingalls \$11.86B, CACI \$11.61B, Booz Allen Hamilton \$9.24B, SAIC \$4.86B. Tradeable float across the 13 Western primes (adding BAE Systems and Thales) approx \$880B, about 91 percent of combined cap (range \$850-900B), from per-company float and shares-outstanding statistics pages; big-5 floats verified individually (RTX 92.6%, BA 96.0%, LMT 85.7%, GD 94.2%, NOC 99.7%); Thales is the outlier at approx 45% float because the French State (26.60%) and Dassault Aviation (26.59%) stakes are locked. Additional sources: https://stockanalysis.com/stocks/rtx/statistics/ \| https://www.dassault-aviation.com/en/group/about-us/shareholding-structure-and-organization-chart/* . 41\. Rummel, R. J. *[Death by Government: Genocide and Mass Murder Since 1900](https://www.hawaii.edu/powerkills/NOTE1.HTM)*. (Transaction Publishers, 1994). *Political scientist R.J. Rummel’s comprehensive accounting of democide (government murder of unarmed civilians) in the 20th century. His final revised estimate: 262 million people murdered by their own governments from 1900-1999, excluding battle deaths in wars. Range: 200-272+ million. Communist regimes account for the largest share (100-148+ million). Updated figures at hawaii.edu/powerkills.* 42\. GiveWell. Cost per DALY for deworming programs. *Schistosomiasis treatment: \$28.19-\$70.48 per DALY (using arithmetic means with varying disability weights) Soil-transmitted helminths (STH) treatment: \$82.54 per DALY (midpoint estimate) Note: GiveWell explicitly states this 2011 analysis is "out of date" and their current methodology focuses on long-term income effects rather than short-term health DALYs Additional sources: https://www.givewell.org/international/technical/programs/deworming/cost-effectiveness* . 43\. GBD 2017 Diet Collaborators. [Health effects of dietary risks in 195 countries, 1990-2017: A systematic analysis for the global burden of disease study 2017](https://pubmed.ncbi.nlm.nih.gov/30954305/). *The Lancet* **393**, 1958–1972 (2019). 44\. Calculated from IHME Global Burden of Disease (2.55B DALYs) and global GDP per capita valuation. \$109 trillion annual global disease burden. *The global economic burden of disease, including direct healthcare costs (\$8.2 trillion) and lost productivity (\$100.9 trillion from 2.55 billion DALYs × \$39,570 per DALY), totals approximately \$109.1 trillion annually.* 45\. U.S. Department of Transportation. [Departmental guidance on valuation of a statistical life in economic analysis](https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis). (2024). 46\. Biotechnology Innovation Organization (BIO). BIO clinical development success rates 2011-2020. *Biotechnology Innovation Organization (BIO)* (2021) *Phase I duration: 2.3 years average Total time to market (Phase I-III + approval): 10.5 years average Phase transition success rates: Phase I→II: 63.2%, Phase II→III: 30.7%, Phase III→Approval: 58.1% Overall probability of approval from Phase I: 12% Note: Largest publicly available study of clinical trial success rates. Efficacy lag = 10.5 - 2.3 = 8.2 years post-safety verification. Additional sources: https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf* . 47\. Nature Medicine. Drug repurposing rate ( 30%). *Nature Medicine* (2024) *Approximately 30% of drugs gain at least one new indication after initial approval. Additional sources: https://www.nature.com/articles/s41591-024-03233-x* . 48\. EPI. Education investment economic multiplier (2.1). *EPI: Public Investments Outside Core Infrastructure* *Early childhood education: Benefits 12X outlays by 2050; \$8.70 per dollar over lifetime Educational facilities: \$1 spent → \$1.50 economic returns Energy efficiency comparison: 2-to-1 benefit-to-cost ratio (McKinsey) Private return to schooling:  9% per additional year (World Bank meta-analysis) Note: 2.1 multiplier aligns with benefit-to-cost ratios for educational infrastructure/energy efficiency. Early childhood education shows much higher returns (12X by 2050) Additional sources: https://www.epi.org/publication/bp348-public-investments-outside-core-infrastructure/ \| https://documents1.worldbank.org/curated/en/442521523465644318/pdf/WPS8402.pdf \| https://freopp.org/whitepapers/establishing-a-practical-return-on-investment-framework-for-education-and-skills-development-to-expand-economic-opportunity/* . 49\. PMC. Healthcare investment economic multiplier (1.8). *PMC: California Universal Health Care* (2022) *Healthcare fiscal multiplier: 4.3 (95% CI: 2.5-6.1) during pre-recession period (1995-2007) Overall government spending multiplier: 1.61 (95% CI: 1.37-1.86) Why healthcare has high multipliers: No effect on trade deficits (spending stays domestic); improves productivity & competitiveness; enhances long-run potential output Gender-sensitive fiscal spending (health & care economy) produces substantial positive growth impacts Note: "1.8" appears to be conservative estimate; research shows healthcare multipliers of 4.3 Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5954824/ \| https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus \| https://ncbi.nlm.nih.gov/pmc/articles/PMC3849102/ \| https://set.odi.org/wp-content/uploads/2022/01/Fiscal-multipliers-review.pdf* . 50\. World Bank. Infrastructure investment economic multiplier (1.6). *World Bank: Infrastructure Investment as Stimulus* (2022) *Infrastructure fiscal multiplier:  1.6 during contractionary phase of economic cycle Average across all economic states:  1.5 (meaning \$1 of public investment → \$1.50 of economic activity) Time horizon: 0.8 within 1 year,  1.5 within 2-5 years Range of estimates: 1.5-2.0 (following 2008 financial crisis & American Recovery Act) Italian public construction: 1.5-1.9 multiplier US ARRA: 0.4-2.2 range (differential impacts by program type) Economic Policy Institute: Uses 1.6 for infrastructure spending (middle range of estimates) Note: Public investment less likely to crowd out private activity during recessions; particularly effective when monetary policy loose with near-zero rates Additional sources: https://blogs.worldbank.org/en/ppps/effectiveness-infrastructure-investment-fiscal-stimulus-what-weve-learned \| https://www.gihub.org/infrastructure-monitor/insights/fiscal-multiplier-effect-of-infrastructure-investment/ \| https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus \| https://www.richmondfed.org/publications/research/economic_brief/2022/eb_22-04* . 51\. Mercatus. Military spending economic multiplier (0.6). *Mercatus: Defense Spending and Economy* *Ramey (2011):  0.6 short-run multiplier Barro (1981): 0.6 multiplier for WWII spending (war spending crowded out  40¢ private economic activity per federal dollar) Barro & Redlick (2011): 0.4 within current year, 0.6 over two years; increased govt spending reduces private-sector GDP portions General finding: \$1 increase in deficit-financed federal military spending = less than \$1 increase in GDP Variation by context: Central/Eastern European NATO: 0.6 on impact, 1.5-1.6 in years 2-3, gradual fall to zero Ramey & Zubairy (2018): Cumulative 1% GDP increase in military expenditure raises GDP by  0.7% Additional sources: https://www.mercatus.org/research/research-papers/defense-spending-and-economy \| https://cepr.org/voxeu/columns/world-war-ii-america-spending-deficits-multipliers-and-sacrifice \| https://www.rand.org/content/dam/rand/pubs/research_reports/RRA700/RRA739-2/RAND_RRA739-2.pdf* . 52\. Nordhaus, W. D. *Schumpeterian Profits in the American Economy: Theory and Measurement*. (2004) doi:[10.3386/w10433](https://doi.org/10.3386/w10433). 53\. Anthis, J. R. [US factory farming estimates](https://www.sentienceinstitute.org/us-factory-farming-estimates). (2024). 54\. Rosado, P. [More than 80 billion land animals are slaughtered for meat every year](https://ourworldindata.org/data-insights/billions-of-chickens-ducks-and-pigs-are-slaughtered-for-meat-every-year). (2024). 55\. University of Oxford, LEAP. [Reducing food’s environmental impacts](https://www.leap.ox.ac.uk/article/reducing-foods-environmental-impacts). (2018). 56\. FDA. FDA-approved prescription drug products (20,000+). *FDA* *There are over 20,000 prescription drug products approved for marketing. Additional sources: https://www.fda.gov/media/143704/download* . 57\. FDA. FDA GRAS list count ( 570-700). *FDA* *The FDA GRAS (Generally Recognized as Safe) list contains approximately 570–700 substances. Additional sources: https://www.fda.gov/food/generally-recognized-safe-gras/gras-notice-inventory* . 58\. Ritchie, H., Rosado, P. & Roser, M. [Environmental impacts of food production](https://ourworldindata.org/environmental-impacts-of-food). (2022). 59\. Food and Land Use Coalition. *Growing Better: Ten Critical Transitions to Transform Food and Land Use*. (2019). 60\. [Gallup. (2013)](https://news.gallup.com/poll/166211/worldwide-median-household-income-000.aspx). 61\. ACLED. Active combat deaths annually. *ACLED: Global Conflict Surged 2024* (2024) *2024: 233,597 deaths (30% increase from 179,099 in 2023) Deadliest conflicts: Ukraine (67,000), Palestine (35,000) Nearly 200,000 acts of violence (25% higher than 2023, double from 5 years ago) One in six people globally live in conflict-affected areas Additional sources: https://acleddata.com/2024/12/12/data-shows-global-conflict-surged-in-2024-the-washington-post/ \| https://acleddata.com/media-citation/data-shows-global-conflict-surged-2024-washington-post \| https://acleddata.com/conflict-index/index-january-2024/* . 62\. UCDP. State violence deaths annually. *UCDP: Uppsala Conflict Data Program* *Uppsala Conflict Data Program (UCDP): Tracks one-sided violence (organized actors attacking unarmed civilians) UCDP definition: Conflicts causing at least 25 battle-related deaths in calendar year 2023 total organized violence: 154,000 deaths; Non-state conflicts: 20,900 deaths UCDP collects data on state-based conflicts, non-state conflicts, and one-sided violence Specific "2,700 annually" figure for state violence not found in recent UCDP data; actual figures vary annually Additional sources: https://ucdp.uu.se/ \| https://en.wikipedia.org/wiki/Uppsala_Conflict_Data_Program \| https://ourworldindata.org/grapher/deaths-in-armed-conflicts-by-region* . 63\. Our World in Data. Terror attack deaths (8,300 annually). *Our World in Data: Terrorism* (2024) *2023: 8,352 deaths (22% increase from 2022, highest since 2017) 2023: 3,350 terrorist incidents (22% decrease), but 56% increase in avg deaths per attack Global Terrorism Database (GTD): 200,000+ terrorist attacks recorded (2021 version) Maintained by: National Consortium for Study of Terrorism & Responses to Terrorism (START), U. of Maryland Geographic shift: Epicenter moved from Middle East to Central Sahel (sub-Saharan Africa) - now \>50% of all deaths Additional sources: https://ourworldindata.org/terrorism \| https://reliefweb.int/report/world/global-terrorism-index-2024 \| https://www.start.umd.edu/gtd/ \| https://ourworldindata.org/grapher/fatalities-from-terrorism* . 64\. Institute for Health Metrics and Evaluation (IHME). IHME global burden of disease 2021 (2.88B DALYs, 1.13B YLD). *Institute for Health Metrics and Evaluation (IHME)* (2024) *In 2021, global DALYs totaled approximately 2.88 billion, comprising 1.75 billion Years of Life Lost (YLL) and 1.13 billion Years Lived with Disability (YLD). This represents a 13% increase from 2019 (2.55B DALYs), largely attributable to COVID-19 deaths and aging populations. YLD accounts for approximately 39% of total DALYs, reflecting the substantial burden of non-fatal chronic conditions. Additional sources: https://vizhub.healthdata.org/gbd-results/ \| https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00757-8/fulltext \| https://www.healthdata.org/research-analysis/about-gbd* . 65\. Costs of War Project, Brown University Watson Institute. Environmental cost of war (\$100B annually). *Brown Watson Costs of War: Environmental Cost* *War on Terror emissions: 1.2B metric tons GHG (equivalent to 257M cars/year) Military: 5.5% of global GHG emissions (2X aviation + shipping combined) US DoD: World’s single largest institutional oil consumer, 47th largest emitter if nation Cleanup costs: \$500B+ for military contaminated sites Gaza war environmental damage: \$56.4B; landmine clearance: \$34.6B expected Climate finance gap: Rich nations spend 30X more on military than climate finance Note: Military activities cause massive environmental damage through GHG emissions, toxic contamination, and long-term cleanup costs far exceeding current climate finance commitments Additional sources: https://watson.brown.edu/costsofwar/costs/social/environment \| https://earth.org/environmental-costs-of-wars/ \| https://transformdefence.org/transformdefence/stats/* . 66\. SIPRI. 36:1 disparity ratio of spending on weapons over cures. *SIPRI: Military Spending* (2016) *Global military spending: \$2.7 trillion (2024, SIPRI) Global government medical research:  \$68 billion (2024) Actual ratio: 39.7:1 in favor of weapons over medical research Military R&D alone:  \$85B (2004 data, 10% of global R&D) Military spending increases crowd out health: 1% ↑ military = 0.62% ↓ health spending Note: Ratio actually worse than 36:1. Each 1% increase in military spending reduces health spending by 0.62%, with effect more intense in poorer countries (0.962% reduction) Additional sources: https://www.sipri.org/commentary/blog/2016/opportunity-cost-world-military-spending \| https://pmc.ncbi.nlm.nih.gov/articles/PMC9174441/ \| https://www.congress.gov/crs-product/R45403* . 67\. Think by Numbers. Lost human capital due to war (\$270B annually). *Think by Numbers* (2021) *Lost human capital from war: \$300B annually (economic impact of losing skilled/productive individuals to conflict) Broader conflict/violence cost: \$14T/year globally 1.4M violent deaths/year; conflict holds back economic development, causes instability, widens inequality, erodes human capital 2002: 48.4M DALYs lost from 1.6M violence deaths = \$151B economic value (2000 USD) Economic toll includes: commodity prices, inflation, supply chain disruption, declining output, lost human capital Additional sources: https://thinkbynumbers.org/military/war/the-economic-case-for-peace-a-comprehensive-financial-analysis/ \| https://www.weforum.org/stories/2021/02/war-violence-costs-each-human-5-a-day/ \| https://pubmed.ncbi.nlm.nih.gov/19115548/* . 68\. PubMed. Psychological impact of war cost (\$100B annually). *PubMed: Economic Burden of PTSD* *PTSD economic burden (2018 U.S.): \$232.2B total (\$189.5B civilian, \$42.7B military) Civilian costs driven by: Direct healthcare (\$66B), unemployment (\$42.7B) Military costs driven by: Disability (\$17.8B), direct healthcare (\$10.1B) Exceeds costs of other mental health conditions (anxiety, depression) War-exposed populations: 2-3X higher rates of anxiety, depression, PTSD; women and children most vulnerable Note: Actual burden \$232B, significantly higher than "\$100B" claimed Additional sources: https://pubmed.ncbi.nlm.nih.gov/35485933/ \| https://news.va.gov/103611/study-national-economic-burden-of-ptsd-staggering/ \| https://pmc.ncbi.nlm.nih.gov/articles/PMC9957523/* . 69\. CGDev. UNHCR average refugee support cost. *CGDev* (2024) *The average cost of supporting a refugee is \$1,384 per year. This represents total host country costs (housing, healthcare, education, security). OECD countries average \$6,100 per refugee (mean 2022-2023), with developing countries spending \$700-1,000. Global weighted average of  \$1,384 is reasonable given that 75-85% of refugees are in low/middle-income countries. Additional sources: https://www.cgdev.org/blog/costs-hosting-refugees-oecd-countries-and-why-uk-outlier \| https://www.unhcr.org/sites/default/files/2024-11/UNHCR-WB-global-cost-of-refugee-inclusion-in-host-country-health-systems.pdf* . 70\. World Bank. World bank trade disruption cost from conflict. *World Bank* *Estimated \$616B annual cost from conflict-related trade disruption. World Bank research shows civil war costs an average developing country 30 years of GDP growth, with 20 years needed for trade to return to pre-war levels. Trade disputes analysis shows tariff escalation could reduce global exports by up to \$674 billion. Additional sources: https://www.worldbank.org/en/topic/trade/publication/trading-away-from-conflict \| https://www.nber.org/papers/w11565 \| http://blogs.worldbank.org/en/trade/impacts-global-trade-and-income-current-trade-disputes* . 71\. VA. Veteran healthcare cost projections. *VA* (2026) *VA budget: \$441.3B requested for FY 2026 (10% increase). Disability compensation: \$165.6B in FY 2024 for 6.7M veterans. PACT Act projected to increase spending by \$300B between 2022-2031. Costs under Toxic Exposures Fund: \$20B (2024), \$30.4B (2025), \$52.6B (2026). Additional sources: https://department.va.gov/wp-content/uploads/2025/06/2026-Budget-in-Brief.pdf \| https://www.cbo.gov/publication/45615 \| https://www.legion.org/information-center/news/veterans-healthcare/2025/june/va-budget-tops-400-billion-for-2025-from-higher-spending-on-mandated-benefits-medical-care* . 72\. Sinn, M. P. [Private industry clinical trial spending estimate](https://cost-of-change.warondisease.org). (2025) *Estimated private pharmaceutical and biotech clinical trial spending is approximately \$75-90 billion annually, representing roughly 90% of global clinical trial spending.* 73\. Cybersecurity Ventures. Cybercrime economy projected to reach \$10.5 trillion. *Cybersecurity Ventures: \$10.5T Cybercrime* (2016) *Global cybercrime costs: \$3T (2015) → \$6T (2021) → \$10.5T (2025 projected) 15% annual growth rate If measured as country, would be 3rd largest economy after US and China Greatest transfer of economic wealth in history Note: More profitable than global trade of all major illegal drugs combined. Includes data theft, productivity loss, IP theft, fraud Additional sources: \ \| https://www.boisestate.edu/cybersecurity/2022/06/16/cybercrime-to-cost-the-world-10-5-trillion-annually-by-2025/* . 74\. Bolt, J. & Zanden, J. L. van. [Maddison project database 2020](https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020). (2020) *Historical GDP per capita estimates from year 1 to present. Global GDP per capita in 1900: approximately 1,260 in 1990 international dollars (roughly 3,150 in 2024 USD after PPP and inflation adjustment). Standard reference for long-run comparative economic history.* 75\. Applied Clinical Trials. Global government spending on interventional clinical trials:  \$3-6 billion/year. *Applied Clinical Trials* *Estimated range based on NIH ( \$0.8-5.6B), NIHR (\$1.6B total budget), and EU funding ( \$1.3B/year). Roughly 5-10% of global market. Additional sources: https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market \| https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30357-0/fulltext* . 76\. World Bank. [Expense (% of GDP) - world](https://data.worldbank.org/indicator/GC.XPN.TOTL.GD.ZS?locations=1W). (2024). 77\. UBS. Credit suisse global wealth report 2023. *Credit Suisse/UBS* (2023) *Total global household wealth: USD 454.4 trillion (2022) Wealth declined by USD 11.3 trillion (-2.4%) in 2022, first decline since 2008 Wealth per adult: USD 84,718 Additional sources: https://www.ubs.com/global/en/family-office-uhnw/reports/global-wealth-report-2023.html* . 78\. World Health Organization. [Life expectancy at age 60 (years): Global health observatory](https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-age-60-(years)). (2021). 79\. United Nations Department of Economic and Social Affairs, Population Division. [World population prospects 2024: Summary of results](https://population.un.org/wpp). (2024) *The 2024 Revision of the World Population Prospects provides population estimates and projections for 237 countries or areas. Global median age approximately 30.5 years in 2024, reflecting population-weighted average across all regions.* 80\. Component country budgets. Global government medical research spending (\$67.5B, 2023–2024). *See component country budgets: NIH Budget* . 81\. Stockholm International Peace Research Institute. [Trends in world military expenditure, 2024](https://www.sipri.org/publications/2025/sipri-fact-sheets/trends-world-military-expenditure-2024). (2025). 82\. SIPRI. Global military spending (\$2.72T, 2024). *SIPRI* (2025). 83\. ICAN. Global nuclear weapon maintenance cost: \$100 billion/year. *ICAN: Global Spending \$100B 2024* (2024) *2024: \>\$100 billion (\$190,151/minute) - 11% increase (\$9.9B) from 2023 Nine nuclear-armed states: China, France, India, Israel, N. Korea, Pakistan, Russia, UK, US US: \$56.8B (more than all other 8 states combined); China: \$12.5B; UK: \$10B (+26% YoY, biggest increase) Historical trend: \$72.9B (2019) → \$82.4B (2021) → \>\$100B (2024) Private sector contracts: \$463B ongoing; \$42.5B earned from contracts in 2024 alone Note: \$100B/year figure accurate for 2024. Rapid growth from \$73B (2019). US spends more than rest of world combined on nuclear weapons Additional sources: https://www.icanw.org/global_spending_on_nuclear_weapons_topped_100_billion_in_2024 \| https://www.icanw.org/the_cost_of_nuclear_weapons* . 84\. UN. Global population reaches 8 billion. *UN: World Population 8 Billion Nov 15 2022* (2022) *Milestone: November 15, 2022 (UN World Population Prospects 2022) Day of Eight Billion" designated by UN Added 1 billion people in just 11 years (2011-2022) Growth rate: Slowest since 1950; fell under 1% in 2020 Future: 15 years to reach 9B (2037); projected peak 10.4B in 2080s Projections: 8.5B (2030), 9.7B (2050), 10.4B (2080-2100 plateau) Note: Milestone reached Nov 2022. Population growth slowing; will take longer to add next billion (15 years vs 11 years) Additional sources: https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022 \| https://www.un.org/en/dayof8billion \| https://en.wikipedia.org/wiki/Day_of_Eight_Billion* . 85\. Harvard Kennedy School. 3.5% participation tipping point. *Harvard Kennedy School* (2020) *The research found that nonviolent campaigns were twice as likely to succeed as violent ones, and once 3.5% of the population were involved, they were always successful. Chenoweth and Maria Stephan studied the success rates of civil resistance efforts from 1900 to 2006, finding that nonviolent movements attracted, on average, four times as many participants as violent movements and were more likely to succeed. Key finding: Every campaign that mobilized at least 3.5% of the population in sustained protest was successful (in their 1900-2006 dataset) Note: The 3.5% figure is a descriptive statistic from historical analysis, not a guaranteed threshold. One exception (Bahrain 2011-2014 with 6%+ participation) has been identified. The rule applies to regime change, not policy change in democracies. Additional sources: https://www.hks.harvard.edu/centers/carr/publications/35-rule-how-small-minority-can-change-world \| https://www.hks.harvard.edu/sites/default/files/2024-05/Erica%20Chenoweth_2020-005.pdf \| https://www.bbc.com/future/article/20190513-it-only-takes-35-of-people-to-change-the-world \| https://en.wikipedia.org/wiki/3.5%25_rule* . 86\. International IDEA. [International IDEA voter turnout database world export](https://www.idea.int/data-tools/export?type=region_only&themeId=293&world=all&loc=home). (2026) *Best current register-based estimate of global registered voters. Sum of the latest available country-level Registration counts in International IDEA’s world export on 2026-04-22 = 4,128,142,495 registered voters across 199 countries and political entities. Methodology notes that Registration is the number of names on the voters’ register as reported by electoral management bodies, and comparability is imperfect because voter rolls and registration systems differ across countries. Additional sources: https://www.idea.int/data-tools/data/voter-turnout-database \| https://www.idea.int/data-tools/export?type=region_only&themeId=293&world=all&loc=home* . 87\. World Bank. [Gross savings (% of GDP)](https://data.worldbank.org/indicator/NY.GNS.ICTR.ZS?locations=1W). (2024). 88\. Federation of American Scientists. World nuclear forces. *Federation of American Scientists* (2024) *As of early 2025, we estimate that the world’s nine nuclear-armed states possess a combined total of approximately 12,241 nuclear warheads. Additional sources: https://fas.org/issues/nuclear-weapons/status-world-nuclear-forces/* . 89\. OpenSecrets. [Top lobbying industries 2025](https://www.opensecrets.org/federal-lobbying/industries?cycle=2025). (2025) *Sector ranks and per-company federal lobbying spending for 2025. Combined market capitalization of the top-5 publicly traded US lobbying spenders in each government-controlling sector: pharmaceuticals \$1,794.7B; technology \$13,279.5B; insurance \$385.6B; oil and gas \$1,246.9B; four-sector total approx \$16.71T. Caveats: Meta (Zuckerberg holds 60.8% of voting power) and Alphabet (Page and Brin hold 52.3%) cannot be majority-acquired; Ellison owns 40.6% of Oracle; the largest insurance lobbyists are mutuals with no public shares; trade associations (PhRMA, AHIP, SIFMA, API) are not acquirable. Additional sources: https://stockanalysis.com/stocks/* . 90\. PMC. Only  12% of human interactome targeted. *PMC* (2023) *Mapping 350,000+ clinical trials showed that only  12% of the human interactome has ever been targeted by drugs. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/* . 91\. WHO. ICD-10 code count ( 14,000). *WHO* (2019) *The ICD-10 classification contains approximately 14,000 codes for diseases, signs and symptoms. Additional sources: https://icd.who.int/browse10/2019/en* . 92\. Congressional Research Service. *Internal Revenue Service Appropriations, FY2025 (IF12647)*. (2024). 93\. McFarland, M. J., Hauer, M. E. & Reuben, A. [Half of US population exposed to adverse lead levels in early childhood](https://doi.org/10.1073/pnas.2118631119). *Proceedings of the National Academy of Sciences* **119**, e2118631119 (2022) *Leaded gasoline, used in the US from 1923 until its on-road ban in 1996, exposed more than half of the 2015 US population to adverse blood-lead levels in early childhood. The authors estimate childhood lead exposure cost the population a cumulative 824 million IQ points, an average of 2.6 points per person, rising to 5.9 points for the most-exposed 1966-1970 birth cohort.* 94\. Wikipedia. Longevity escape velocity (LEV) - maximum human life extension potential. *Wikipedia: Longevity Escape Velocity* *Longevity escape velocity: Hypothetical point where medical advances extend life expectancy faster than time passes Term coined by Aubrey de Grey (biogerontologist) in 2004 paper; concept from David Gobel (Methuselah Foundation) Current progress: Science adds  3 months to lifespan per year; LEV requires adding \>1 year per year Sinclair (Harvard): "There is no biological upper limit to age" - first person to live to 150 may already be born De Grey: 50% chance of reaching LEV by mid-to-late 2030s; SENS approach = damage repair rather than slowing damage Kurzweil (2024): LEV by 2029-2035, AI will simulate biological processes to accelerate solutions George Church: LEV "in a decade or two" via age-reversal clinical trials Natural lifespan cap:  120-150 years (Jeanne Calment record: 122); engineering approach could bypass via damage repair Key mechanisms: Epigenetic reprogramming, senolytic drugs, stem cell therapy, gene therapy, AI-driven drug discovery Current record: Jeanne Calment (122 years, 164 days) - record unbroken since 1997 Note: LEV is theoretical but increasingly plausible given demonstrated age reversal in mice (109% lifespan extension) and human cells (30-year epigenetic age reversal) Additional sources: https://en.wikipedia.org/wiki/Longevity_escape_velocity \| https://pmc.ncbi.nlm.nih.gov/articles/PMC423155/ \| https://www.popularmechanics.com/science/a36712084/can-science-cure-death-longevity/ \| https://www.diamandis.com/blog/longevity-escape-velocity* . 95\. Ritchie, H. & Roser, M. [Half of the world’s habitable land is used for agriculture](https://ourworldindata.org/global-land-for-agriculture). (2019). 96\. Poore, J. & Nemecek, T. [Reducing food’s environmental impacts through producers and consumers](https://doi.org/10.1126/science.aaq0216). *Science* **360**, 987–992 (2018). 97\. OpenSecrets. Lobbyist statistics for washington d.c. *OpenSecrets: Lobbying in US* *Registered lobbyists: Over 12,000 (some estimates); 12,281 registered (2013) Former government employees as lobbyists: 2,200+ former federal employees (1998-2004), including 273 former White House staffers,  250 former Congress members & agency heads Congressional revolving door: 43% (86 of 198) lawmakers who left 1998-2004 became lobbyists; currently 59% leaving to private sector work for lobbying/consulting firms/trade groups Executive branch: 8% were registered lobbyists at some point before/after government service Additional sources: https://en.wikipedia.org/wiki/Lobbying_in_the_United_States \| https://www.opensecrets.org/revolving-door \| https://www.citizen.org/article/revolving-congress/ \| https://www.propublica.org/article/we-found-a-staggering-281-lobbyists-whove-worked-in-the-trump-administration* . 98\. Gosse, M. E. Assessing cost-effectiveness in healthcare: History of the \$50,000 per QALY threshold. *Sustainability Impact Metrics* (2008). 99\. National Institutes of Health BRAIN Initiative. [BRAIN 2025: A scientific vision](https://braininitiative.nih.gov/vision/nih-brain-initiative-reports/brain-2025-scientific-vision). (2014). 100\. Congressional Research Service. *Advanced Gene Editing: CRISPR-Cas9*. (2018). 101\. NHGRI. Human genome project and CRISPR discovery. *NHGRI* (2003) *Your DNA is 3 billion base pairs Read the entire code (Human Genome Project, completed 2003) Learned to edit it (CRISPR, discovered 2012) Additional sources: https://www.genome.gov/11006929/2003-release-international-consortium-completes-hgp \| https://www.nobelprize.org/prizes/chemistry/2020/press-release/* . 102\. U.S. Government Accountability Office. *Electronic Health Records: First Year of CMS’s Incentive Programs Shows Opportunities to Improve Processes to Verify Providers Met Requirements*. (2012). 103\. U.S. Government Accountability Office. *Operation Warp Speed Vaccine Candidate Awards Potential Value*. (2020). 104\. PCORnet. [PCORnet quarterly progress report, Q4 2025](https://pcornet.org/wp-content/uploads/2026/01/EXTERNAL_PCORnetDashboard.Q4.v2.FINAL_.pdf). (2026). 105\. Jones, G. & Schneider, W. J. [Intelligence, human capital, and economic growth: A Bayesian averaging of classical estimates (BACE) approach](https://doi.org/10.1007/s10887-006-7407-2). *Journal of Economic Growth* **11**, 71–93 (2006) *IQ was significant at the 95% level in 99.8% of 1,330 BACE growth regressions. A 1 point increase in a nation’s average IQ is associated with a persistent 0.11% annual increase in GDP per capita.* 106\. Calculated from Orphanet Journal of Rare Diseases (2024). Diseases getting first effective treatment each year. *Calculated from Orphanet Journal of Rare Diseases (2024)* (2024) *Under the current system, approximately 10-15 diseases per year receive their FIRST effective treatment. Calculation: 5% of 7,000 rare diseases ( 350) have FDA-approved treatment, accumulated over 40 years of the Orphan Drug Act =  9 rare diseases/year. Adding  5-10 non-rare diseases that get first treatments yields  10-20 total. FDA approves  50 drugs/year, but many are for diseases that already have treatments (me-too drugs, second-line therapies). Only  15 represent truly FIRST treatments for previously untreatable conditions.* 107\. NIH. NIH budget (FY 2025). *NIH* (2024) *The budget total of \$47.7 billion also includes \$1.412 billion derived from PHS Evaluation financing... Additional sources: https://www.nih.gov/about-nih/organization/budget \| https://officeofbudget.od.nih.gov/* . 108\. Bentley et al. NIH spending on clinical trials:  3.3%. *Bentley et al.* (2023) *NIH spent \$8.1 billion on clinical trials for approved drugs (2010-2019), representing 3.3% of relevant NIH spending. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10349341/ \| https://catalyst.harvard.edu/news/article/nih-spent-8-1b-for-phased-clinical-trials-of-drugs-approved-2010-19-10-of-reported-industry-spending/* . 109\. PMC. Standard medical research ROI (\$20k-\$100k/QALY). *PMC: Cost-effectiveness Thresholds Used by Study Authors* (1990) *Typical cost-effectiveness thresholds for medical interventions in rich countries range from \$50,000 to \$150,000 per QALY. The Institute for Clinical and Economic Review (ICER) uses a \$100,000-\$150,000/QALY threshold for value-based pricing. Between 1990-2021, authors increasingly cited \$100,000 (47% by 2020-21) or \$150,000 (24% by 2020-21) per QALY as benchmarks for cost-effectiveness. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ \| https://icer.org/our-approach/methods-process/cost-effectiveness-the-qaly-and-the-evlyg/* . 110\. Xia et al., Nature Food. Nuclear winter famine. *Xia et al.* (2022) *We estimate that a nuclear war between the United States and Russia would produce 150 Tg of soot and lead to  5 billion people dying at the end of year 2. Additional sources: https://www.nature.com/articles/s43016-022-00573-0* . 111\. Trials. Patient willingness to participate in clinical trials. *Trials: Patients’ Willingness Survey* *Recent surveys: 49-51% willingness (2020-2022) - dramatic drop from 85% (2019) during COVID-19 pandemic Cancer patients when approached: 88% consented to trials (Royal Marsden Hospital) Study type variation: 44.8% willing for drug trial, 76.2% for diagnostic study Top motivation: "Learning more about my health/medical condition" (67.4%) Top barrier: "Worry about experiencing side effects" (52.6%) Additional sources: https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-1105-3 \| https://www.appliedclinicaltrialsonline.com/view/industry-forced-to-rethink-patient-participation-in-trials \| https://pmc.ncbi.nlm.nih.gov/articles/PMC7183682/* . 112\. The Commune. Pentagon audit failures (\$2.46T unaccounted). *The Commune* (2024) *In the most recent audit, the Department of Defense (DoD) could not account for approximately 60% of its $4.1 trillion in assets, amounting to$2.46 trillion unaccounted for. Alternative title: Pentagon unsupported accounting adjustments ($6.5T, single year, US Army) In 2015, the Department of Defense's Inspector General reported that the Army could not adequately support$6.5 trillion in year-end adjustments, indicating severe accounting discrepancies. Additional sources: https://thecommunemag.com/the-pentagon-misplaced-2-46-trillion-an-in-depth-look-at-the-financial-audit-failures \| https://accmag.com/audit-pentagon-cannot-account-for-6-5-trillion-dollars-is-taxpayer-money/* . 113\. Tufts CSDD. Cost of drug development. *Various estimates suggest \$1.0 - \$2.5 billion to bring a new drug from discovery through FDA approval, spread across  10 years. Tufts Center for the Study of Drug Development often cited for \$1.0 - \$2.6 billion/drug. Industry reports (IQVIA, Deloitte) also highlight \$2+ billion figures.* 114\. Value in Health. Average lifetime revenue per successful drug. *Value in Health: Sales Revenues for New Therapeutic Agents* *Study of 361 FDA-approved drugs from 1995-2014 (median follow-up 13.2 years): Mean lifetime revenue: \$15.2 billion per drug Median lifetime revenue: \$6.7 billion per drug Revenue after 5 years: \$3.2 billion (mean) Revenue after 10 years: \$9.5 billion (mean) Revenue after 15 years: \$19.2 billion (mean) Distribution highly skewed: top 25 drugs (7%) accounted for 38% of total revenue (\$2.1T of \$5.5T) Additional sources: https://www.sciencedirect.com/science/article/pii/S1098301524027542* . 115\. Deloitte. Pharmaceutical r&d return on investment (ROI). *Deloitte: Measuring Pharmaceutical Innovation 2025* (2025) *Deloitte’s annual study of top 20 pharma companies by R&D spend (2010-2024): 2024 ROI: 5.9% (second year of growth after decade of decline) 2023 ROI:  4.3% (estimated from trend) 2022 ROI: 1.2% (historic low since study began, 13-year low) 2021 ROI: 6.8% (record high, inflated by COVID-19 vaccines/treatments) Long-term trend: Declining for over a decade before 2023 recovery Average R&D cost per asset: \$2.3B (2022), \$2.23B (2024) These returns (1.2-5.9% range) fall far below typical corporate ROI targets (15-20%) Additional sources: https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/research/measuring-return-from-pharmaceutical-innovation.html \| https://www.prnewswire.com/news-releases/deloittes-13th-annual-pharmaceutical-innovation-report-pharma-rd-return-on-investment-falls-in-post-pandemic-market-301738807.html \| https://hitconsultant.net/2023/02/16/pharma-rd-roi-falls-to-lowest-level-in-13-years/* . 116\. Nature Reviews Drug Discovery. Drug trial success rate from phase i to approval. *Nature Reviews Drug Discovery: Clinical Success Rates* (2016) *Overall Phase I to approval: 10-12.8% (conventional wisdom  10%, studies show 12.8%) Recent decline: Average LOA now 6.7% for Phase I (2014-2023 data) Leading pharma companies: 14.3% average LOA (range 8-23%) Varies by therapeutic area: Oncology 3.4%, CNS/cardiovascular lowest at Phase III Phase-specific success: Phase I 47-54%, Phase II 28-34%, Phase III 55-70% Note: 12% figure accurate for historical average. Recent data shows decline to 6.7%, with Phase II as primary attrition point (28% success) Additional sources: https://www.nature.com/articles/nrd.2016.136 \| https://pmc.ncbi.nlm.nih.gov/articles/PMC6409418/ \| https://academic.oup.com/biostatistics/article/20/2/273/4817524* . 117\. SofproMed. Phase 3 cost per trial range. *SofproMed* *Phase 3 clinical trials cost between \$20 million and \$282 million per trial, with significant variation by therapeutic area and trial complexity. Additional sources: https://www.sofpromed.com/how-much-does-a-clinical-trial-cost \| https://www.cbo.gov/publication/57126* . 118\. Ramsberg, J. & Platt, R. Pragmatic trial cost per patient (median \$97). *Learning Health Systems* (2018) *Meta-analysis of 108 embedded pragmatic clinical trials (2006-2016). The median cost per patient was \$97 (IQR \$19–\$478), based on 2015 dollars. 25% of trials cost \<\$19/patient; 10 trials exceeded \$1,000/patient. U.S. studies median \$187 vs non-U.S. median \$27. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/* . 119\. ICRC. International campaign to ban landmines (ICBL) - ottawa treaty (1997). *ICRC* (1997) *ICBL: Founded 1992 by 6 NGOs (Handicap International, Human Rights Watch, Medico International, Mines Advisory Group, Physicians for Human Rights, Vietnam Veterans of America Foundation) Started with ONE staff member: Jody Williams as founding coordinator Grew to 1,000+ organizations in 60 countries by 1997 Ottawa Process: 14 months (October 1996 - December 1997) Convention signed by 122 states on December 3, 1997; entered into force March 1, 1999 Achievement: Nobel Peace Prize 1997 (shared by ICBL and Jody Williams) Government funding context: Canada established \$100M CAD Canadian Landmine Fund over 10 years (1997); International donors provided \$169M in 1997 for mine action (up from \$100M in 1996) Additional sources: https://www.icrc.org/en/doc/resources/documents/article/other/57jpjn.htm \| https://en.wikipedia.org/wiki/International_Campaign_to_Ban_Landmines \| https://www.nobelprize.org/prizes/peace/1997/summary/ \| https://un.org/press/en/1999/19990520.MINES.BRF.html \| https://www.the-monitor.org/en-gb/reports/2003/landmine-monitor-2003/mine-action-funding.aspx* . 120\. OpenSecrets. [Revolving door: Former members of congress](https://www.opensecrets.org/revolving-door). (2024) *388 former members of Congress are registered as lobbyists. Nearly 5,400 former congressional staffers have left Capitol Hill to become federal lobbyists in the past 10 years. Additional sources: https://www.opensecrets.org/revolving-door* . 121\. Kinch, M. S. & Griesenauer, R. H. [Lost medicines: A longer view of the pharmaceutical industry with the potential to reinvigorate discovery](https://doi.org/10.1016/j.drudis.2019.01.009). *Drug Discovery Today* **24**, 875–880 (2019) *Research identified 1,600+ medicines available in 1962. The 1950s represented industry high-water mark with \>30 new products in five of ten years; this rate would not be replicated until late 1990s. More than half (880) of these medicines were lost following implementation of Kefauver-Harris Amendment. The peak of 1962 would not be seen again until early 21st century. By 2016 number of organizations actively involved in R&D at level not seen since 1914.* 122\. Think by Numbers. Pre-1962 physician-led clinical trials. *Think by Numbers: How Many Lives Does FDA Save?* (1966) *Pre-1962: Physicians could report real-world evidence directly 1962 Drug Amendments replaced "premarket notification" with "premarket approval", requiring extensive efficacy testing Impact: New regulatory clampdown reduced new treatment production by 70%; lifespan growth declined from  4 years/decade to  2 years/decade Drug Efficacy Study Implementation (DESI): NAS/NRC evaluated 3,400+ drugs approved 1938-1962 for safety only; reviewed \>3,000 products, \>16,000 therapeutic claims FDA has had authority to accept real-world evidence since 1962, clarified by 21st Century Cures Act (2016) Note: Specific "144,000 physicians" figure not verified in sources Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ \| https://www.fda.gov/drugs/enforcement-activities-fda/drug-efficacy-study-implementation-desi \| http://www.nasonline.org/about-nas/history/archives/collections/des-1966-1969-1.html* . 123\. GAO. 95% of diseases have 0 FDA-approved treatments. *GAO* (2025) *95% of diseases have no treatment Additional sources: https://www.gao.gov/products/gao-25-106774 \| https://globalgenes.org/rare-disease-facts/* . 124\. Oren Cass, Manhattan Institute. RECOVERY trial cost per patient. *Oren Cass* (2023) *The RECOVERY trial, for example, cost only about \$500 per patient... By contrast, the median per-patient cost of a pivotal trial for a new therapeutic is around \$41,000. Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs* . 125\. NHS England; Águas et al. RECOVERY trial global lives saved ( 1 million). *NHS England: 1 Million Lives Saved* (2021) *Dexamethasone saved  1 million lives worldwide (NHS England estimate, March 2021, 9 months after discovery). UK alone: 22,000 lives saved. Methodology: Águas et al. Nature Communications 2021 estimated 650,000 lives (range: 240,000-1,400,000) for July-December 2020 alone, based on RECOVERY trial mortality reductions (36% for ventilated, 18% for oxygen-only patients) applied to global COVID hospitalizations. June 2020 announcement: Dexamethasone reduced deaths by up to 1/3 (ventilated patients), 1/5 (oxygen patients). Impact immediate: Adopted into standard care globally within hours of announcement. Additional sources: https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ \| https://www.nature.com/articles/s41467-021-21134-2 \| https://pharmaceutical-journal.com/article/news/steroid-has-saved-the-lives-of-one-million-covid-19-patients-worldwide-figures-show \| https://www.recoverytrial.net/news/recovery-trial-celebrates-two-year-anniversary-of-life-saving-dexamethasone-result* . 126\. National September 11 Memorial & Museum. [September 11 attack facts](https://www.911memorial.org/911-faqs). (2024) *2,977 people were killed in the September 11, 2001 attacks: 2,753 at the World Trade Center, 184 at the Pentagon, and 40 passengers and crew on United Flight 93 in Shanksville, Pennsylvania.* 127\. World Bank. [Market capitalization of listed domestic companies (current US\$)](https://data.worldbank.org/indicator/CM.MKT.LCAP.CD). (2026). 128\. Anthropic. [Pricing](https://platform.claude.com/docs/en/about-claude/pricing). (2026). 129\. World Health Organization. [WHO life expectancy data by country](https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-life-expectancy-and-healthy-life-expectancy). (2024) *Life expectancy at birth varies significantly among developed nations: Switzerland: 84.0 years (2023) Singapore: 84.1 years (2023) Japan: 84.3 years (2023) United States: 77.5 years (2023) - 6.5 years below Switzerland, Singapore Global average:  73 years Note: US spends more per capita on healthcare than any other nation, yet achieves lower life expectancy Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-life-expectancy-and-healthy-life-expectancy* . 130\. CSIS. Smallpox eradication ROI. *CSIS* . 131\. ICER. Value per QALY (standard economic value). *ICER* (2024) *Standard economic value per QALY: \$100,000–\$150,000. This is the US and global standard willingness-to-pay threshold for interventions that add costs. Dominant interventions (those that save money while improving health) are favorable regardless of this threshold. Additional sources: https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf* . 132\. GAO. Annual cost of u.s. Sugar subsidies. *GAO: Sugar Program* *Consumer costs: \$2.5-3.5 billion per year (GAO estimate) Net economic cost:  \$1 billion per year 2022: US consumers paid 2X world price for sugar Program costs \$3-4 billion/year but no federal budget impact (costs passed directly to consumers via higher prices) Employment impact: 10,000-20,000 manufacturing jobs lost annually in sugar-reliant industries (confectionery, etc.) Multiple studies confirm: Sweetener Users Association (\$2.9-3.5B), AEI (\$2.4B consumer cost), Beghin & Elobeid (\$2.9-3.5B consumer surplus) Additional sources: https://www.gao.gov/products/gao-24-106144 \| https://www.heritage.org/agriculture/report/the-us-sugar-program-bad-consumers-bad-agriculture-and-bad-america \| https://www.aei.org/articles/the-u-s-spends-4-billion-a-year-subsidizing-stalinist-style-domestic-sugar-production/* . 133\. World Bank. Swiss military budget as percentage of GDP. *World Bank: Military Expenditure* *2023: 0.70272% of GDP (World Bank) 2024: CHF 5.95 billion official military spending When including militia system costs:  1% GDP (CHF 8.75B) Comparison: Near bottom in Europe; only Ireland, Malta, Moldova spend less (excluding microstates with no armies) Additional sources: https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?locations=CH \| https://www.avenir-suisse.ch/en/blog-defence-spending-switzerland-is-in-better-shape-than-it-seems/ \| https://tradingeconomics.com/switzerland/military-expenditure-percent-of-gdp-wb-data.html* . 134\. World Bank. Switzerland vs. US GDP per capita comparison. *World Bank: Switzerland GDP Per Capita* *2024 GDP per capita (PPP-adjusted): Switzerland \$93,819 vs United States \$75,492 Switzerland’s GDP per capita 24% higher than US when adjusted for purchasing power parity Nominal 2024: Switzerland \$103,670 vs US \$85,810 Additional sources: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH \| https://tradingeconomics.com/switzerland/gdp-per-capita-ppp \| https://www.theglobaleconomy.com/USA/gdp_per_capita_ppp/* . 135\. Wikipedia. Thalidomide scandal: Worldwide cases and mortality. *Wikipedia* *The total number of embryos affected by the use of thalidomide during pregnancy is estimated at 10,000, of whom about 40% died around the time of birth. More than 10,000 children in 46 countries were born with deformities such as phocomelia. Additional sources: https://en.wikipedia.org/wiki/Thalidomide_scandal* . 136\. PLOS One. Health and quality of life of thalidomide survivors as they age. *PLOS One* (2019) *Study of thalidomide survivors documenting ongoing disability impacts, quality of life, and long-term health outcomes. Survivors (now in their 60s) continue to experience significant disability from limb deformities, organ damage, and other effects. Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210222* . 137\. US Census Bureau. Historical world population estimates. *US Census Bureau* *US Census Bureau historical estimates of world population by country and region (1950-2050). US population in 1960:  180 million of  3 billion worldwide (6%). Additional sources: https://www.census.gov/data/tables/time-series/demo/international-programs/historical-est-worldpop.html* . 138\. FDA Study via NCBI. Trial costs, FDA study. *FDA Study via NCBI* *Overall, the 138 clinical trials had an estimated median (IQR) cost of \$19.0 million (\$12.2 million-\$33.1 million)... The clinical trials cost a median (IQR) of \$41,117 (\$31,802-\$82,362) per patient. Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/* . 139\. GBD 2019 Diseases and Injuries Collaborators. [Global burden of disease study 2019: Disability weights](https://doi.org/10.1016/S0140-6736(20)30925-9). *The Lancet* **396**, 1204–1222 (2020) *Disability weights for 235 health states used in Global Burden of Disease calculations. Weights range from 0 (perfect health) to 1 (death equivalent). Chronic conditions like diabetes (0.05-0.35), COPD (0.04-0.41), depression (0.15-0.66), and cardiovascular disease (0.04-0.57) show substantial variation by severity. Treatment typically reduces disability weights by 50-80 percent for manageable chronic conditions.* 140\. WHO. Annual global economic burden of alzheimer’s and other dementias. *WHO: Dementia Fact Sheet* (2019) *Global cost: \$1.3 trillion (2019 WHO-commissioned study) 50% from informal caregivers (family/friends,  5 hrs/day) 74% of costs in high-income countries despite 61% of patients in LMICs \$818B (2010) → \$1T (2018) → \$1.3T (2019) - rapid growth Note: Costs increased 35% from 2010-2015 alone. Informal care represents massive hidden economic burden Additional sources: https://www.who.int/news-room/fact-sheets/detail/dementia \| https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12901* . 141\. JAMA Oncology. Annual global economic burden of cancer. *JAMA Oncology: Global Cost 2020-2050* (2020) *2020-2050 projection: \$25.2 trillion total (\$840B/year average) 2010 annual cost: \$1.16 trillion (direct costs only) Recent estimate:  \$3 trillion/year (all costs included) Top 5 cancers: lung (15.4%), colon/rectum (10.9%), breast (7.7%), liver (6.5%), leukemia (6.3%) Note: China/US account for 45% of global burden; 75% of deaths in LMICs but only 50.0% of economic cost Additional sources: https://jamanetwork.com/journals/jamaoncology/fullarticle/2801798 \| https://www.nature.com/articles/d41586-023-00634-9* . 142\. CDC. U.s. Chronic disease healthcare spending. *CDC* *Chronic diseases account for  90% of U.S. healthcare spending ( \$3.7T/year). Additional sources: https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html* . 143\. Diabetes Care. Annual global economic burden of diabetes. *Diabetes Care: Global Economic Burden* *2015: \$1.3 trillion (1.8% of global GDP) 2030 projections: \$2.1T-2.5T depending on scenario IDF health expenditure: \$760B (2019) → \$845B (2045 projected) 2/3 direct medical costs (\$857B), 1/3 indirect costs (lost productivity) Note: Costs growing rapidly; expected to exceed \$2T by 2030 Additional sources: https://diabetesjournals.org/care/article/41/5/963/36522/Global-Economic-Burden-of-Diabetes-in-Adults \| https://doi.org/10.1016/S2213-8587(17)30097-9* . 144\. CBO. *The 2024 Long-Term Budget Outlook*. (2024). 145\. World Bank, Bureau of Economic Analysis. US GDP 2024 (\$28.78 trillion). *World Bank* (2024) *US GDP reached \$28.78 trillion in 2024, representing approximately 26% of global GDP. Additional sources: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US \| https://www.bea.gov/news/2024/gross-domestic-product-fourth-quarter-and-year-2024-advance-estimate* . 146\. Environmental Working Group. US farm subsidy database and analysis. *Environmental Working Group* (2024) *US agricultural subsidies total approximately \$30 billion annually, but create much larger economic distortions. Top 10% of farms receive 78% of subsidies, benefits concentrated in commodity crops (corn, soy, wheat, cotton), environmental damage from monoculture incentivized, and overall deadweight loss estimated at \$50-120 billion annually. Additional sources: https://farm.ewg.org/ \| https://www.ers.usda.gov/topics/farm-economy/farm-sector-income-finances/government-payments-the-safety-net/* . 147\. Drug Policy Alliance. [The drug war by the numbers](https://drugpolicy.org/drug-war-stats/). (2021) *Since 1971, the war on drugs has cost the United States an estimated \$1 trillion in enforcement. The federal drug control budget was \$41 billion in 2022. Mass incarceration costs the U.S. at least \$182 billion every year, with over \$450 billion spent to incarcerate individuals on drug charges in federal prisons.* 148\. International Monetary Fund. [IMF fossil fuel subsidies data: 2023 update](https://www.imf.org/en/Blogs/Articles/2023/08/24/fossil-fuel-subsidies-surged-to-record-7-trillion). (2023) *Globally, fossil fuel subsidies were \$7 trillion in 2022 or 7.1 percent of GDP. The United States subsidies totaled \$649 billion. Underpricing for local air pollution costs and climate damages are the largest contributor, accounting for about 30 percent each.* 149\. Papanicolas, Irene et al. Health care spending in the united states and other high-income countries. *Papanicolas et al.* (2018) *The US spent approximately twice as much as other high-income countries on medical care (mean per capita: \$9,892 vs \$5,289), with similar utilization but much higher prices. Administrative costs accounted for 8% of US spending vs 1-3% in other countries. US spending on pharmaceuticals was \$1,443 per capita vs \$749 elsewhere. Despite spending more, US health outcomes are not better. Additional sources: https://jamanetwork.com/journals/jama/article-abstract/2674671* . 150\. Hsieh, C.-T. & Moretti, E. Housing constraints and spatial misallocation. *American Economic Journal: Macroeconomics* (2019) *We quantify the amount of spatial misallocation of labor across US cities and its aggregate costs. Tight land-use restrictions in high-productivity cities like New York, San Francisco, and Boston lowered aggregate US growth by 36% from 1964 to 2009. Local constraints on housing supply have had enormous effects on the national economy. Additional sources: https://www.aeaweb.org/articles?id=10.1257/mac.20170388* . 151\. Yale Budget Lab. [The fiscal, economic, and distributional effects of all u.s. tariffs](https://budgetlab.yale.edu/research/where-we-stand-fiscal-economic-and-distributional-effects-all-us-tariffs-enacted-2025-through-april). (2025) *Accounting for all the 2025 US tariffs and retaliation implemented to date, the level of real GDP is persistently -0.6% smaller in the long run, the equivalent of \$160 billion 2024\$ annually.* 152\. Tax Foundation. Tax compliance costs the US economy \$546 billion annually. (2024) *Americans will spend over 7.9 billion hours complying with IRS tax filing and reporting requirements in 2024. This costs the economy roughly \$413 billion in lost productivity. In addition, the IRS estimates that Americans spend roughly \$133 billion annually in out-of-pocket costs, bringing the total compliance costs to \$546 billion, or nearly 2 percent of GDP.* 153\. Cook, C., Cole, G., Asaria, P., Jabbour, R. & Francis, D. P. Annual global economic burden of heart disease. *International Journal of Cardiology* (2014) *Heart failure alone: \$108 billion/year (2012 global analysis, 197 countries) US CVD: \$555B (2016) → projected \$1.8T by 2050 LMICs total CVD loss: \$3.7T cumulative (2011-2015, 5-year period) CVD is costliest disease category in most developed nations Note: No single \$2.1T global figure found; estimates vary widely by scope and year Additional sources: https://www.ahajournals.org/doi/10.1161/CIR.0000000000001258* . 154\. Manuel, D. U.s. Defense spending history: 100 years of military budgets. *DaveManuel.com* (2025) *US military spending in constant 2024 dollars: 1939 \$29B (pre-WW2 baseline), 1940 \$37B, 1944 \$1,383B, 1945 \$1,420B (peak), 1946 \$674B, 1947 \$176B, 1948 \$117B, 2024 \$886B. The post-WW2 demobilization cut spending 88% in two years (1945-1947). Current peacetime spending (\$886B) is 30x the pre-WW2 baseline and 62% of peak WW2 spending, in inflation-adjusted dollars.* 155\. Statista. US military budget as percentage of GDP. *Statista* (2024) *U.S. military spending amounted to 3.5% of GDP in 2024. In 2024, the U.S. spent nearly \$1 trillion on its military budget, equal to 3.4% of GDP. Additional sources: https://www.statista.com/statistics/262742/countries-with-the-highest-military-spending/ \| https://www.sipri.org/sites/default/files/2025-04/2504_fs_milex_2024.pdf* . 156\. US Census Bureau. Number of registered or eligible voters in the u.s. *US Census Bureau* (2024) *73.6% (or 174 million people) of the citizen voting-age population was registered to vote in 2024 (Census Bureau). More than 211 million citizens were active registered voters (86.6% of citizen voting age population) according to the Election Assistance Commission. Additional sources: https://www.census.gov/newsroom/press-releases/2025/2024-presidential-election-voting-registration-tables.html \| https://www.eac.gov/news/2025/06/30/us-election-assistance-commission-releases-2024-election-administration-and-voting* . 157\. U.S. Senate. Treaties. *U.S. Senate* *The Constitution provides that the president ’shall have Power, by and with the Advice and Consent of the Senate, to make Treaties, provided two-thirds of the Senators present concur’ (Article II, section 2). Treaties are formal agreements with foreign nations that require two-thirds Senate approval. 67 senators (two-thirds of 100) must vote to ratify a treaty for it to take effect. Additional sources: https://www.senate.gov/about/powers-procedures/treaties.htm* . 158\. Mexico News Daily. [Survey finds mexican migrants pay average of US \$7,000 to ’coyotes’](https://mexiconewsdaily.com/news/survey-finds-mexican-migrants-pay-average-of-us-7000-to-coyotes/). (2023). 159\. Federal Election Commission. [Statistical summary of 24-month campaign activity of the 2023-2024 election cycle](https://www.fec.gov/updates/statistical-summary-of-24-month-campaign-activity-of-the-2023-2024-election-cycle/). (2023) *Presidential candidates raised \$2 billion; House and Senate candidates raised \$3.8 billion and spent \$3.7 billion; PACs raised \$15.7 billion and spent \$15.5 billion. Total federal campaign spending approximately \$20 billion. Additional sources: https://www.fec.gov/updates/statistical-summary-of-24-month-campaign-activity-of-the-2023-2024-election-cycle/* . 160\. OpenSecrets. [Federal lobbying hit record \$4.4 billion in 2024](https://www.opensecrets.org/news/2025/02/federal-lobbying-set-new-record-in-2024/). (2024) *Total federal lobbying reached record \$4.4 billion in 2024. The \$150 million increase in lobbying continues an upward trend that began in 2016. Additional sources: https://www.opensecrets.org/news/2025/02/federal-lobbying-set-new-record-in-2024/* . 161\. Columbia/NBER. Odds of a single vote being decisive in a u.s. Presidential election. *Columbia/NBER: What Is the Probability Your Vote Will Make a Difference?* (2012) *National average: 1 in 60 million chance (2008 election analysis by Gelman, Silver, Edlin) Swing states (NM, VA, NH, CO):  1 in 10 million chance Non-competitive states: 34 states \>1 in 100 million odds; 20 states \>1 in 1 billion Washington DC: 1 in 490 billion odds Methodology: Probability state is necessary for electoral college win × probability state vote is tied Additional sources: https://sites.stat.columbia.edu/gelman/research/published/probdecisive2.pdf \| https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1465-7295.2010.00272.x* . 162\. Hutchinson and Kirk. [Valley of death in drug development](https://pmc.ncbi.nlm.nih.gov/articles/PMC3324971/). (2011) *The overall failure rate of drugs that passed into Phase 1 trials to final approval is 90%. This lack of translation from promising preclinical findings to success in human trials is known as the "valley of death." Estimated 30-50% of promising compounds never proceed to Phase 2/3 trials primarily due to funding barriers rather than scientific failure. The late-stage attrition rate for oncology drugs is as high as 70% in Phase II and 59% in Phase III trials.* 163\. DOT. DOT value of statistical life (\$13.6M). *DOT: VSL Guidance 2024* (2024) *Current VSL (2024): \$13.7 million (updated from \$13.6M) Used in cost-benefit analyses for transportation regulations and infrastructure Methodology updated in 2013 guidance, adjusted annually for inflation and real income VSL represents aggregate willingness to pay for safety improvements that reduce fatalities by one Note: DOT has published VSL guidance periodically since 1993. Current \$13.7M reflects 2024 inflation/income adjustments Additional sources: https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis \| https://www.transportation.gov/regulations/economic-values-used-in-analysis* . 164\. PLOS ONE. Cost per DALY for vitamin a supplementation. *PLOS ONE: Cost-effectiveness of "Golden Mustard" for Treating Vitamin A Deficiency in India (2010)* (2010) *India: \$23-\$50 per DALY averted (least costly intervention, \$1,000-\$6,100 per death averted) Sub-Saharan Africa (2022): \$220-\$860 per DALY (Burkina Faso: \$220, Kenya: \$550, Nigeria: \$860) WHO estimates for Africa: \$40 per DALY for fortification, \$255 for supplementation Uganda fortification: \$18-\$82 per DALY (oil: \$18, sugar: \$82) Note: Wide variation reflects differences in baseline VAD prevalence, coverage levels, and whether intervention is supplementation or fortification Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012046 \| https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266495* . 165\. UN News. Clean water & sanitation (LMICs) ROI. *UN News* (2014). 166\. PMC. Cost-effectiveness threshold (\$50,000/QALY). *PMC* *The \$50,000/QALY threshold is widely used in US health economics literature, originating from dialysis cost benchmarks in the 1980s. In US cost-utility analyses, 77.5% of authors use either \$50,000 or \$100,000 per QALY as reference points. Most successful health programs cost \$3,000-10,000 per QALY. WHO-CHOICE uses GDP per capita multiples (1× GDP/capita = "very cost-effective", 3× GDP/capita = "cost-effective"), which for the US ( \$70,000 GDP/capita) translates to \$70,000-\$210,000/QALY thresholds. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5193154/ \| https://pmc.ncbi.nlm.nih.gov/articles/PMC9278384/* .