Methodology, Parameters, and Calculations
Keywords
health economics methodology, clinical trial cost analysis, medical research ROI, cost-benefit analysis healthcare, sensitivity analysis, Monte Carlo simulation, DALY calculation, pragmatic clinical trials
Overview
This appendix documents all 699 parameters used in the analysis, organized by type:
- External sources (peer-reviewed): 204
- Calculated values: 358
- Core definitions: 137
Calculated Values
Parameters derived from mathematical formulas and economic models.
Apocalypse Markup: $2.72 trillion
Note📊 Details
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 📊: $2.72 trillion
- Price of Apocalypse (Minimum Viable 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
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 the distribution of Apocalypse Markup across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Apocalypse Markup will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Apocalypse Markup Multiplier: 3.6kx
Note📊 Details
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 📊: $2.72 trillion
- Price of Apocalypse (Minimum Viable 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
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 the distribution of Apocalypse Markup Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Apocalypse Markup Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Best-Practice Life Expectancy Gain: 10.7 years
Note📊 Details
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 📊: 84 years
- Singapore Life Expectancy 📊: 84.1 years
- 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
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 the distribution of Best-Practice Life Expectancy Gain across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Best-Practice Life Expectancy Gain will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Bullets Purchasable Per Person Per Year: 850 rounds/person/year
Note📊 Details
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 🔢: 6.8 trillion rounds
- Global Population in 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
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 the distribution of Bullets Purchasable Per Person Per Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Bullets Purchasable Per Person Per Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Engagement Rate: 10%
Note📊 Details
Probability someone engages with the idea (1 - dismissal rate)
Inputs:
- Dismissal Rate: 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
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 the distribution of Engagement Rate across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Engagement Rate will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Expected Engaged Implementers: 3.48 people
Note📊 Details
Expected number of implementers who engage (orbit reached x engagement rate x implementer count)
Inputs:
- Implementer Orbit Size: 1,000 people (95% CI: 150 people - 5,000 people)
- Effective R: 0.15:1 (95% CI: 0.04:1 - 0.8:1)
- Initial Audience: 50,000 people (95% CI: 10,000 people - 500 thousand people)
- Engagement Rate 🔢: 10%
- Potential Implementers 🔢: 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
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 the distribution of Expected Engaged Implementers across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Engaged Implementers will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Potential Implementers: 2,976 people
Note📊 Details
Total potential implementers (billionaires + world leaders)
Inputs:
- Global Billionaire Count 📊: 2,781 people
- 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📊 Details
Probability at least one implementer engages (information diffusion only; dominant strategy proof handles action)
Inputs:
- 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
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 the distribution of P(At Least One Engages) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that P(At Least One Engages) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Implementer Orbit Reach Probability: 1.17%
Note📊 Details
Probability a given implementer’s information orbit is reached by the content cascade
Inputs:
- Implementer Orbit Size: 1,000 people (95% CI: 150 people - 5,000 people)
- Effective R: 0.15:1 (95% CI: 0.04:1 - 0.8:1)
- 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
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 the distribution of Implementer Orbit Reach Probability across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Implementer Orbit Reach Probability will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
P(No Implementer Engages): 3.08%
Note📊 Details
Probability that NO implementer engages (all orbits missed or all dismiss)
Inputs:
- Implementer Orbit Size: 1,000 people (95% CI: 150 people - 5,000 people)
- Effective R: 0.15:1 (95% CI: 0.04:1 - 0.8:1)
- Initial Audience: 50,000 people (95% CI: 10,000 people - 500 thousand people)
- Engagement Rate 🔢: 10%
- Potential Implementers 🔢: 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
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 the distribution of P(No Implementer Engages) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that P(No Implementer Engages) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Chronic Disease Patients Treated: 982 million people
Note📊 Details
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 📊: 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:54
? 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
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 the distribution of Annual Chronic Disease Patients Treated across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Chronic Disease Patients Treated will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Combination Therapy Space: 45.1 billion combinations
Note📊 Details
Total combination therapy space (pairwise drug combinations × diseases). Standard in oncology, HIV, cardiology.
Inputs:
- Pairwise Drug Combinations 🔢: 45.1 million combinations
- Trial-Relevant Diseases: 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
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 the distribution of Combination Therapy Space across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Combination Therapy Space will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pairwise Drug Combinations: 45.1 million combinations
Note📊 Details
Unique pairwise drug combinations from known safe compounds (n choose 2)
Inputs:
- Safe Compounds Available for Testing: 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
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 the distribution of Pairwise Drug Combinations across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pairwise Drug Combinations will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
DALYs Averted per Percentage Point: 5.65 billion DALYs
Note📊 Details
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 🔢: 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
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 the distribution of DALYs Averted per Percentage Point across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that DALYs Averted per Percentage Point will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Contribution EV per Percentage Point (Treaty): $5,189
Note📊 Details
Personal expected value per percentage point of implementation probability shift under Treaty Trajectory. One percent of the per-capita lifetime income gain.
Inputs:
\[ \begin{gathered} EV_{pp,treaty} \\ = \Delta Y_{lifetime,treaty} \times 0.01 \\ = \$519K \times 0.01 \\ = \$5.19K \end{gathered} \] 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
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 the distribution of Contribution EV per Percentage Point (Treaty) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Treaty) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Contribution EV per Percentage Point (Treaty, Blended): $29,339
Note📊 Details
Blended personal expected value per percentage point of implementation probability shift under Treaty Trajectory.
Inputs:
- Treaty Personal Upside (Blended) 🔢: $2.93 million
\[ \begin{gathered} EV_{pp,treaty,blend} \\ = Upside_{blend,treaty} \times 0.01 \\ = \$2.93M \times 0.01 \\ = \$29.3K \end{gathered} \] where: \[ \begin{gathered} Upside_{blend,treaty} \\ = \Delta Y_{lifetime,treaty} + Value_{HALE,treaty} \\ = \$519K + \$2.42M \\ = \$2.93M \end{gathered} \] 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: \[ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} \] 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, Blended)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Personal Upside (Blended) (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
Simulation Results Summary: Contribution EV per Percentage Point (Treaty, Blended)
| 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 the distribution of Contribution EV per Percentage Point (Treaty, Blended) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Treaty, Blended) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Contribution EV per Percentage Point (Wishonia): $364,454
Note📊 Details
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) 🔢: $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
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 the distribution of Contribution EV per Percentage Point (Wishonia) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Wishonia) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Contribution EV per Percentage Point (Wishonia, Blended): $404,654
Note📊 Details
Blended personal expected value per percentage point of implementation probability shift under Wishonia Trajectory.
Inputs:
- Wishonia Personal Upside (Blended) 🔢: $40.5 million
\[ \begin{gathered} EV_{pp,wish,blend} \\ = Upside_{blend,wish} \times 0.01 \\ = \$40.5M \times 0.01 \\ = \$405K \end{gathered} \] where: \[ \begin{gathered} Upside_{blend,wish} \\ = \Delta Y_{lifetime,wish} + Value_{HALE,wish} \\ = \$36.4M + \$4.02M \\ = \$40.5M \end{gathered} \] 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: \[ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{wish,extra,15} \end{gathered} \] 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, Blended)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Wishonia Personal Upside (Blended) (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
Simulation Results Summary: Contribution EV per Percentage Point (Wishonia, Blended)
| 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 the distribution of Contribution EV per Percentage Point (Wishonia, Blended) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Contribution EV per Percentage Point (Wishonia, Blended) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Lives Saved per Percentage Point: 107 million lives
Note📊 Details
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 🔢: 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
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 the distribution of Lives Saved per Percentage Point across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Lives Saved per Percentage Point will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Suffering Hours Prevented per Percentage Point: 19.3 trillion hours
Note📊 Details
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 🔢: 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
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 the distribution of Suffering Hours Prevented per Percentage Point across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Suffering Hours Prevented per Percentage Point will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Conventional Retirement Horizon Multiple: 2.57x
Note📊 Details
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) 📊: 6.5% (95% CI: 5% - 8%)
- Year Destructive Economy Reaches 50% of GDP 🔢: 2040
- 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
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 the distribution of Conventional Retirement Horizon Multiple across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Conventional Retirement Horizon Multiple will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Drugs Never Developed VSL: $3 quadrillion
Note📊 Details
Corporate-defendant wrongful-death valuation for the aggressive prosecutor estimate of deaths from drugs never developed.
Inputs:
- Corporate Damages Drugs Never Developed Deaths: 300 million deaths
- 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
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 the distribution of Corporate Damages Drugs Never Developed VSL across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Drugs Never Developed VSL will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Efficacy Lag Deaths VSL: $1.02 quadrillion
Note📊 Details
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 🔢: 102 million deaths
- 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
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 the distribution of Corporate Damages Efficacy Lag Deaths VSL across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Efficacy Lag Deaths VSL will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Forward Settlement Value Per Capita: $10.6 million
Note📊 Details
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 🔢: $84.8 quadrillion
- Global Population in 2024 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people)
\[ \begin{gathered} V_{settlement,pc} \\ = \frac{Value_{max}}{Pop_{global}} \\ = \frac{\$84800T}{8B} \\ = \$10.6M \end{gathered} \] where: \[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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 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) | 0.9996 | Strong driver |
| Global Population in 2024 (of people) | -0.0289 | 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
Simulation Results Summary: Corporate Damages Forward Settlement Value Per Capita
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $10.6 million |
| Mean (expected value) | $11.9 million |
| Median (50th percentile) | $11 million |
| Standard Deviation | $5.03 million |
| 90% Range (5th-95th percentile) | [$5.36 million, $21.4 million] |
The histogram shows the distribution of Corporate Damages Forward Settlement Value Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Forward Settlement Value Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Pentagon FCA Penalty Increment: $4.92 trillion
Note📊 Details
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 🔢: $7.38 trillion
- 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📊 Details
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: $45 trillion (95% CI: $30 trillion - $60 trillion)
- Cumulative Environmental Destruction from War 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
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 the distribution of Corporate Damages Property Plus Environmental Destruction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Property Plus Environmental Destruction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Base Ask Per Capita: $913,219
Note📊 Details
Aggressive corporate-liability prosecutor base ask per living human.
Inputs:
- Corporate Damages Prosecutor Base Ask Total 🔢: $7.31 quadrillion
- Global Population in 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
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 the distribution of Corporate Damages Prosecutor Base Ask Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Base Ask Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Base Ask Total: $7.31 quadrillion
Note📊 Details
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 🔢: $4.31 quadrillion
- 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
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 the distribution of Corporate Damages Prosecutor Base Ask Total across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Base Ask Total will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Gross Aging Intake Exposure: $17.8 quadrillion
Note📊 Details
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 🔢: 1.78 billion plaintiffs
- 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
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 the distribution of Corporate Damages Prosecutor Gross Aging Intake Exposure across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Aging Intake Exposure will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Gross Disease DALY Exposure: $30 quadrillion
Note📊 Details
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 🔢: 200 billion DALYs
- Standard Economic Value per QALY 📊: $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
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 the distribution of Corporate Damages Prosecutor Gross Disease DALY Exposure across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Disease DALY Exposure will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Gross Medical Misallocation Exposure: $38.2 quadrillion
Note📊 Details
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 🔢: 3.82 billion plaintiffs
- 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
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 the distribution of Corporate Damages Prosecutor Gross Medical Misallocation Exposure across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Medical Misallocation Exposure will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human: $5.31 million
Note📊 Details
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 🔢: $42.5 quadrillion
- Global Population in 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
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 the distribution of Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure Per Living Human will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Gross Pleading Exposure Total: $42.5 quadrillion
Note📊 Details
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 🔢: $4.31 quadrillion
- 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
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 the distribution of Corporate Damages Prosecutor Gross Pleading Exposure Total across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure Total will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Gross Pleading Exposure With DALYs: $72.5 quadrillion
Note📊 Details
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 🔢: $4.31 quadrillion
- Corporate Damages Prosecutor Gross Medical Misallocation Exposure 🔢: $38.2 quadrillion
- 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
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 the distribution of Corporate Damages Prosecutor Gross Pleading Exposure With DALYs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure With DALYs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human: $9.07 million
Note📊 Details
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 🔢: $72.5 quadrillion
- Global Population in 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
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 the distribution of Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Prosecutor Gross Pleading Exposure With DALYs Per Living Human will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita: $9.13 million
Note📊 Details
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 🔢: $73.1 quadrillion
- Global Population in 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
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 the distribution of Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages State Farm Constitutional-Ceiling Exposure Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages State Farm Constitutional-Ceiling Exposure Total: $73.1 quadrillion
Note📊 Details
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 🔢: $7.31 quadrillion
- State Farm Constitutional-Ceiling Exposure 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
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 the distribution of Corporate Damages State Farm Constitutional-Ceiling Exposure Total across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages State Farm Constitutional-Ceiling Exposure Total will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Strict Floor Per Capita: $538,219
Note📊 Details
Strict non-duplicative corporate damages floor per living human.
Inputs:
- Corporate Damages Strict Floor Total 🔢: $4.31 quadrillion
- Global Population in 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
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 the distribution of Corporate Damages Strict Floor Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Strict Floor Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Strict Floor Total: $4.31 quadrillion
Note📊 Details
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 🔢: $3.1 quadrillion
- Corporate Damages Efficacy Lag Deaths VSL 🔢: $1.02 quadrillion
- Corporate Damages Property Plus Environmental Destruction 🔢: $50 trillion
- Excess Military Spending Above 1900 Freeze 🔢: $135 trillion
- 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
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 the distribution of Corporate Damages Strict Floor Total across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Strict Floor Total will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Treble-Style Exposure Per Capita: $2.74 million
Note📊 Details
Treble-style exposure per living human under the False Claims Act-style corporate penalty analogy.
Inputs:
- Corporate Damages Treble-Style Exposure Total 🔢: $21.9 quadrillion
- Global Population in 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
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 the distribution of Corporate Damages Treble-Style Exposure Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Treble-Style Exposure Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages Treble-Style Exposure Total: $21.9 quadrillion
Note📊 Details
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 🔢: $7.31 quadrillion
- Corporate Analog False Claims Act 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
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 the distribution of Corporate Damages Treble-Style Exposure Total across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages Treble-Style Exposure Total will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Corporate Damages War Deaths VSL: $3.1 quadrillion
Note📊 Details
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: 310 million deaths (95% CI: 200 million deaths - 340 million deaths)
- 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
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 the distribution of Corporate Damages War Deaths VSL across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Corporate Damages War Deaths VSL will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Military Spending in Government Clinical Trial Years: 37,778 years
Note📊 Details
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): $170 trillion
- Annual Global Government Spending on Clinical Trials 📊: $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
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 the distribution of Military Spending in Government Clinical Trial Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Military Spending in Government Clinical Trial Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Combination Therapy Exploration Time (Current): 13.7 million years
Note📊 Details
Years to test all pairwise drug combinations at current trial capacity. Combination therapy is standard in oncology, HIV, cardiology.
Inputs:
- Combination Therapy Space 🔢: 45.1 billion combinations
- Current Global Clinical 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
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 the distribution of Combination Therapy Exploration Time (Current) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Combination Therapy Exploration Time (Current) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Known Safe Exploration Time (Current): 2,879 years
Note📊 Details
Years to test all known safe drug-disease combinations at current global trial capacity
Inputs:
- Possible Drug-Disease Combinations 🔢: 9.5 million combinations
- Current Global Clinical 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
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 the distribution of Known Safe Exploration Time (Current) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Known Safe Exploration Time (Current) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Current Patient Participation Rate in Clinical Trials: 0.0792%
Note📊 Details
Current patient participation rate in clinical trials (0.08% = 1.9M participants / 2.4B disease patients)
Inputs:
- Annual Global Clinical Trial Participants 📊: 1.9 million patients/year (95% CI: 1.5 million patients/year - 2.3 million patients/year)
- Global Population with Chronic Diseases 📊: 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:18
✓ 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
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 the distribution of Current Patient Participation Rate in Clinical Trials across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Current Patient Participation Rate in Clinical Trials will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Current Trajectory Average Income at Year 15: $18,714
Note📊 Details
Average income (GDP per capita) at year 15 under current trajectory.
Inputs:
- Current Trajectory GDP at Year 15 🔢: $167 trillion
- 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📊 Details
Average income (GDP per capita) at year 20 under current trajectory trajectory.
Inputs:
- Current Trajectory GDP at Year 20 🔢: $188 trillion
- 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📊 Details
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) 🔢: $14,375
- Current Trajectory Average Income at Year 20 🔢: $20,483
- Average Remaining Years (Median Person) 🔢: 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
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 the distribution of Current Trajectory Cumulative Lifetime Income (Per Capita) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Current Trajectory Cumulative Lifetime Income (Per Capita) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Current Trajectory GDP at Year 15: $167 trillion
Note📊 Details
Global GDP at year 15 under status-quo current trajectory growth.
Inputs:
- Global GDP (2025) 📊: $115 trillion
- Baseline Global GDP 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📊 Details
Global GDP at year 20 under status-quo current trajectory growth.
Inputs:
- Global GDP (2025) 📊: $115 trillion
- Baseline Global GDP 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📊 Details
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 🔢: $20,483
- Military Share of Global GDP 🔢: 2.37%
- Military Spending Real CAGR (10-Year) 📊: 3.4%
- Baseline Global GDP Growth Rate: 2.5%
- Global Median-to-Mean Income Ratio 🔢: 0.203:1
- Median Share Erosion Rate (Annual): 0% (95% CI: -0.5% - 0.78%)
- Effective Tax Rate on Median Earner: 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
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 the distribution of Median After-Tax Consumable Income, Status Quo (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Status Quo (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Military Activist-Stake Cost (Realistic Entry): $48.4 billion
Note📊 Details
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 📊: $836 billion
- Allied Military Primes Market Cap 📊: $132 billion
- 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📊 Details
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) 🔢: $48.4 billion
- Global Investable Financial 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📊 Details
Per-person cost of the military takeover distributed across global population
Inputs:
- Military Takeover Cost (Outright-Control Ceiling) 🔢: $873 billion
- Global Population in 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
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 the distribution of Military Takeover Cost per Human across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Military Takeover Cost per Human will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Military Takeover Cost (Outright-Control Ceiling): $873 billion
Note📊 Details
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 📊: $836 billion
- Allied Military Primes Market Cap 📊: $132 billion
- Control Fraction: 0.501:1
- Acquisition Premium Multiplier: 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
Military Takeover Cost as Share of Global Investable Assets: 0.286%
Note📊 Details
Cost to acquire controlling stakes in all major Western military contractors, expressed as a share of total global investable assets. The affordability framing: the entire takeover is a rounding error against the world’s investable wealth.
Inputs:
- Military Takeover Cost (Outright-Control Ceiling) 🔢: $873 billion
- Global Investable Financial Assets 📊: $305 trillion
\[ \begin{gathered} C_{takeover}/A_{investable} \\ = \frac{C_{takeover}}{Assets_{invest}} \\ = \frac{\$873B}{\$305T} \\ = 0.286\% \end{gathered} \] 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: Military Takeover 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) | 0.286% |
| Mean (expected value) | 0.286% |
| Median (50th percentile) | 0.286% |
| Standard Deviation | 0% |
| 90% Range (5th-95th percentile) | [0.286%, 0.286%] |
Exceedance Probability
Exceedance note: Military Takeover 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: 0.286%
Year Destructive Economy Reaches 25% of GDP: 2033
Note📊 Details
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: 2025
- Destructive Economy as % of GDP 🔢: 11.5%
- Cybercrime Cost CAGR 📊: 15%
- Military Spending Real CAGR (10-Year) 📊: 3.4%
- Baseline Global GDP Growth Rate: 2.5%
- Global Military Spending in 2024 📊: $2.72 trillion
- Global Destructive Economy (2025) 🔢: $13.2 trillion
- Global Cybercrime Costs (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📊 Details
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: 2025
- Destructive Economy as % of GDP 🔢: 11.5%
- Cybercrime Cost CAGR 📊: 15%
- Military Spending Real CAGR (10-Year) 📊: 3.4%
- Baseline Global GDP Growth Rate: 2.5%
- Global Military Spending in 2024 📊: $2.72 trillion
- Global Destructive Economy (2025) 🔢: $13.2 trillion
- Global Cybercrime Costs (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📊 Details
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: 2025
- Destructive Economy as % of GDP 🔢: 11.5%
- Cybercrime Cost CAGR 📊: 15%
- Military Spending Real CAGR (10-Year) 📊: 3.4%
- Baseline Global GDP Growth Rate: 2.5%
- Global Military Spending in 2024 📊: $2.72 trillion
- Global Destructive Economy (2025) 🔢: $13.2 trillion
- Global Cybercrime Costs (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📊 Details
Total annual pragmatic trial platform operational costs (sum of all components: platform + staff + infra + regulatory + community)
Inputs:
- Pragmatic Trial Platform Maintenance Costs: $15 million (95% CI: $10 million - $22 million)
- Pragmatic Trial Platform Staff Costs: $10 million (95% CI: $7 million - $15 million)
- Pragmatic Trial Platform Infrastructure Costs: $8 million (95% CI: $5 million - $12 million)
- Pragmatic Trial Platform Regulatory Coordination Costs: $5 million (95% CI: $3 million - $8 million)
- Pragmatic Trial Platform Community Support Costs: $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
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 the distribution of Total Annual Pragmatic Trial Platform Operational Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Pragmatic Trial Platform Operational Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual R&D Savings from Pragmatic Trials: $58.6 billion
Note📊 Details
Annual benefit from pragmatic trial R&D savings (trial cost reduction, secondary component)
Inputs:
- Annual Global Spending on Clinical Trials 📊: $60 billion (95% CI: $50 billion - $75 billion)
- Pragmatic Trial Cost Reduction Percentage 🔢: 97.7%
\[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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) | 0.9809 | Strong driver |
| Pragmatic Trial Cost Reduction Percentage (percentage) | 0.1871 | 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
Simulation Results Summary: Annual R&D Savings from Pragmatic Trials
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $58.6 billion |
| Mean (expected value) | $58.5 billion |
| Median (50th percentile) | $57.6 billion |
| Standard Deviation | $7.97 billion |
| 90% Range (5th-95th percentile) | [$48.1 billion, $73.1 billion] |
The histogram shows the distribution of Annual R&D Savings from Pragmatic Trials across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual R&D Savings from Pragmatic Trials will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Direct Pragmatic Trial Funding Cost per DALY: $0.842
Note📊 Details
Cost per DALY at direct funding level for the therapeutic space exploration period. Still highly cost-effective vs bed nets.
Inputs:
- Direct Pragmatic Trial Funding NPV (Exploration Period) 🔢: $476 billion
- Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565 billion DALYs
\[ \begin{gathered} Cost_{direct,DALY} \\ = \frac{NPV_{direct}}{DALYs_{max}} \\ = \frac{\$476B}{565B} \\ = \$0.842 \end{gathered} \] where: \[ \begin{gathered} NPV_{direct} \\ = \frac{T_{queue,trial}}{Funding_{trial,ref} \times r_{discount}} \\ = \frac{36}{\$21.8B \times 3\%} \\ = \$476B \end{gathered} \] 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: \[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \] 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: \[ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} \] ~ 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 |
|---|---|---|
| Direct Pragmatic Trial Funding NPV (Exploration Period) (USD) | 0.7920 | Strong driver |
| Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | -0.7046 | 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
Simulation Results Summary: Direct Pragmatic Trial Funding Cost per DALY
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $0.842 |
| Mean (expected value) | $0.742 |
| Median (50th percentile) | $0.662 |
| Standard Deviation | $0.395 |
| 90% Range (5th-95th percentile) | [$0.264, $1.49] |
The histogram shows the distribution of Direct Pragmatic Trial Funding Cost per DALY across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Direct Pragmatic Trial Funding Cost per DALY will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Direct Pragmatic Trial Funding NPV (Exploration Period): $476 billion
Note📊 Details
NPV of annual direct funding for the therapeutic space exploration period. 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: $21.8 billion
- Standard Discount Rate for NPV Analysis: 3%
- Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity 🔢: 36 years
\[ \begin{gathered} NPV_{direct} \\ = \frac{T_{queue,trial}}{Funding_{trial,ref} \times r_{discount}} \\ = \frac{36}{\$21.8B \times 3\%} \\ = \$476B \end{gathered} \] 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
Simulation Results Summary: Direct Pragmatic Trial Funding NPV (Exploration Period)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $476 billion |
| Mean (expected value) | $425 billion |
| Median (50th percentile) | $423 billion |
| Standard Deviation | $169 billion |
| 90% Range (5th-95th percentile) | [$156 billion, $695 billion] |
The histogram shows the distribution of Direct Pragmatic Trial Funding NPV (Exploration Period) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Direct Pragmatic Trial Funding NPV (Exploration Period) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput: 178 thousand:1
Note📊 Details
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 🔢: $84.8 quadrillion
- Direct Pragmatic Trial Funding NPV (Exploration Period) 🔢: $476 billion
\[ \begin{gathered} ROI_{direct,max} \\ = \frac{Value_{max}}{NPV_{direct}} \\ = \frac{\$84800T}{\$476B} \\ = 178{,}000 \end{gathered} \] where: \[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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: \[ \begin{gathered} NPV_{direct} \\ = \frac{T_{queue,trial}}{Funding_{trial,ref} \times r_{discount}} \\ = \frac{36}{\$21.8B \times 3\%} \\ = \$476B \end{gathered} \] where: \[ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} \] ✓ 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) | -0.7627 | Strong driver |
| Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.6139 | 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
Simulation Results Summary: Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 178 thousand:1 |
| Mean (expected value) | 265 thousand:1 |
| Median (50th percentile) | 223 thousand:1 |
| Standard Deviation | 168 thousand:1 |
| 90% Range (5th-95th percentile) | [92 thousand:1, 575 thousand:1] |
The histogram shows the distribution of Direct Funding ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Total DALYs Lost from Disease Eradication Delay: 8.77 billion DALYs
Note📊 Details
Total Disability-Adjusted Life Years lost from disease eradication delay (PRIMARY estimate)
Inputs:
- Years of Life Lost from Disease Eradication Delay 🔢: 7.9 billion years
- Years Lived with Disability During Disease Eradication Delay 🔢: 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
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 the distribution of Total DALYs Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total DALYs Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Deaths from Disease Eradication Delay: 416 million deaths
Note📊 Details
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 📊: 8.2 years (SE: ±2 years)
- Global Daily Deaths from Disease and Aging 📊: 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
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 the distribution of Total Deaths from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Deaths from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Economic Loss from Disease Eradication Delay: $1.32 quadrillion
Note📊 Details
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 🔢: 8.77 billion DALYs
- Standard Economic Value per QALY 📊: $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
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 the distribution of Total Economic Loss from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Economic Loss from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Years Lived with Disability During Disease Eradication Delay: 873 million years
Note📊 Details
Years Lived with Disability during disease eradication delay (PRIMARY estimate)
Inputs:
- Total Deaths from Disease Eradication Delay 🔢: 416 million deaths
- Pre-Death Suffering Period During Post-Safety Efficacy Delay 📊: 6 years (95% CI: 4 years - 9 years)
- Disability Weight for Untreated Chronic Conditions 📊: 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
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 the distribution of Years Lived with Disability During Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Years Lived with Disability During Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Years of Life Lost from Disease Eradication Delay: 7.9 billion years
Note📊 Details
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 🔢: 416 million deaths
- Remaining Life Expectancy at Age 60 (Global) 📊: 21 years (95% CI: 19.6 years - 22 years)
- Mean Age of Preventable Death from Post-Safety Efficacy Delay 📊: 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
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 the distribution of Years of Life Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Years of Life Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
New Treatments Per Year at Treaty-Scale Trial Capacity: 185 diseases/year
Note📊 Details
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 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year)
- Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding 🔢: 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
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 the distribution of New Treatments Per Year at Treaty-Scale Trial Capacity across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that New Treatments Per Year at Treaty-Scale Trial Capacity will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Known Safe Exploration Time at Treaty-Scale Trial Capacity: 234 years
Note📊 Details
Years to test all known safe drug-disease combinations with treaty-scale pragmatic trial capacity
Inputs:
- Possible Drug-Disease Combinations 🔢: 9.5 million combinations
- Maximum Trials per Year at Treaty-Scale Trial 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
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 the distribution of Known Safe Exploration Time at Treaty-Scale Trial Capacity across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Known Safe Exploration Time at Treaty-Scale Trial Capacity will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Maximum Trial Capacity Multiplier (Physical Limit): 566x
Note📊 Details
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 🔢: 1.08 billion people
- Annual Global Clinical Trial Participants 📊: 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
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 the distribution of Maximum Trial Capacity Multiplier (Physical Limit) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Maximum Trial Capacity Multiplier (Physical Limit) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Net Savings from Pragmatic Trials (R&D Only): $58.6 billion
Note📊 Details
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 🔢: $58.6 billion
- Total Annual Pragmatic Trial Platform Operational Costs 🔢: $40 million
\[ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{trial} \\ = \$58.6B - \$40M \\ = \$58.6B \end{gathered} \] where: \[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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) | -0.0005 | 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
Simulation Results Summary: Annual Net Savings from Pragmatic Trials (R&D Only)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $58.6 billion |
| Mean (expected value) | $58.5 billion |
| Median (50th percentile) | $57.6 billion |
| Standard Deviation | $7.97 billion |
| 90% Range (5th-95th percentile) | [$48.1 billion, $73 billion] |
The histogram shows the distribution of Annual Net Savings from Pragmatic Trials (R&D Only) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Net Savings from Pragmatic Trials (R&D Only) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Platform Total NPV Annual OPEX: $40 million
Note📊 Details
Total NPV annual opex (pragmatic trial platform core + DIH initiatives)
Inputs:
- Pragmatic Trial Platform Core Framework Annual OPEX: $18.9 million (95% CI: $11 million - $26.5 million)
- DIH Broader Initiatives Annual OPEX: $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
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 the distribution of Pragmatic Trial Platform Total NPV Annual OPEX across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Total NPV Annual OPEX will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted): $389 billion
Note📊 Details
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) 🔢: $58.6 billion
- Standard Discount Rate for NPV Analysis: 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
Simulation Results Summary: NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $389 billion |
| Mean (expected value) | $388 billion |
| Median (50th percentile) | $383 billion |
| Standard Deviation | $52.9 billion |
| 90% Range (5th-95th percentile) | [$319 billion, $485 billion] |
The histogram shows the distribution of NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
NPV Net Benefit (R&D Only): $389 billion
Note📊 Details
NPV net benefit using R&D savings only (benefits minus costs)
Inputs:
- NPV of Pragmatic Trial Benefits (R&D Only, 10-Year Discounted) 🔢: $389 billion
- Pragmatic Trial Platform Total NPV Cost 🔢: $611 million
\[ \begin{gathered} NPV_{net,RD} \\ = NPV_{RD} - Cost_{platform,total} \\ = \$389B - \$611M \\ = \$389B \end{gathered} \] 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: \[ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{trial} \\ = \$58.6B - \$40M \\ = \$58.6B \end{gathered} \] where: \[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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) | -0.0013 | 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
Simulation Results Summary: NPV Net Benefit (R&D Only)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $389 billion |
| Mean (expected value) | $388 billion |
| Median (50th percentile) | $382 billion |
| Standard Deviation | $52.9 billion |
| 90% Range (5th-95th percentile) | [$319 billion, $485 billion] |
The histogram shows the distribution of NPV Net Benefit (R&D Only) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that NPV Net Benefit (R&D Only) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years: $342 million
Note📊 Details
Present value of annual opex over 10 years (NPV formula)
Inputs:
- Pragmatic Trial Platform Total NPV Annual OPEX 🔢: $40 million
- Standard Discount Rate for NPV Analysis: 3%
- Standard Time Horizon for NPV Analysis: 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
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 the distribution of Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Platform Total NPV Cost: $611 million
Note📊 Details
Total NPV cost (upfront + PV of annual opex)
Inputs:
- Pragmatic Trial Platform Present Value of Annual OPEX Over 10 Years 🔢: $342 million
- Pragmatic Trial Platform Total NPV Upfront Costs 🔢: $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
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 the distribution of Pragmatic Trial Platform Total NPV Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Total NPV Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Platform Total NPV Upfront Costs: $270 million
Note📊 Details
Total NPV upfront costs (pragmatic trial platform core + DIH initiatives)
Inputs:
- Pragmatic Trial Platform Core Framework Build Cost: $40 million (95% CI: $25 million - $65 million)
- DIH Broader Initiatives Upfront Cost: $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
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 the distribution of Pragmatic Trial Platform Total NPV Upfront Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Total NPV Upfront Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Platform Overhead Percentage of Treaty Funding: 0.147%
Note📊 Details
Percentage of treaty funding allocated to pragmatic trial platform overhead
Inputs:
- Total Annual Pragmatic Trial Platform Operational Costs 🔢: $40 million
- Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $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
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 the distribution of Pragmatic Trial Platform Overhead Percentage of Treaty Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Platform Overhead Percentage of Treaty Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Patients Fundable Annually at Reference Funding: 23.4 million patients/year
Note📊 Details
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 🔢: $21.8 billion
- 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
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 the distribution of Patients Fundable Annually at Reference Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Patients Fundable Annually at Reference Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity: 36 years
Note📊 Details
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 🔢: 443 years
- Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding 🔢: 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
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 the distribution of Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Daily R&D Savings from Trial Cost Reduction: $161 million
Note📊 Details
Daily R&D savings from trial cost reduction (opportunity cost of delay)
Inputs:
- Annual R&D Savings from Pragmatic Trials 🔢: $58.6 billion
\[ \begin{gathered} Savings_{RD,daily} \\ = Benefit_{RD,ann} \times 0.00274 \\ = \$58.6B \times 0.00274 \\ = \$161M \end{gathered} \] where: \[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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
Simulation Results Summary: Daily R&D Savings from Trial Cost Reduction
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $161 million |
| Mean (expected value) | $160 million |
| Median (50th percentile) | $158 million |
| Standard Deviation | $21.8 million |
| 90% Range (5th-95th percentile) | [$132 million, $200 million] |
The histogram shows the distribution of Daily R&D Savings from Trial Cost Reduction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Daily R&D Savings from Trial Cost Reduction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
ROI from Pragmatic Trial R&D Savings Only: 637:1
Note📊 Details
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) 🔢: $389 billion
- Pragmatic Trial Platform Total NPV Cost 🔢: $611 million
\[ \begin{gathered} ROI_{RD} \\ = \frac{NPV_{RD}}{Cost_{platform,total}} \\ = \frac{\$389B}{\$611M} \\ = 637 \end{gathered} \] 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: \[ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{trial} \\ = \$58.6B - \$40M \\ = \$58.6B \end{gathered} \] where: \[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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) | 0.7625 | Strong driver |
| Pragmatic Trial Platform Total NPV Cost (USD) | -0.6344 | 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
Simulation Results Summary: ROI from Pragmatic Trial R&D Savings Only
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 637:1 |
| Mean (expected value) | 646:1 |
| Median (50th percentile) | 634:1 |
| Standard Deviation | 115:1 |
| 90% Range (5th-95th percentile) | [479:1, 854:1] |
The histogram shows the distribution of ROI from Pragmatic Trial R&D Savings Only across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that ROI from Pragmatic Trial R&D Savings Only will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Maximum Trials per Year at Treaty-Scale Trial Capacity: 40,682 trials/year
Note📊 Details
Maximum trials per year possible with trial capacity multiplier
Inputs:
- Current Global Clinical 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 🔢: 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
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 the distribution of Maximum Trials per Year at Treaty-Scale Trial Capacity across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Maximum Trials per Year at Treaty-Scale Trial Capacity will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding: 12.3x
Note📊 Details
Trial capacity multiplier from treaty-scale pragmatic trial funding capacity vs. current global trial participation
Inputs:
- Annual Global Clinical Trial Participants 📊: 1.9 million patients/year (95% CI: 1.5 million patients/year - 2.3 million patients/year)
- Patients Fundable Annually at Reference Funding 🔢: 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
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 the distribution of Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 565 billion DALYs
Note📊 Details
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).
Inputs:
- Global Annual DALY Burden 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year)
- Eventually Avoidable DALY Percentage: 92.6% (95% CI: 50% - 98%)
- Average Total Treatment Timeline Shift 🔢: 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
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 the distribution of Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: $84.8 quadrillion
Note📊 Details
Total economic value from the combined treatment timeline shift. DALYs valued at standard economic rate.
Inputs:
- Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565 billion DALYs
- Standard Economic Value per QALY 📊: $150,000 (SE: ±$30,000)
\[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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 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 |
|---|---|---|
| Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | 0.8856 | Strong driver |
| Standard Economic Value per QALY (USD/QALY) | 0.4321 | 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
Simulation Results Summary: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $84.8 quadrillion |
| Mean (expected value) | $95 quadrillion |
| Median (50th percentile) | $88 quadrillion |
| Standard Deviation | $40.2 quadrillion |
| 90% Range (5th-95th percentile) | [$42.9 quadrillion, $172 quadrillion] |
The histogram shows the distribution of Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 10.7 billion deaths
Note📊 Details
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 📊: 150 thousand deaths/day (SE: ±7,500 deaths/day)
- Average Total Treatment Timeline Shift 🔢: 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
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 the distribution of Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 1.93 quadrillion hours
Note📊 Details
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 🔢: 565 billion DALYs
- YLD Proportion of Total 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
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 the distribution of Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Average Total Treatment Timeline Shift: 212 years
Note📊 Details
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 🔢: 204 years
- Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 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
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 the distribution of Average Total Treatment Timeline Shift across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Average Total Treatment Timeline Shift will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treatment Timeline Acceleration from Pragmatic Trial Capacity: 204 years
Note📊 Details
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 🔢: 222 years
- Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding 🔢: 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
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 the distribution of Treatment Timeline Acceleration from Pragmatic Trial Capacity across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treatment Timeline Acceleration from Pragmatic Trial Capacity will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Cost Reduction Factor: 44.1x
Note📊 Details
Cost reduction factor projected for embedded pragmatic trials (traditional Phase 3 cost / pragmatic trial cost per patient)
Inputs:
- Phase 3 Cost per Patient 📊: $41,000 (95% CI: $20,000 - $120,000)
- 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
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 the distribution of Pragmatic Trial Cost Reduction Factor across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Cost Reduction Factor will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pragmatic Trial Cost Reduction Percentage: 97.7%
Note📊 Details
Trial cost reduction percentage: 1 - (pragmatic trial cost / traditional Phase 3 cost)
Inputs:
- Pragmatic Trial Cost per Patient 📊: $929 (95% CI: $97 - $3,000)
- Phase 3 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
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 the distribution of Pragmatic Trial Cost Reduction Percentage across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Cost Reduction Percentage will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Reference Annual Trial Subsidies: $21.8 billion
Note📊 Details
Annual patient-level pragmatic trial subsidies after operating costs at the reference funding level
Inputs:
- Reference Annual Pragmatic Trial Funding: $21.8 billion
- Total Annual Pragmatic Trial Platform Operational Costs 🔢: $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
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 the distribution of Reference Annual Trial Subsidies across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Reference Annual Trial Subsidies will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Valley of Death Rescue Multiplier: 1.4x
Note📊 Details
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 📊: 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
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 the distribution of Valley of Death Rescue Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Valley of Death Rescue Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Patients Fundable Annually Under the 1% Treaty: 23.4 million patients/year
Note📊 Details
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 🔢: $21.7 billion
- 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
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 the distribution of Patients Fundable Annually Under the 1% Treaty across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Patients Fundable Annually Under the 1% Treaty will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Medical Research Percentage of Treaty Funding: 80%
Note📊 Details
Percentage of treaty funding allocated to medical research (after bond payouts and IAB incentives)
Inputs:
- 1% Treaty Pragmatic Trial Funding 🔢: $21.8 billion
- Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $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📊 Details
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 🔢: $27.2 billion
- Annual VICTORY Incentive Alignment Bond Payout 🔢: $2.72 billion
- Annual IAB Political Incentive Funding 🔢: $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📊 Details
Annual 1% Treaty patient-level pragmatic trial subsidies after platform operating costs
Inputs:
- Total Annual Pragmatic Trial Platform Operational Costs 🔢: $40 million
- 1% Treaty Pragmatic Trial Funding 🔢: $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
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 the distribution of 1% Treaty Annual Trial Subsidies across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that 1% Treaty Annual Trial Subsidies will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Patient Trial Subsidies Percentage of Treaty Funding: 79.9%
Note📊 Details
Percentage of treaty funding going directly to patient trial subsidies
Inputs:
- 1% Treaty Annual Trial Subsidies 🔢: $21.7 billion
- Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $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
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 the distribution of Patient Trial Subsidies Percentage of Treaty Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Patient Trial Subsidies Percentage of Treaty Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Diseases Without Effective Treatment: 6,650 diseases
Note📊 Details
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 📊: 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:167
~ 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
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 the distribution of Diseases Without Effective Treatment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Diseases Without Effective Treatment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths: 18.4 thousand:1
Note📊 Details
Ratio of annual disease deaths to 9/11 terrorism deaths
Inputs:
- Annual Deaths from All Diseases and Aging Globally 📊: 55 million deaths/year (SE: ±5 million deaths/year)
- Deaths from 9/11 Terrorist Attacks 📊: 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
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 the distribution of Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Ratio of Annual Disease Deaths to War Deaths: 225:1
Note📊 Details
Ratio of annual disease deaths to war deaths
Inputs:
- Annual Deaths from All Diseases and Aging Globally 📊: 55 million deaths/year (SE: ±5 million deaths/year)
- Total Annual Conflict Deaths Globally 🔢: 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
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 the distribution of Ratio of Annual Disease Deaths to War Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Annual Disease Deaths to War Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Drugs Approved Since 1962: 3,100 drugs
Note📊 Details
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 📊: 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:20
~ 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
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 the distribution of Total Drugs Approved Since 1962 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Drugs Approved Since 1962 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Drug Cost Increase: 1980s to Current: 13.4x
Note📊 Details
Drug development cost increase from 1980s to current
Inputs:
- Drug Development Cost (1980s) 📊: $194 million (95% CI: $146 million - $242 million)
- Pharma Drug Development Cost (Current System) 📊: $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:31
✓ 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
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 the distribution of Drug Cost Increase: 1980s to Current across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Drug Cost Increase: 1980s to Current will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Drug Cost Increase: Pre-1962 to Current: 105x
Note📊 Details
Drug development cost increase from pre-1962 to current
Inputs:
- Pharma Drug Development Cost (Current System) 📊: $2.6 billion (95% CI: $1.5 billion - $4 billion)
- Pre-1962 Drug Development Cost (2024 Dollars) 📊: $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:111
✓ 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
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 the distribution of Drug Cost Increase: Pre-1962 to Current across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Drug Cost Increase: Pre-1962 to Current will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Possible Drug-Disease Combinations: 9.5 million combinations
Note📊 Details
Total possible drug-disease combinations using existing safe compounds
Inputs:
- Safe Compounds Available for Testing: 9,500 compounds (95% CI: 7,000 compounds - 12,000 compounds)
- Trial-Relevant Diseases: 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
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 the distribution of Possible Drug-Disease Combinations across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Possible Drug-Disease Combinations will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Earth Optimization Point Value: $6,671
Note📊 Details
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 🔢: $27.5 trillion
- 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
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 the distribution of Earth Optimization Point Value across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Earth Optimization Point Value will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Earth Optimization Points Payout (2 Claims): $13,341
Note📊 Details
Payout for a depositor who recruits 2 verified participants (earning 2 Earth Optimization Points). CI range reflects participation uncertainty.
Inputs:
- 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
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 the distribution of Earth Optimization Points Payout (2 Claims) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Earth Optimization Points Payout (2 Claims) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cumulative Efficacy Testing Cost (1962-2024): $4.84 trillion
Note📊 Details
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: $1.56 billion (SE: ±$200 million)
- Total 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
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 the distribution of Cumulative Efficacy Testing Cost (1962-2024) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative Efficacy Testing Cost (1962-2024) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Efficacy Lag Deaths (9/11 Equivalents): 34,132 9/11s
Note📊 Details
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 🔢: 102 million deaths
- September 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
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 the distribution of Efficacy Lag Deaths (9/11 Equivalents) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Efficacy Lag Deaths (9/11 Equivalents) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treatment Delay YLD - Annual: 2.01 billion DALYs
Note📊 Details
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 🔢: 982 million people
- Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±2 years)
- 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
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 the distribution of Treatment Delay YLD - Annual across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treatment Delay YLD - Annual will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
EOS Equity Value (V): $74.1 trillion
Note📊 Details
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 🔢: $101 trillion
- EOS Social Value Capture Rate 📊: 2.2% (95% CI: 0.5% - 5%)
- Standard Discount Rate for NPV Analysis: 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
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 the distribution of EOS Equity Value (V) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that EOS Equity Value (V) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923): 23.2%
Note📊 Details
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 📊: 2.6 IQ points (95% CI: 1.5 IQ points - 5.9 IQ points)
- GDP Growth Effect per National IQ Point 📊: 0.11% (95% CI: 0.04% - 0.18%)
- Leaded Gasoline Era Duration: 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
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 the distribution of Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Ethyl Shareholder Counterfactual Wealth Gain (Ethanol Switch, 1923) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Deaths from Historical Progress Delays: 102 million deaths
Note📊 Details
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 🔢: 12.4 million deaths
- Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 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
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 the distribution of Total Deaths from Historical Progress Delays across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Deaths from Historical Progress Delays will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Economic Loss from Historical Progress Delays: $290 trillion
Note📊 Details
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) 📊: 21 years (95% CI: 19.6 years - 22 years)
- Total Deaths from Historical Progress Delays 🔢: 102 million deaths
- Mean Age of Preventable Death from Post-Safety Efficacy Delay 📊: 62 years (SE: ±3 years)
- Standard Economic Value per QALY 📊: $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
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 the distribution of Total Economic Loss from Historical Progress Delays across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Economic Loss from Historical Progress Delays will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Therapeutic Frontier Exploration Ratio: 0.342%
Note📊 Details
Fraction of possible drug-disease space actually tested (<1%)
Inputs:
- Tested Drug-Disease Relationships: 32,500 relationships (95% CI: 15,000 relationships - 50,000 relationships)
- Possible Drug-Disease Combinations 🔢: 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
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 the distribution of Therapeutic Frontier Exploration Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Therapeutic Frontier Exploration Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier: 32.8x
Note📊 Details
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 📊: 8.2 years (SE: ±2 years)
- Oxford RECOVERY Trial Duration 📊: 3 months
\[ \begin{gathered} k_{FDA:RECOVERY} \\ = T_{lag} \times \text{MONTHS\_PER\_YEAR} / T_{RECOVERY} \end{gathered} \]
Methodology:97
✓ 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
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 the distribution of FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that FDA Efficacy Testing to Oxford RECOVERY Trial Time Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Full Corporate Activist-Stake Cost (All Sectors): $884 billion
Note📊 Details
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 📊: $836 billion
- Allied Military Primes Market Cap 📊: $132 billion
- Government-Controlling Sectors Top-5 Market Cap 📊: $16.7 trillion (95% CI: $15 trillion - $18 trillion)
- 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📊 Details
Activist stake across every government-controlling industry as a share of total global investable assets.
Inputs:
- Full Corporate Activist-Stake Cost (All Sectors) 🔢: $884 billion
- Global Investable Financial 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📊 Details
Total annual conflict deaths globally (sum of combat, terror, state violence)
Inputs:
- Annual Deaths from Active Combat Worldwide 📊: 234 thousand deaths/year (95% CI: 180 thousand deaths/year - 300 thousand deaths/year)
- Annual Deaths from State Violence 📊: 2,700 deaths/year (95% CI: 1,500 deaths/year - 5,000 deaths/year)
- Annual Deaths from Terror Attacks Globally 📊: 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
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 the distribution of Total Annual Conflict Deaths Globally across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Conflict Deaths Globally will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Annual Cost of War Worldwide: $11.4 trillion
Note📊 Details
Total annual cost of war worldwide (direct + indirect costs)
Inputs:
- Total Annual Direct War Costs 🔢: $7.66 trillion
- Total Annual Indirect War Costs 🔢: $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
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 the distribution of Total Annual Cost of War Worldwide across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Cost of War Worldwide will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Cost of Combat Deaths: $2.34 trillion
Note📊 Details
Annual cost of combat deaths (deaths × VSL)
Inputs:
- Annual Deaths from Active Combat Worldwide 📊: 234 thousand deaths/year (95% CI: 180 thousand deaths/year - 300 thousand deaths/year)
- 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
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 the distribution of Annual Cost of Combat Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Cost of Combat Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Cost of State Violence Deaths: $27 billion
Note📊 Details
Annual cost of state violence deaths (deaths × VSL)
Inputs:
- Annual Deaths from State Violence 📊: 2,700 deaths/year (95% CI: 1,500 deaths/year - 5,000 deaths/year)
- 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
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 the distribution of Annual Cost of State Violence Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Cost of State Violence Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Cost of Terror Deaths: $83 billion
Note📊 Details
Annual cost of terror deaths (deaths × VSL)
Inputs:
- Annual Deaths from Terror Attacks Globally 📊: 8,300 deaths/year (95% CI: 6,000 deaths/year - 12,000 deaths/year)
- 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
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 the distribution of Annual Cost of Terror Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Cost of Terror Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Annual Human Life Losses from Conflict: $2.45 trillion
Note📊 Details
Total annual human life losses from conflict (sum of combat, terror, state violence)
Inputs:
- Annual Cost of Combat Deaths 🔢: $2.34 trillion
- Annual Cost of State Violence Deaths 🔢: $27 billion
- Annual Cost of Terror Deaths 🔢: $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
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 the distribution of Total Annual Human Life Losses from Conflict across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Human Life Losses from Conflict will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Annual Infrastructure Destruction: $1.88 trillion
Note📊 Details
Total annual infrastructure destruction (sum of transportation, energy, communications, water, education, healthcare)
Inputs:
- Annual Infrastructure Damage to Communications from Conflict 📊: $298 billion (95% CI: $209 billion - $418 billion)
- Annual Infrastructure Damage to Education Facilities from Conflict 📊: $234 billion (95% CI: $164 billion - $328 billion)
- Annual Infrastructure Damage to Energy Systems from Conflict 📊: $422 billion (95% CI: $295 billion - $590 billion)
- Annual Infrastructure Damage to Healthcare Facilities from Conflict 📊: $166 billion (95% CI: $116 billion - $232 billion)
- Annual Infrastructure Damage to Transportation from Conflict 📊: $487 billion (95% CI: $340 billion - $680 billion)
- Annual Infrastructure Damage to Water Systems from 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
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 the distribution of Total Annual Infrastructure Destruction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Infrastructure Destruction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Annual Savings: $31.1 trillion
Note📊 Details
Global annual savings in USD (savings rate × GDP)
Inputs:
- Global Gross Savings Rate 📊: 27% (95% CI: 24% - 30%)
- 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
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 the distribution of Global Annual Savings across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Annual Savings will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Annual Savings Per Capita: $3,881
Note📊 Details
Global annual savings divided by global population. Useful as a rough average-person default for prize-contribution sizing.
Inputs:
- Global Annual Savings 🔢: $31.1 trillion
- Global Population in 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
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 the distribution of Global Annual Savings Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Annual Savings Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Annual Trade Disruption: $616 billion
Note📊 Details
Total annual trade disruption (sum of shipping, supply chain, energy prices, currency instability)
Inputs:
- Annual Trade Disruption Costs from Currency Instability 📊: $57.4 billion (95% CI: $40 billion - $80 billion)
- Annual Trade Disruption Costs from Energy Price Volatility 📊: $125 billion (95% CI: $87 billion - $175 billion)
- Annual Trade Disruption Costs from Shipping Disruptions 📊: $247 billion (95% CI: $173 billion - $346 billion)
- Annual Trade Disruption Costs from Supply Chain Disruptions 📊: $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
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 the distribution of Total Annual Trade Disruption across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Trade Disruption will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Annual Direct War Costs: $7.66 trillion
Note📊 Details
Total annual direct war costs (military spending + infrastructure + human life + trade disruption)
Inputs:
- Total Annual Human Life Losses from Conflict 🔢: $2.45 trillion
- Total Annual Infrastructure Destruction 🔢: $1.88 trillion
- Total Annual Trade Disruption 🔢: $616 billion
- Global Military Spending in 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
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 the distribution of Total Annual Direct War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Direct War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Annual Indirect War Costs: $3.7 trillion
Note📊 Details
Total annual indirect war costs (opportunity cost + veterans + refugees + environment + mental health + lost productivity)
Inputs:
- Annual Environmental Damage and Restoration Costs from Conflict 📊: $100 billion (95% CI: $70 billion - $140 billion)
- Annual Lost Economic Growth from Military Spending Opportunity Cost 📊: $2.72 trillion (95% CI: $1.9 trillion - $3.8 trillion)
- Annual Lost Productivity from Conflict Casualties 📊: $300 billion (95% CI: $210 billion - $420 billion)
- Annual PTSD and Mental Health Costs from Conflict 📊: $232 billion (95% CI: $162 billion - $325 billion)
- Annual Refugee Support Costs 📊: $150 billion (95% CI: $105 billion - $210 billion)
- 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
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 the distribution of Total Annual Indirect War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Indirect War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Average Hourly Income: $7.19
Note📊 Details
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) 🔢: $14,375
- 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
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 the distribution of Global Average Hourly Income across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Average Hourly Income will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Average Income (2025 Baseline): $14,375
Note📊 Details
Global average income (GDP per capita) in 2025 baseline.
Inputs:
- Global GDP (2025) 📊: $115 trillion
- Global Population in 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
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 the distribution of Global Average Income (2025 Baseline) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Average Income (2025 Baseline) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Average Remaining Years (Median Person): 42.9 years
Note📊 Details
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) 📊: 73.4 years (SE: ±2 years)
- 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
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 the distribution of Average Remaining Years (Median Person) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Average Remaining Years (Median Person) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Bullets Purchasable with Global Military Budget: 6.8 trillion rounds
Note📊 Details
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 📊: $2.72 trillion
- Cost per 5.56mm NATO Round (Bulk) 📊: $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
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 the distribution of Bullets Purchasable with Global Military Budget across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Bullets Purchasable with Global Military Budget will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Coordination Activation Budget: $30.8 billion
Note📊 Details
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 📊: 4.13 billion of people
- Activation Cost per Participant 🔢: $6.5
- 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
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 the distribution of Global Coordination Activation Budget across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Coordination Activation Budget will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Activation Cost per Participant: $6.5
Note📊 Details
Blended variable activation cost per successful verified participant: direct incentive plus verification and payment operations.
Inputs:
- Activation Reward per Verified Participant: $5 (95% CI: $2 - $10)
- 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
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 the distribution of Activation Cost per Participant across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Activation Cost per Participant will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Majority of Humanity Coordination Target: 51.6%
Note📊 Details
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 📊: 4.13 billion of people
- Global Population in 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
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 the distribution of Majority of Humanity Coordination Target across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Majority of Humanity Coordination Target will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Destructive Economy (2025): $13.2 trillion
Note📊 Details
Combined annual cost of military spending and cybercrime. The ‘destructive economy’ that competes with the productive economy.
Inputs:
- Global Military Spending in 2024 📊: $2.72 trillion
- Global Cybercrime Costs (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📊 Details
Destructive economy (military + cybercrime) as percentage of global GDP.
Inputs:
- Global Destructive Economy (2025) 🔢: $13.2 trillion
- 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📊 Details
Global deaths per minute from all disease and aging
Inputs:
- Global Daily Deaths from Disease and Aging 📊: 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
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 the distribution of Global Deaths per Minute from Disease across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Deaths per Minute from Disease will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Welfare Cost of Avoidable Disease: $400 trillion
Note📊 Details
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 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year)
- Eventually Avoidable DALY Percentage: 92.6% (95% CI: 50% - 98%)
- Standard Economic Value per QALY 📊: $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
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 the distribution of Annual Welfare Cost of Avoidable Disease across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Welfare Cost of Avoidable Disease will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Annual Total Market Cost of Disease: $14.9 trillion
Note📊 Details
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 📊: $9.9 trillion (95% CI: $7 trillion - $14 trillion)
- Global Annual Productivity Loss from Disease 📊: $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
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 the distribution of Global Annual Total Market Cost of Disease across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Annual Total Market Cost of Disease will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Global Government Expense: $36.5 trillion
Note📊 Details
Approximate annual global government expenditure, computed as global GDP times the World Bank general-government expense share of GDP.
Inputs:
- Global GDP (2025) 📊: $115 trillion
- Global Government Expense Share of 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
Global Median After-Tax Consumable Income (2025): $2,138
Note📊 Details
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) 🔢: $14,375
- Military Share of Global GDP 🔢: 2.37%
- Global Median-to-Mean Income Ratio 🔢: 0.203:1
- Effective Tax Rate on Median Earner: 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
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 the distribution of Global Median After-Tax Consumable Income (2025) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Median After-Tax Consumable Income (2025) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Median-to-Mean Income Ratio: 0.203:1
Note📊 Details
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) 📊: $2,920 (95% CI: $2,300 - $3,700)
- Global Average Income (2025 Baseline) 🔢: $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
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 the distribution of Global Median-to-Mean Income Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Median-to-Mean Income Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Per Capita Military Spending Globally: $340
Note📊 Details
Per capita military spending globally
Inputs:
- Global Military Spending in 2024 📊: $2.72 trillion
- Global Population in 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
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 the distribution of Per Capita Military Spending Globally across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Per Capita Military Spending Globally will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Military Spending After 1% Treaty Reduction: $2.69 trillion
Note📊 Details
Global military spending after 1% treaty reduction
Inputs:
- Global Military Spending in 2024 📊: $2.72 trillion
- 1% Reduction in Military Spending/War Costs from Treaty: 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📊 Details
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 📊: $2.72 trillion
- Global Military Spending in 2005 (Constant 2023 USD) 📊: $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📊 Details
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) 🔢: $25.5 billion
- 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
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 the distribution of Global Political Reform Investment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Political Reform Investment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Annual Cost of War and Disease: $412 trillion
Note📊 Details
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 🔢: $11.4 trillion
- Annual Welfare Cost of Avoidable Disease 🔢: $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
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 the distribution of Total Annual Cost of War and Disease across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Cost of War and Disease will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cumulative 80-Year War Cost (Baseline Trajectory): $3.22 quadrillion
Note📊 Details
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 🔢: $11.4 trillion
- Military Spending Real CAGR (20-Year) 🔢: 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
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 the distribution of Cumulative 80-Year War Cost (Baseline Trajectory) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative 80-Year War Cost (Baseline Trajectory) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cumulative 80-Year War Cost (Treaty Trajectory): $899 trillion
Note📊 Details
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 🔢: $11.4 trillion
- 1% Reduction in Military Spending/War Costs from Treaty: 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
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 the distribution of Cumulative 80-Year War Cost (Treaty Trajectory) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative 80-Year War Cost (Treaty Trajectory) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years: $244 million
Note📊 Details
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 🔢: $11.4 trillion
- Military Spending Real CAGR (20-Year) 🔢: 2.76%
- Assumed Long-Term Real Return for Investment Compounding: 13%
- Global Population in 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
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 the distribution of Per-Person War Cost (Full Abolition) Compounded at 13% over 80 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Per-Person 80-Year War Cost (Baseline Trajectory): $402,488
Note📊 Details
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) 🔢: $3.22 quadrillion
- Global Population in 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
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 the distribution of Per-Person 80-Year War Cost (Baseline Trajectory) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Per-Person 80-Year War Cost (Baseline Trajectory) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Per-Person 80-Year War Cost (Flat Assumption): $113,571
Note📊 Details
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 🔢: $11.4 trillion
- Global Population in 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
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 the distribution of Per-Person 80-Year War Cost (Flat Assumption) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Per-Person 80-Year War Cost (Flat Assumption) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Years Until Baseline War Cost Exceeds Current World GDP: 85.1 years
Note📊 Details
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) 📊: $115 trillion
- Total Annual Cost of War Worldwide 🔢: $11.4 trillion
- Military Spending Real CAGR (20-Year) 🔢: 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
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 the distribution of Years Until Baseline War Cost Exceeds Current World GDP across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Years Until Baseline War Cost Exceeds Current World GDP will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual War Cost in Year 80 (Baseline Trajectory): $100 trillion
Note📊 Details
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 🔢: $11.4 trillion
- Military Spending Real CAGR (20-Year) 🔢: 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
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 the distribution of Annual War Cost in Year 80 (Baseline Trajectory) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual War Cost in Year 80 (Baseline Trajectory) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Healthcare vs Military Multiplier Ratio: 7.17x
Note📊 Details
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 📊: 4.3x (95% CI: 3x - 6x)
- Economic Multiplier for 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
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 the distribution of Healthcare vs Military Multiplier Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Healthcare vs Military Multiplier Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Human Laughs per Healthy Life-Year: 6,205 laughs
Note📊 Details
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): 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
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 the distribution of Human Laughs per Healthy Life-Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Human Laughs per Healthy Life-Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
IAB Mechanism Benefit-Cost Ratio: 230:1
Note📊 Details
Benefit-Cost Ratio of the IAB mechanism itself
Inputs:
- 1% treaty Basic Annual Benefits (Peace + R&D Savings) 🔢: $172 billion
- IAB Mechanism Annual Cost (High Estimate): $750 million (95% CI: $160 million - $750 million)
\[ \begin{gathered} BCR_{IAB} \\ = \frac{Benefit_{peace+RD}}{Cost_{IAB,ann}} \\ = \frac{\$172B}{\$750M} \\ = 230 \end{gathered} \] where: \[ \begin{gathered} Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$58.6B \\ = \$172B \end{gathered} \] 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: \[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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
✓ 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
Simulation Results Summary: IAB Mechanism Benefit-Cost Ratio
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 230:1 |
| Mean (expected value) | 229:1 |
| Median (50th percentile) | 228:1 |
| Standard Deviation | 15.8:1 |
| 90% Range (5th-95th percentile) | [204:1, 256:1] |
The histogram shows the distribution of IAB Mechanism Benefit-Cost Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that IAB Mechanism Benefit-Cost Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual IAB Political Incentive Funding: $2.72 billion
Note📊 Details
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 🔢: $27.2 billion
- IAB Political Incentive Funding Percentage: 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📊 Details
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 🔢: $2.72 billion
- Annual Military Sector Lobbying 📊: $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
Ratio of Industry to Government Clinical Trials Spending: 12.3:1
Note📊 Details
Ratio of Industry to Government spending on clinical trials (approx 90/10 split)
Inputs:
- Annual Global Spending on Clinical Trials 📊: $60 billion (95% CI: $50 billion - $75 billion)
- Annual Global Government Spending on Clinical Trials 📊: $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:168
✓ 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
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 the distribution of Ratio of Industry to Government Clinical Trials Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Industry to Government Clinical Trials Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort: $23.4 million
Note📊 Details
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 🔢: $319,261
- 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
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 the distribution of Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Lost-Prosperity-Only Lifetime Damages, Representative Full-Life Cohort will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon): $187 quadrillion
Note📊 Details
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 🔢: $2.55 quadrillion
- 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
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 the distribution of Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Lost-Prosperity-Only Lifetime Damages Total (Cohort Horizon) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline): $10.6 million
Note📊 Details
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:
\[ \begin{gathered} D_{prosperity,NPV,pc} \\ = \frac{GDP_{pc,lost}}{r_{discount}} \\ = \frac{\$319K}{3\%} \\ = \$10.6M \end{gathered} \] 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
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 the distribution of Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Lost-Prosperity-Only NPV Perpetuity Per Capita (3%, No-Cure Baseline) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline): $85.1 quadrillion
Note📊 Details
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 🔢: $2.55 quadrillion
- Standard Discount Rate for NPV Analysis: 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
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 the distribution of Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Lost-Prosperity-Only NPV Perpetuity Total (3%, No-Cure Baseline) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Medical Research Spending as Percentage of Total Disease Burden: 0.0164%
Note📊 Details
Medical research spending as percentage of total disease burden
Inputs:
- Global Government Medical Research Spending 📊: $67.5 billion (95% CI: $54 billion - $81 billion)
- Total Annual Cost of War and Disease 🔢: $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
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 the distribution of Medical Research Spending as Percentage of Total Disease Burden across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Medical Research Spending as Percentage of Total Disease Burden will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Observed Medical Toolchain Anchor Costs: $58.6 billion
Note📊 Details
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 📊: $2.7 billion
- Medical Toolchain NIH CRISPR Funding, FY2011-FY2018 📊: $3.1 billion
- Medical Toolchain BRAIN Initiative Planned Budget 📊: $4.5 billion
- Medical Toolchain PCORnet Infrastructure Funding Anchor 📊: $325 million
- Medical Toolchain HITECH EHR Incentive Estimated Spending 📊: $30 billion
- Medical Toolchain Operation Warp Speed 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📊 Details
Ratio of global military spending to all clinical trials spending (government + industry + nonprofit)
Inputs:
- Global Military Spending in 2024 📊: $2.72 trillion
- Annual Global Spending on Clinical Trials 📊: $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
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 the distribution of Ratio of Military to Clinical Trials Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Military to Clinical Trials Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Ratio of Military to Government Clinical Trials Spending: 604:1
Note📊 Details
Ratio of global military spending to government clinical trials spending
Inputs:
- Global Military Spending in 2024 📊: $2.72 trillion
- Annual Global Government Spending on Clinical Trials 📊: $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
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 the distribution of Ratio of Military to Government Clinical Trials Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Military to Government Clinical Trials Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
NIH Traditional Trial Maximum Efficiency vs Pragmatic (%): 2.27%
Note📊 Details
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 📊: $929 (95% CI: $97 - $3,000)
- Phase 3 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
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 the distribution of NIH Traditional Trial Maximum Efficiency vs Pragmatic (%) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that NIH Traditional Trial Maximum Efficiency vs Pragmatic (%) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Nuclear Winter Overkill Factor: 122x
Note📊 Details
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 📊: 12,241 warheads
- 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
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 the distribution of Nuclear Winter Overkill Factor across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Nuclear Winter Overkill Factor will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Spare Apocalypses (Overkill Factor Minus One): 121 apocalypses
Note📊 Details
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 🔢: 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
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 the distribution of Spare Apocalypses (Overkill Factor Minus One) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Spare Apocalypses (Overkill Factor Minus One) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Peace Dividend from 1% Reduction in Total War Costs: $114 billion
Note📊 Details
Annual peace dividend from 1% reduction in total war costs (theoretical maximum at ε=1.0)
Inputs:
- Total Annual Cost of War Worldwide 🔢: $11.4 trillion
- 1% Reduction in Military Spending/War Costs from Treaty: 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
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 the distribution of Annual Peace Dividend from 1% Reduction in Total War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Peace Dividend from 1% Reduction in Total War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Conflict Reduction Benefits from 1% Less Military Spending: $86.4 billion
Note📊 Details
Conflict reduction benefits from 1% less military spending (lower confidence - assumes proportional relationship)
Inputs:
- Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114 billion
- Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $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
? 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
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 the distribution of Conflict Reduction Benefits from 1% Less Military Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Conflict Reduction Benefits from 1% Less Military Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Savings from 1% Reduction in Direct War Costs: $76.6 billion
Note📊 Details
Annual savings from 1% reduction in direct war costs
Inputs:
- Total Annual Direct War Costs 🔢: $7.66 trillion
- 1% Reduction in Military Spending/War Costs from Treaty: 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
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 the distribution of Annual Savings from 1% Reduction in Direct War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Direct War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Savings from 1% Reduction in Indirect War Costs: $37 billion
Note📊 Details
Annual savings from 1% reduction in indirect war costs
Inputs:
- Total Annual Indirect War Costs 🔢: $3.7 trillion
- 1% Reduction in Military Spending/War Costs from Treaty: 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
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 the distribution of Annual Savings from 1% Reduction in Indirect War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Indirect War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Per-Person 80-Year Peace Dividend: $290,052
Note📊 Details
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 🔢: $2.32 quadrillion
- Global Population in 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
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 the distribution of Per-Person 80-Year Peace Dividend across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Per-Person 80-Year Peace Dividend will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Per-Person Peace Dividend Compounded at 13% over 80 Years: $53.7 million
Note📊 Details
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 🔢: $11.4 trillion
- Military Spending Real CAGR (20-Year) 🔢: 2.76%
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
- Assumed Long-Term Real Return for Investment Compounding: 13%
- Global Population in 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
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 the distribution of Per-Person Peace Dividend Compounded at 13% over 80 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Per-Person Peace Dividend Compounded at 13% over 80 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cumulative Peace Dividend Over 80 Years: $2.32 quadrillion
Note📊 Details
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) 🔢: $3.22 quadrillion
- Cumulative 80-Year War Cost (Treaty Trajectory) 🔢: $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 (Baseline Trajectory) (USD) | 0.5000 | Moderate driver |
| Cumulative 80-Year War Cost (Treaty Trajectory) (USD) | 0.5000 | 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
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 the distribution of Cumulative Peace Dividend Over 80 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative Peace Dividend Over 80 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pentagon Unaccounted Funds in Clinical Trial Years: 547 years
Note📊 Details
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 📊: $2.46 trillion
- Annual Global Government Spending on Clinical Trials 📊: $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
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 the distribution of Pentagon Unaccounted Funds in Clinical Trial Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pentagon Unaccounted Funds in Clinical Trial Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pentagon Unaccounted Funds False Claims Analog Exposure: $7.38 trillion
Note📊 Details
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 📊: $2.46 trillion
- Corporate Analog False Claims Act 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📊 Details
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 📊: 149 million life-years (95% CI: 79.4 million life-years - 240 million life-years)
- Average 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:102
? 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
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 the distribution of Annual Lives Saved by Pharmaceuticals across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Lives Saved by Pharmaceuticals will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Governance Efficiency Score: 51.9%
Note📊 Details
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 🔢: $109 trillion
- Theoretical Maximum Welfare (Conservative) 🔢: $210 trillion
\[ \begin{gathered} E_{gov} \\ = \frac{W_{real}}{W_{max}} \\ = \frac{\$109T}{\$210T} \\ = 51.9\% \end{gathered} \] where: \[ \begin{gathered} W_{real} \\ = GDP_{global} - W_{waste} \\ = \$115T - \$6.2T \\ = \$109T \end{gathered} \] where: \[ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.9T + \$1.3T \\ = \$6.2T \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] 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:57
? 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) | -0.9807 | Strong driver |
| Adjusted Realized Welfare (USD) | 0.0224 | 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
Simulation Results Summary: Global Governance Efficiency Score
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 51.9% |
| 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 the distribution of Global Governance Efficiency Score across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Governance Efficiency Score will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Opportunity Cost as % of GDP: 87.8%
Note📊 Details
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 🔢: $101 trillion
- 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:57
? 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
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 the distribution of Global Opportunity Cost as % of GDP across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Opportunity Cost as % of GDP will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Opportunity Cost Total: $101 trillion
Note📊 Details
Total global opportunity cost from governance failures: health innovation delays ($34T), underfunded science ($4T), lead poisoning ($6T), migration restrictions ($57T). Sum: $101T annually in unrealized potential.
Inputs:
- Global Health Opportunity Cost 📊: $34 trillion (95% CI: $20 trillion - $80 trillion)
- Global Science Opportunity Cost 📊: $4 trillion (95% CI: $2 trillion - $10 trillion)
- Global Lead Poisoning Cost 📊: $6 trillion (95% CI: $4 trillion - $8 trillion)
- 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:57
? 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 Science Opportunity 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
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 the distribution of Global Opportunity Cost Total across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Opportunity Cost Total will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Adjusted Realized Welfare: $109 trillion
Note📊 Details
Adjusted realized welfare after subtracting measured governance waste from global GDP.
Inputs:
- Global GDP (2025) 📊: $115 trillion
- Global Waste Total (Efficiency Accounting) 🔢: $6.2 trillion
\[ \begin{gathered} W_{real} \\ = GDP_{global} - W_{waste} \\ = \$115T - \$6.2T \\ = \$109T \end{gathered} \] where: \[ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.9T + \$1.3T \\ = \$6.2T \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ~ 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
Simulation Results Summary: Adjusted Realized Welfare
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $109 trillion |
| Mean (expected value) | $109 trillion |
| Median (50th percentile) | $109 trillion |
| Standard Deviation | $390 billion |
| 90% Range (5th-95th percentile) | [$108 trillion, $109 trillion] |
The histogram shows the distribution of Adjusted Realized Welfare across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Adjusted Realized Welfare will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Theoretical Maximum Welfare (Conservative): $210 trillion
Note📊 Details
Conservative theoretical maximum welfare under opportunity-cost recapture assumptions.
Inputs:
- Adjusted Realized Welfare 🔢: $109 trillion
- Global Opportunity Cost Total 🔢: $101 trillion
\[ W_{max} = W_{real} + O_{total} = \$109T + \$101T = \$210T \] where: \[ \begin{gathered} W_{real} \\ = GDP_{global} - W_{waste} \\ = \$115T - \$6.2T \\ = \$109T \end{gathered} \] where: \[ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.9T + \$1.3T \\ = \$6.2T \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] 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) | 0.0123 | 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
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) | [$169 trillion, $270 trillion] |
The histogram shows the distribution of Theoretical Maximum Welfare (Conservative) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Theoretical Maximum Welfare (Conservative) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Waste Total (Efficiency Accounting): $6.2 trillion
Note📊 Details
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) 🔢: $4.9 trillion
- Global Fossil Fuel Subsidies 📊: $1.3 trillion (95% CI: $1.1 trillion - $1.5 trillion)
\[ \begin{gathered} W_{waste} \\ = W_{total,US} + W_{ff,global} \\ = \$4.9T + \$1.3T \\ = \$6.2T \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ~ 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) | 0.9676 | Strong driver |
| Global Fossil Fuel Subsidies (USD) | 0.2444 | 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
Simulation Results Summary: Global Waste Total (Efficiency Accounting)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $6.2 trillion |
| Mean (expected value) | $6.19 trillion |
| Median (50th percentile) | $6.18 trillion |
| Standard Deviation | $390 billion |
| 90% Range (5th-95th percentile) | [$5.57 trillion, $6.85 trillion] |
The histogram shows the distribution of Global Waste Total (Efficiency Accounting) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Waste Total (Efficiency Accounting) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Political Dysfunction Tax per Household of Four (Annual): $50,500
Note📊 Details
Annual household burden for a 4-person household implied by global Political Dysfunction Tax.
Inputs:
\[ T_{pd,hh4} = T_{pd,pc} \times 4 = \$12.6K \times 4 = \$50.5K \] 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
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 the distribution of Political Dysfunction Tax per Household of Four (Annual) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Political Dysfunction Tax per Household of Four (Annual) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Political Dysfunction Tax per Person (Annual): $12,625
Note📊 Details
Annual per-person burden implied by global Political Dysfunction Tax opportunity costs.
Inputs:
- Global Opportunity Cost Total 🔢: $101 trillion
- Global Population in 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
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 the distribution of Political Dysfunction Tax per Person (Annual) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Political Dysfunction Tax per Person (Annual) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Percentage Military Spending Cut After WW2: 87.6%
Note📊 Details
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) 📊: $176 billion
- US Military Spending at WW2 Peak (Constant 2024 Dollars) 📊: $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📊 Details
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 📊: $20 million (95% CI: $15 million - $25 million)
- 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:97
~ 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
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 the distribution of Pragmatic Trial Cost per QALY (RECOVERY) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Pragmatic Trial Cost per QALY (RECOVERY) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Price of Apocalypse (Minimum Viable Apocalypse): $752 million
Note📊 Details
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 📊: $92 billion
- 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
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 the distribution of Price of Apocalypse (Minimum Viable Apocalypse) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Price of Apocalypse (Minimum Viable Apocalypse) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
PRIZE Pool Annual Return: 15.8%
Note📊 Details
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 📊: 17% (95% CI: 13% - 22%)
- Scale Compression Factor: -2.5% (95% CI: -5% - -1%)
- Wishocratic Crowd Allocation Alpha: 0.5% (95% CI: 0% - 1.5%)
- Home Bias Return Drag 📊: 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
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 the distribution of PRIZE Pool Annual Return across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that PRIZE Pool Annual Return will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
PRIZE Pool Horizon Multiple: 9.03x
Note📊 Details
Compound multiple for prize pool growth over the resolution horizon (tied to the destructive economy 50% threshold year).
Inputs:
- PRIZE Pool Annual Return 🔢: 15.8%
- Year Destructive Economy Reaches 50% of GDP 🔢: 2040
- 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
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 the distribution of PRIZE Pool Horizon Multiple across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that PRIZE Pool Horizon Multiple will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
PRIZE Pool Retirement-Equivalent Principal: $2.28 trillion
Note📊 Details
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 📊: 4.13 billion of people
- Retirement-Equivalent Claim Value Target 🔢: $4,991
- 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
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 the distribution of PRIZE Pool Retirement-Equivalent Principal across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that PRIZE Pool Retirement-Equivalent Principal will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Prize Pool Size: $27.5 trillion
Note📊 Details
Terminal prize pool size: global investable assets × participation rate × compound multiple over the resolution horizon.
Inputs:
- Global Investable Financial Assets 📊: $305 trillion
- Prize Pool Participation Rate: 1% (95% CI: 0.1% - 10%)
- 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
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 the distribution of Prize Pool Size across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Prize Pool Size will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Prize Settlement Target: Global HALE (Year 15): 79.4 years
Note📊 Details
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 🔢: 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: \[ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} \] 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
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 the distribution of Prize Settlement Target: Global HALE (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Prize Settlement Target: Global HALE (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Prize Settlement Target: Median Income (Year 15): $4,381
Note📊 Details
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:
\[ \begin{gathered} \tilde{m}^{*}_{15} \\ = \tilde{m}_{treaty,15} \\ = \$4.38K \\ = \$4.38K \end{gathered} \] 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
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 the distribution of Prize Settlement Target: Median Income (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Prize Settlement Target: Median Income (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
RECOVERY Trial Cost Reduction Factor: 82x
Note📊 Details
Cost reduction factor demonstrated by RECOVERY trial (traditional Phase 3 cost / RECOVERY cost per patient)
Inputs:
- Phase 3 Cost per Patient 📊: $41,000 (95% CI: $20,000 - $120,000)
- 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:97
✓ 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
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 the distribution of RECOVERY Trial Cost Reduction Factor across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that RECOVERY Trial Cost Reduction Factor will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
RECOVERY Trial Total QALYs Generated: 5 million QALYs
Note📊 Details
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 📊: 1 million lives (95% CI: 500 thousand lives - 2 million lives)
- 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
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 the distribution of RECOVERY Trial Total QALYs Generated across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that RECOVERY Trial Total QALYs Generated will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Retirement-Equivalent 2-Claims Target Payout: $9,982
Note📊 Details
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 🔢: $3,881
- 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
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 the distribution of Retirement-Equivalent 2-Claims Target Payout across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Retirement-Equivalent 2-Claims Target Payout will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Retirement-Equivalent Claim Value Target: $4,991
Note📊 Details
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:
\[ \begin{gathered} V_{claim,target} \\ = V_{2claims,target} \times 0.5 \\ = \$9.98K \times 0.5 \\ = \$4.99K \end{gathered} \] 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
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 the distribution of Retirement-Equivalent Claim Value Target across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Retirement-Equivalent Claim Value Target will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
SE Bot Annual Modeled Belief Changes: 4.38 million people/year
Note📊 Details
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): 100 thousand posts/day (95% CI: 5,000 posts/day - 1 million posts/day)
- 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
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 the distribution of SE Bot Annual Modeled Belief Changes across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that SE Bot Annual Modeled Belief Changes will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Special Education Peace-Dividend Expected Value: $11.4 million
Note📊 Details
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: 0.01% (95% CI: 0.0001% - 1%)
- Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $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
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 the distribution of Special Education Peace-Dividend Expected Value across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Special Education Peace-Dividend Expected Value will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
SE Bot Annual Operational Cost: $292,000
Note📊 Details
Annual cost to correct all relevant posts globally: cost_per_post * posts_per_day * days_per_year.
Inputs:
- SE Bot Cost Per Post 🔢: $0.008
- 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
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 the distribution of SE Bot Annual Operational Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that SE Bot Annual Operational Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
$1T AUM Universal-Owner Breakeven Attribution Fraction: 0.000166%
Note📊 Details
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 🔢: $292,000
- Global Listed Equity Market Capitalization 📊: $115 trillion (95% CI: $90 trillion - $140 trillion)
- Global Opportunity Cost Total 🔢: $101 trillion
- Equity Capture Share of Public Value: 20% (95% CI: 5% - 40%)
- Reference AUM for SE Bot Portfolio Case: $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
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 the distribution of $1T AUM Universal-Owner Breakeven Attribution Fraction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that $1T AUM Universal-Owner Breakeven Attribution Fraction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
$1T AUM Universal-Owner Portfolio Gain from Special Education: $17.6 million
Note📊 Details
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: 0.01% (95% CI: 0.0001% - 1%)
- Global Opportunity Cost Total 🔢: $101 trillion
- Equity Capture Share of Public Value: 20% (95% CI: 5% - 40%)
- Global Listed Equity Market Capitalization 📊: $115 trillion (95% CI: $90 trillion - $140 trillion)
- Reference AUM for SE Bot Portfolio Case: $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
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 the distribution of $1T AUM Universal-Owner Portfolio Gain from Special Education across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that $1T AUM Universal-Owner Portfolio Gain from Special Education will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
$1T AUM Universal-Owner Portfolio ROI from Special Education: 60.2:1
Note📊 Details
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 🔢: $17.6 million
- SE Bot Annual Operational Cost 🔢: $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
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 the distribution of $1T AUM Universal-Owner Portfolio ROI from Special Education across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that $1T AUM Universal-Owner Portfolio ROI from Special Education will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
SE Bot Cost Per Belief Change: $0.067
Note📊 Details
Cost per person whose belief is durably updated: cost_per_post / people_persuaded_per_post.
Inputs:
- SE Bot Cost Per Post 🔢: $0.008
- 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
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 the distribution of SE Bot Cost Per Belief Change across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that SE Bot Cost Per Belief Change will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
SE Bot Cost Per Post: $0.008
Note📊 Details
Total marginal cost to generate, screen, and post one correction reply.
Inputs:
- SE Bot LLM Cost Per Post 📊: $0.006 (95% CI: $0.002 - $0.03)
- SE Bot Platform Overhead Per Post: $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
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 the distribution of SE Bot Cost Per Post across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that SE Bot Cost Per Post will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
People Persuaded Per Post: 0.12 people/post
Note📊 Details
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: 2% (95% CI: 0.2% - 10%)
- Observer Multiplier: 20 people per post (95% CI: 2 people per post - 200 people per post)
- 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
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 the distribution of People Persuaded Per Post across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that People Persuaded Per Post will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Special Education Political Dysfunction Expected Social Value: $10.1 billion
Note📊 Details
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: 0.01% (95% CI: 0.0001% - 1%)
- 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
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 the distribution of Special Education Political Dysfunction Expected Social Value across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Special Education Political Dysfunction Expected Social Value will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Special Education Political Dysfunction Social ROI: 34.6 thousand:1
Note📊 Details
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 🔢: $10.1 billion
- SE Bot Annual Operational Cost 🔢: $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
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 the distribution of Special Education Political Dysfunction Social ROI across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Special Education Political Dysfunction Social ROI will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Special Education Peace-Dividend Social ROI: 38.9:1
Note📊 Details
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 🔢: $11.4 million
- SE Bot Annual Operational Cost 🔢: $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
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 the distribution of Special Education Peace-Dividend Social ROI across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Special Education Peace-Dividend Social ROI will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Sharing Breakeven (1 in N): 8.66 million:1
Note📊 Details
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 🔢: 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
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 the distribution of Sharing Breakeven (1 in N) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Sharing Breakeven (1 in N) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Sharing Breakeven Probability: 1 in 8.66 million
Note📊 Details
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 🔢: $0.06
- 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
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 the distribution of Sharing Breakeven Probability across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Sharing Breakeven Probability will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Sharing Opportunity Cost: $0.06
Note📊 Details
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: 0.5 minutes
- Global Average 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
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 the distribution of Sharing Opportunity Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Sharing Opportunity Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Sharing Upside/Downside Ratio: 8.7Mx
Note📊 Details
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) 🔢: $518,879
- 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
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 the distribution of Sharing Upside/Downside Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Sharing Upside/Downside Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Shirt-Induced Laughs Gained: 3.51 quadrillion laughs
Note📊 Details
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 🔢: 565 billion DALYs
- 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
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 the distribution of Shirt-Induced Laughs Gained across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Shirt-Induced Laughs Gained will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Shirt Program Expected Value per Dollar: 424 million:1
Note📊 Details
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 🔢: $84.8 quadrillion
- Shirt Cascade Probability Given Seed: 25% (95% CI: 5% - 60%)
- Shirt Seed Program Total Cost 🔢: $50 million
\[ \begin{gathered} EV_{shirt} \\ = (Value_{max} \times P_{cascade,shirt}) / C_{seed,total} \end{gathered} \] where: \[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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: \[ \begin{gathered} C_{seed,total} \\ = N_{seed,shirt} \times C_{seed,wearer} \\ = 1M \times \$50 \\ = \$50M \end{gathered} \] ✓ 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) | 0.3039 | Moderate driver |
| Shirt Seed Program Total Cost (USD) | -0.2616 | Weak driver |
| Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.2388 | 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
Simulation Results Summary: Shirt Program Expected Value per Dollar
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 424 million:1 |
| Mean (expected value) | 2038 million:1 |
| Median (50th percentile) | 805 million:1 |
| Standard Deviation | 3732 million:1 |
| 90% Range (5th-95th percentile) | [74.1 million:1, 8213 million:1] |
The histogram shows the distribution of Shirt Program Expected Value per Dollar across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Shirt Program Expected Value per Dollar will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Shirt Program ROI Ratio: 1696 million:1
Note📊 Details
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 🔢: $84.8 quadrillion
- Shirt Seed Program Total Cost 🔢: $50 million
\[ \begin{gathered} ROI_{shirt} \\ = \frac{Value_{max}}{C_{seed,total}} \\ = \frac{\$84800T}{\$50M} \\ = 1.7B \end{gathered} \] where: \[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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: \[ \begin{gathered} C_{seed,total} \\ = N_{seed,shirt} \times C_{seed,wearer} \\ = 1M \times \$50 \\ = \$50M \end{gathered} \] ✓ 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) | -0.3053 | Moderate driver |
| Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (USD) | 0.2829 | 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
Simulation Results Summary: Shirt Program ROI Ratio
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 1696 million:1 |
| Mean (expected value) | 8255 million:1 |
| Median (50th percentile) | 3758 million:1 |
| Standard Deviation | 12900 million:1 |
| 90% Range (5th-95th percentile) | [447 million:1, 31123 million:1] |
The histogram shows the distribution of Shirt Program ROI Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Shirt Program ROI Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Shirt Seed Program Total Cost: $50 million
Note📊 Details
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: 1 million of people (95% CI: 100 thousand of people - 5 million of people)
- Shirt Seed Cost per Wearer: $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
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 the distribution of Shirt Seed Program Total Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Shirt Seed Program Total Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Shirt Value per Wearer: $10.6 million
Note📊 Details
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 🔢: $84.8 quadrillion
- Global Population in 2024 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people)
\[ \begin{gathered} V_{wearer} \\ = \frac{Value_{max}}{Pop_{global}} \\ = \frac{\$84800T}{8B} \\ = \$10.6M \end{gathered} \] where: \[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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 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) | 0.9996 | Strong driver |
| Global Population in 2024 (of people) | -0.0289 | 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
Simulation Results Summary: Shirt Value per Wearer
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $10.6 million |
| Mean (expected value) | $11.9 million |
| Median (50th percentile) | $11 million |
| Standard Deviation | $5.03 million |
| 90% Range (5th-95th percentile) | [$5.36 million, $21.4 million] |
The histogram shows the distribution of Shirt Value per Wearer across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Shirt Value per Wearer will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Status Quo Average Years to First Treatment: 222 years
Note📊 Details
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 🔢: 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:169
? 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
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 the distribution of Status Quo Average Years to First Treatment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Status Quo Average Years to First Treatment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Status Quo Therapeutic Space Exploration Time: 443 years
Note📊 Details
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 🔢: 6,650 diseases
- Diseases Getting First Treatment 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:169
? 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
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 the distribution of Status Quo Therapeutic Space Exploration Time across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Status Quo Therapeutic Space Exploration Time will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Thalidomide DALYs Per Event: 41,760 DALYs
Note📊 Details
Total DALYs per US-scale thalidomide event (YLL + YLD)
Inputs:
- Thalidomide YLD Per Event 🔢: 12,960 years
- 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
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 the distribution of Thalidomide DALYs Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide DALYs Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Thalidomide Deaths Per Event: 360 deaths
Note📊 Details
Deaths per US-scale thalidomide event
Inputs:
- Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
- 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
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 the distribution of Thalidomide Deaths Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide Deaths Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Thalidomide Survivors Per Event: 540 cases
Note📊 Details
Survivors per US-scale thalidomide event
Inputs:
- Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
- 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
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 the distribution of Thalidomide Survivors Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide Survivors Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Thalidomide US Cases Prevented: 900 cases
Note📊 Details
Estimated US thalidomide cases prevented by FDA rejection
Inputs:
- Thalidomide Cases Worldwide 📊: 15,000 cases (95% CI: 10,000 cases - 20,000 cases)
- 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
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 the distribution of Thalidomide US Cases Prevented across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide US Cases Prevented will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Thalidomide YLD Per Event: 12,960 years
Note📊 Details
Years Lived with Disability per thalidomide event
Inputs:
- Thalidomide Disability Weight 📊: 0.4:1 (95% CI: 0.32:1 - 0.48:1)
- Thalidomide Survivors Per Event 🔢: 540 cases
- 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
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 the distribution of Thalidomide YLD Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide YLD Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Thalidomide YLL Per Event: 28,800 years
Note📊 Details
Years of Life Lost per thalidomide event (infant deaths)
Inputs:
- 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
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 the distribution of Thalidomide YLL Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide YLL Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
3.5% Activism Benchmark: 280 million of people
Note📊 Details
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 📊: 8 billion of people (95% CI: 7.8 billion of people - 8.2 billion of people)
- Critical Mass Threshold for Social Change 📊: 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
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 the distribution of 3.5% Activism Benchmark across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that 3.5% Activism Benchmark will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2 billion
Note📊 Details
Annual funding from 1% of global military spending redirected to DIH
Inputs:
- Global Military Spending in 2024 📊: $2.72 trillion
- 1% Reduction in Military Spending/War Costs from Treaty: 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: 11.5x
Note📊 Details
Treaty system benefit multiplier vs childhood vaccination programs
Inputs:
- Estimated Annual Global Economic Benefit from Childhood Vaccination Programs 📊: $15 billion (SE: ±$4.5 billion)
- 1% treaty Basic Annual Benefits (Peace + R&D Savings) 🔢: $172 billion
\[ \begin{gathered} k_{treaty:vax} \\ = \frac{Benefit_{peace+RD}}{Benefit_{vax,ann}} \\ = \frac{\$172B}{\$15B} \\ = 11.5 \end{gathered} \] where: \[ \begin{gathered} Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$58.6B \\ = \$172B \end{gathered} \] 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: \[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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) | -0.8963 | Strong driver |
| 1% treaty Basic Annual Benefits (Peace + R&D Savings) (USD/year) | 0.2247 | 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
Simulation Results Summary: Treaty System Benefit Multiplier vs Childhood Vaccination Programs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 11.5x |
| Mean (expected value) | 12.5x |
| Median (50th percentile) | 11.9x |
| Standard Deviation | 3.81x |
| 90% Range (5th-95th percentile) | [7.31x, 19.4x] |
The histogram shows the distribution of Treaty System Benefit Multiplier vs Childhood Vaccination Programs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty System Benefit Multiplier vs Childhood Vaccination Programs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Amortized Annual Treaty Campaign Cost: $250 million
Note📊 Details
Amortized annual campaign cost (total cost ÷ campaign duration)
Inputs:
- Treaty Campaign Duration: 4 years (95% CI: 3 years - 5 years)
- Total 1% Treaty Campaign 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
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 the distribution of Amortized Annual Treaty Campaign Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Amortized Annual Treaty Campaign Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total 1% Treaty Campaign Cost: $1 billion
Note📊 Details
Total treaty campaign cost (100% VICTORY Incentive Alignment Bonds)
Inputs:
- Viral Referendum Budget: $250 million (95% CI: $150 million - $410 million)
- Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance: $650 million (95% CI: $325 million - $1.3 billion)
- Reserve Fund / Contingency Buffer: $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
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 the distribution of Total 1% Treaty Campaign Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total 1% Treaty Campaign Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput): $0.00177
Note📊 Details
Cost per DALY averted from elimination of efficacy lag plus earlier treatment discovery from increased trial throughput. Only counts campaign cost; ignores economic benefits from funding and R&D savings.
Inputs:
- Total 1% Treaty Campaign Cost 🔢: $1 billion
- Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565 billion DALYs
\[ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}} \\ = \frac{\$1B}{565B} \\ = \$0.00177 \end{gathered} \] where: \[ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \] where: \[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \] 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 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 |
|---|---|---|
| Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (DALYs) | -0.7291 | Strong driver |
| Total 1% Treaty Campaign Cost (USD) | 0.5109 | 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
Simulation Results Summary: Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $0.00177 |
| Mean (expected value) | $0.00182 |
| Median (50th percentile) | $0.00162 |
| Standard Deviation | $0.000903 |
| 90% Range (5th-95th percentile) | [$0.000809, $0.00354] |
The histogram shows the distribution of Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion: $3.16 trillion
Note📊 Details
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 📊: $2.72 trillion
- Treaty Ratchet Terminal Redirect 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
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 the distribution of Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative Treaty Funding over 20 Years with IAB Ratchet Expansion will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Cybercrime Recovery GDP Growth Bonus (Year 15): 0.402%
Note📊 Details
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) 📊: $10.5 trillion
- Global GDP (2025) 📊: $115 trillion
- Treaty Effective Reallocation Share (Year 15) 🔢: 4.4%
- 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
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 the distribution of Treaty Cybercrime Recovery GDP Growth Bonus (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Cybercrime Recovery GDP Growth Bonus (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Cybercrime Recovery GDP Growth Bonus (Year 20): 0.53%
Note📊 Details
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) 📊: $10.5 trillion
- Global GDP (2025) 📊: $115 trillion
- Treaty Effective Reallocation Share (Year 20) 🔢: 5.8%
- 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
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 the distribution of Treaty Cybercrime Recovery GDP Growth Bonus (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Cybercrime Recovery GDP Growth Bonus (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Disease Cure Fraction (15yr, Ratchet Schedule): 100%
Note📊 Details
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 🔢: 12.3x
- Maximum Trial Capacity Multiplier (Physical Limit) 🔢: 566x
- Treaty Ratchet Terminal Redirect Share: 10% (95% CI: 1% - 19%)
- Status Quo Therapeutic Space Exploration Time 🔢: 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
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 the distribution of Treaty Disease Cure Fraction (15yr, Ratchet Schedule) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Disease Cure Fraction (15yr, Ratchet Schedule) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Disease Cure Fraction (20yr, Ratchet Schedule): 100%
Note📊 Details
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 🔢: 12.3x
- Maximum Trial Capacity Multiplier (Physical Limit) 🔢: 566x
- Treaty Ratchet Terminal Redirect Share: 10% (95% CI: 1% - 19%)
- Status Quo Therapeutic Space Exploration Time 🔢: 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
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 the distribution of Treaty Disease Cure Fraction (20yr, Ratchet Schedule) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Disease Cure Fraction (20yr, Ratchet Schedule) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Effective Reallocation Share (Year 15): 4.4%
Note📊 Details
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: 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
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 the distribution of Treaty Effective Reallocation Share (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Effective Reallocation Share (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Effective Reallocation Share (Year 20): 5.8%
Note📊 Details
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: 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
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 the distribution of Treaty Effective Reallocation Share (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Effective Reallocation Share (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Expected Cost per DALY (Risk-Adjusted): $0.177
Note📊 Details
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 ($89.0/DALY).
Inputs:
- Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) 🔢: $0.00177
- Political Success Probability 📊: 1% (95% CI: 0.1% - 10%)
\[ \begin{gathered} E[Cost_{DALY}] \\ = \frac{Cost_{treaty,DALY}}{P_{success}} \\ = \frac{\$0.00177}{1\%} \\ = \$0.177 \end{gathered} \] where: \[ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}} \\ = \frac{\$1B}{565B} \\ = \$0.00177 \end{gathered} \] where: \[ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \] where: \[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \] 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 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) | 0.5242 | Strong driver |
| Political Success Probability (rate) | -0.4688 | 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
Simulation Results Summary: Expected Cost per DALY (Risk-Adjusted)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $0.177 |
| Mean (expected value) | $1.05 |
| Median (50th percentile) | $0.864 |
| Standard Deviation | $1.01 |
| 90% Range (5th-95th percentile) | [$0.03, $2.92] |
The histogram shows the distribution of Expected Cost per DALY (Risk-Adjusted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Cost per DALY (Risk-Adjusted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Expected Treaty ROI (Risk-Adjusted): 848 thousand:1
Note📊 Details
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 🔢: 84.8 million:1
- Political Success Probability 📊: 1% (95% CI: 0.1% - 10%)
\[ \begin{gathered} E[ROI_{max}] \\ = ROI_{max} \times P_{success} \\ = 84.8M \times 1\% \\ = 848{,}000 \end{gathered} \] where: \[ \begin{gathered} ROI_{max} \\ = \frac{Value_{max}}{Cost_{campaign}} \\ = \frac{\$84800T}{\$1B} \\ = 84.8M \end{gathered} \] where: \[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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: \[ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \] Methodology: Direct Calculation
? 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) | 0.8674 | Strong driver |
| Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput (ratio) | 0.2450 | 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
Simulation Results Summary: Expected Treaty ROI (Risk-Adjusted)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 848 thousand:1 |
| Mean (expected value) | 1.01 million:1 |
| Median (50th percentile) | 175 thousand:1 |
| Standard Deviation | 2.1 million:1 |
| 90% Range (5th-95th percentile) | [47.8 thousand:1, 4.95 million:1] |
The histogram shows the distribution of Expected Treaty ROI (Risk-Adjusted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Treaty ROI (Risk-Adjusted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Expected Cost-Effectiveness vs Bed Nets Multiplier: 503x
Note📊 Details
Expected value multiplier vs bed nets (accounts for political uncertainty at 1% success rate)
Inputs:
- Bed Nets Cost per DALY 📊: $89 (95% CI: $78 - $100)
- Expected Cost per DALY (Risk-Adjusted) 🔢: $0.177
\[ \begin{gathered} E[k_{nets}] \\ = \frac{Cost_{nets}}{E[Cost_{DALY}]} \\ = \frac{\$89}{\$0.177} \\ = 503 \end{gathered} \] where: \[ \begin{gathered} E[Cost_{DALY}] \\ = \frac{Cost_{treaty,DALY}}{P_{success}} \\ = \frac{\$0.00177}{1\%} \\ = \$0.177 \end{gathered} \] where: \[ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}} \\ = \frac{\$1B}{565B} \\ = \$0.00177 \end{gathered} \] where: \[ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \] where: \[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \] 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 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) | -0.4392 | Moderate driver |
| Bed Nets Cost per DALY (USD/DALY) | 0.0134 | 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
Simulation Results Summary: Expected Cost-Effectiveness vs Bed Nets Multiplier
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 503x |
| Mean (expected value) | 603x |
| Median (50th percentile) | 103x |
| Standard Deviation | 1.2kx |
| 90% Range (5th-95th percentile) | [30.5x, 3.0kx] |
The histogram shows the distribution of Expected Cost-Effectiveness vs Bed Nets Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty HALE Gain at Year 15: 16.1 years
Note📊 Details
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) 🔢: 100%
- Global Healthy Life Expectancy (HALE) 📊: 63.3 years (SE: ±1.5 years)
- Global Life Expectancy (2024) 📊: 73.4 years (SE: ±2 years)
- Treaty Longevity HALE Gain at Year 15 🔢: 6 years
\[ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} \] 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
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 the distribution of Treaty HALE Gain at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty HALE Gain at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty HALE Value Per Capita: $2.42 million
Note📊 Details
Economic value of Treaty Trajectory HALE gains at year 15 using the standard QALY value.
Inputs:
- Treaty HALE Gain at Year 15 🔢: 16.1 years
- Standard Economic Value per QALY 📊: $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: \[ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} \] 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
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 the distribution of Treaty HALE Value Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty HALE Value Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Health Recovery GDP Growth Bonus (Year 15): 0.818%
Note📊 Details
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:
\[ \begin{gathered} g_{health,treaty,15} \\ = \frac{f_{cure,15,treaty} - d_{disease}}{-7.22} \\ = \frac{100\% - 13\%}{-7.22} \\ = 0.818\% \end{gathered} \] 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
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 the distribution of Treaty Health Recovery GDP Growth Bonus (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Health Recovery GDP Growth Bonus (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Health Recovery GDP Growth Bonus (Year 20): 0.613%
Note📊 Details
Annualized GDP growth bonus by year 20 from lower disease burden under the treaty path.
Inputs:
- Treaty Disease Cure Fraction (20yr, Ratchet Schedule) 🔢: 100%
- Disease Burden as % of GDP 📊: 13%
- Treaty Health Recovery Annualization Horizon: 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
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 the distribution of Treaty Health Recovery GDP Growth Bonus (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Health Recovery GDP Growth Bonus (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Longevity HALE Gain at Year 15: 6 years
Note📊 Details
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 📊: 20 years (95% CI: 5 years - 100 years)
- HALE Longevity Realization Share (Year 15): 30%
- Treaty Disease Cure Fraction (15yr, Ratchet Schedule) 🔢: 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
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 the distribution of Treaty Longevity HALE Gain at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Longevity HALE Gain at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
1% treaty Basic Annual Benefits (Peace + R&D Savings): $172 billion
Note📊 Details
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 🔢: $114 billion
- Annual R&D Savings from Pragmatic Trials 🔢: $58.6 billion
\[ \begin{gathered} Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$58.6B \\ = \$172B \end{gathered} \] 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: \[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \end{gathered} \] 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) | 0.7387 | Strong driver |
| Annual R&D Savings from Pragmatic Trials (USD/year) | 0.6712 | 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
Simulation Results Summary: 1% treaty Basic Annual Benefits (Peace + R&D Savings)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $172 billion |
| Mean (expected value) | $172 billion |
| Median (50th percentile) | $171 billion |
| Standard Deviation | $11.9 billion |
| 90% Range (5th-95th percentile) | [$153 billion, $192 billion] |
The histogram shows the distribution of 1% treaty Basic Annual Benefits (Peace + R&D Savings) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that 1% treaty Basic Annual Benefits (Peace + R&D Savings) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Peace Recovery GDP Growth Bonus (Year 15): 0.435%
Note📊 Details
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 🔢: $114 billion
- Global GDP (2025) 📊: $115 trillion
- Treaty Effective Reallocation Share (Year 15) 🔢: 4.4%
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
- 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
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 the distribution of Treaty Peace Recovery GDP Growth Bonus (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Peace Recovery GDP Growth Bonus (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Peace Recovery GDP Growth Bonus (Year 20): 0.573%
Note📊 Details
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 🔢: $114 billion
- Global GDP (2025) 📊: $115 trillion
- Treaty Effective Reallocation Share (Year 20) 🔢: 5.8%
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
- 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
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 the distribution of Treaty Peace Recovery GDP Growth Bonus (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Peace Recovery GDP Growth Bonus (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Personal Upside (Blended): $2.93 million
Note📊 Details
Blended personal upside under Treaty Trajectory: lifetime income gain plus valued healthy-life gains.
Inputs:
- Treaty Trajectory Lifetime Income Gain (Per Capita) 🔢: $518,879
- Treaty HALE Value Per Capita 🔢: $2.42 million
\[ \begin{gathered} Upside_{blend,treaty} \\ = \Delta Y_{lifetime,treaty} + Value_{HALE,treaty} \\ = \$519K + \$2.42M \\ = \$2.93M \end{gathered} \] 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: \[ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} \] 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 (Blended)
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
Simulation Results Summary: Treaty Personal Upside (Blended)
| 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 the distribution of Treaty Personal Upside (Blended) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Personal Upside (Blended) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Projected HALE at Year 15: 79.4 years
Note📊 Details
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) 📊: 63.3 years (SE: ±1.5 years)
- Treaty HALE Gain at 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: \[ \begin{gathered} \Delta HALE_{treaty,15} \\ = f_{cure,15,treaty} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{treaty,longevity,15} \end{gathered} \] 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
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 the distribution of Treaty Projected HALE at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Projected HALE at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Redirect GDP Growth Bonus (Year 15): 0.807%
Note📊 Details
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) 🔢: 4.4%
- GDP Growth Boost at 30% Military Reallocation: 5.5% (95% CI: 3.5% - 7.5%)
- R&D 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
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 the distribution of Treaty Redirect GDP Growth Bonus (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Redirect GDP Growth Bonus (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Redirect GDP Growth Bonus (Year 20): 1.06%
Note📊 Details
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) 🔢: 5.8%
- GDP Growth Boost at 30% Military Reallocation: 5.5% (95% CI: 3.5% - 7.5%)
- R&D 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
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 the distribution of Treaty Redirect GDP Growth Bonus (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Redirect GDP Growth Bonus (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty ROI - Historical Rate (Existing Drugs): 290 thousand:1
Note📊 Details
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 🔢: $290 trillion
- Total 1% Treaty Campaign 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
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 the distribution of Treaty ROI - Historical Rate (Existing Drugs) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty ROI - Historical Rate (Existing Drugs) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput: 84.8 million:1
Note📊 Details
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 🔢: $84.8 quadrillion
- Total 1% Treaty Campaign Cost 🔢: $1 billion
\[ \begin{gathered} ROI_{max} \\ = \frac{Value_{max}}{Cost_{campaign}} \\ = \frac{\$84800T}{\$1B} \\ = 84.8M \end{gathered} \] where: \[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \end{gathered} \] 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: \[ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \] ~ 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) | 0.8562 | Strong driver |
| Total 1% Treaty Campaign Cost (USD) | -0.4450 | 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
Simulation Results Summary: Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 84.8 million:1 |
| Mean (expected value) | 101 million:1 |
| Median (50th percentile) | 91.1 million:1 |
| Standard Deviation | 49.8 million:1 |
| 90% Range (5th-95th percentile) | [39.6 million:1, 194 million:1] |
The histogram shows the distribution of Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
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. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory Average Income at Year 15: $26,713
Note📊 Details
Average income (GDP per capita) at year 15 under the Treaty Trajectory.
Inputs:
- Treaty Trajectory GDP at Year 15 🔢: $238 trillion
- 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
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 the distribution of Treaty Trajectory Average Income at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory Average Income at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory Average Income at Year 20: $34,972
Note📊 Details
Average income (GDP per capita) at year 20 under the Treaty Trajectory.
Inputs:
- Treaty Trajectory GDP at Year 20 🔢: $322 trillion
- 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
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 the distribution of Treaty Trajectory Average Income at Year 20 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory Average Income at Year 20 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory CAGR (20 Years): 5.28%
Note📊 Details
Compound annual growth rate implied by Treaty Trajectory GDP trajectory over 20 years.
Inputs:
- Treaty Trajectory GDP at Year 20 🔢: $322 trillion
- 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
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 the distribution of Treaty Trajectory CAGR (20 Years) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory CAGR (20 Years) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory Cumulative Lifetime Income (Per Capita): $1.42 million
Note📊 Details
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) 🔢: $14,375
- Treaty Trajectory Average Income at Year 20 🔢: $34,972
- Current Trajectory Average Income at Year 20 🔢: $20,483
- Average Remaining Years (Median Person) 🔢: 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
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 the distribution of Treaty Trajectory Cumulative Lifetime Income (Per Capita) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory Cumulative Lifetime Income (Per Capita) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15): 1.43x
Note📊 Details
Treaty Trajectory GDP at year 15 as a multiple of current trajectory GDP at year 15.
Inputs:
- Treaty Trajectory GDP at Year 15 🔢: $238 trillion
- Current Trajectory GDP at 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
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 the distribution of Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20): 1.71x
Note📊 Details
Treaty Trajectory GDP at year 20 as a multiple of current trajectory GDP at year 20.
Inputs:
- Treaty Trajectory GDP at Year 20 🔢: $322 trillion
- Current Trajectory GDP at 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
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 the distribution of Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory vs Current Trajectory GDP Multiplier (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory GDP at Year 15: $238 trillion
Note📊 Details
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) 📊: $115 trillion
- Baseline Global GDP Growth Rate: 2.5%
- Treaty Redirect GDP Growth Bonus (Year 15) 🔢: 0.807%
- Treaty Peace Recovery GDP Growth Bonus (Year 15) 🔢: 0.435%
- Treaty Cybercrime Recovery GDP Growth Bonus (Year 15) 🔢: 0.402%
- 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
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 the distribution of Treaty Trajectory GDP at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory GDP at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory GDP at Year 20: $322 trillion
Note📊 Details
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) 📊: $115 trillion
- Baseline Global GDP Growth Rate: 2.5%
- Treaty Redirect GDP Growth Bonus (Year 20) 🔢: 1.06%
- Treaty Peace Recovery GDP Growth Bonus (Year 20) 🔢: 0.573%
- Treaty Cybercrime Recovery GDP Growth Bonus (Year 20) 🔢: 0.53%
- 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
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 the distribution of Treaty Trajectory GDP at Year 20 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory GDP at Year 20 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory Lifetime Income Gain (Per Capita): $518,879
Note📊 Details
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) 🔢: $1.42 million
- Current Trajectory Cumulative Lifetime Income (Per Capita) 🔢: $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
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 the distribution of Treaty Trajectory Lifetime Income Gain (Per Capita) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory Lifetime Income Gain (Per Capita) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Trajectory Lifetime Income Multiplier: 1.57x
Note📊 Details
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) 🔢: $1.42 million
- Current Trajectory Cumulative Lifetime Income (Per Capita) 🔢: $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
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 the distribution of Treaty Trajectory Lifetime Income Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Trajectory Lifetime Income Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Median After-Tax Consumable Income, Treaty (Year 15): $4,381
Note📊 Details
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 🔢: $26,713
- Military Share of Global GDP 🔢: 2.37%
- Treaty Ratchet Terminal Redirect Share: 10% (95% CI: 1% - 19%)
- Global Median-to-Mean Income Ratio 🔢: 0.203:1
- Median Share Erosion Rate (Annual): 0% (95% CI: -0.5% - 0.78%)
- Median Income Relief from Full Disease Cure: 10% (95% CI: 5% - 15%)
- Treaty Disease Cure Fraction (15yr, Ratchet Schedule) 🔢: 100%
- Effective Tax Rate on Median Earner: 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
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 the distribution of Median After-Tax Consumable Income, Treaty (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Treaty (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Median After-Tax Consumable Income, Treaty (Year 20): $5,736
Note📊 Details
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 🔢: $34,972
- Military Share of Global GDP 🔢: 2.37%
- Treaty Ratchet Terminal Redirect Share: 10% (95% CI: 1% - 19%)
- Global Median-to-Mean Income Ratio 🔢: 0.203:1
- Median Share Erosion Rate (Annual): 0% (95% CI: -0.5% - 0.78%)
- Median Income Relief from Full Disease Cure: 10% (95% CI: 5% - 15%)
- Treaty Disease Cure Fraction (20yr, Ratchet Schedule) 🔢: 100%
- Effective Tax Rate on Median Earner: 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
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 the distribution of Median After-Tax Consumable Income, Treaty (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Treaty (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cost-Effectiveness vs Bed Nets Multiplier: 50.3kx
Note📊 Details
How many times more cost-effective than bed nets (using bed net cost per DALY midpoint estimate)
Inputs:
- Bed Nets Cost per DALY 📊: $89 (95% CI: $78 - $100)
- Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) 🔢: $0.00177
\[ \begin{gathered} k_{treaty:nets} \\ = \frac{Cost_{nets}}{Cost_{treaty,DALY}} \\ = \frac{\$89}{\$0.00177} \\ = 50{,}300 \end{gathered} \] where: \[ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}} \\ = \frac{\$1B}{565B} \\ = \$0.00177 \end{gathered} \] where: \[ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \] where: \[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \] 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 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) | -0.8186 | Strong driver |
| Bed Nets Cost per DALY (USD/DALY) | 0.1316 | 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
Simulation Results Summary: Cost-Effectiveness vs Bed Nets Multiplier
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 50.3kx |
| Mean (expected value) | 59.9kx |
| Median (50th percentile) | 54.9kx |
| Standard Deviation | 27.1kx |
| 90% Range (5th-95th percentile) | [25.0kx, 111.1kx] |
The histogram shows the distribution of Cost-Effectiveness vs Bed Nets Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty vs Status Quo Median Income Multiplier (Year 20): 1.89x
Note📊 Details
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) 🔢: $5,736
- Median After-Tax Consumable Income, Status Quo (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
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 the distribution of Treaty vs Status Quo Median Income Multiplier (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty vs Status Quo Median Income Multiplier (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Treaty Campaign Leverage vs Direct Funding: 476x
Note📊 Details
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 🔢: $0.842
- Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) 🔢: $0.00177
\[ \begin{gathered} Leverage_{treaty} \\ = \frac{Cost_{direct,DALY}}{Cost_{treaty,DALY}} \\ = \frac{\$0.842}{\$0.00177} \\ = 476 \end{gathered} \] where: \[ \begin{gathered} Cost_{direct,DALY} \\ = \frac{NPV_{direct}}{DALYs_{max}} \\ = \frac{\$476B}{565B} \\ = \$0.842 \end{gathered} \] where: \[ \begin{gathered} NPV_{direct} \\ = \frac{T_{queue,trial}}{Funding_{trial,ref} \times r_{discount}} \\ = \frac{36}{\$21.8B \times 3\%} \\ = \$476B \end{gathered} \] 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: \[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \] 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: \[ \begin{gathered} Cost_{treaty,DALY} \\ = \frac{Cost_{campaign}}{DALYs_{max}} \\ = \frac{\$1B}{565B} \\ = \$0.00177 \end{gathered} \] 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) | 0.9006 | Strong driver |
| Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) (USD/DALY) | -0.8340 | 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
Simulation Results Summary: Treaty Campaign Leverage vs Direct Funding
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 476x |
| Mean (expected value) | 450x |
| Median (50th percentile) | 428x |
| Standard Deviation | 211x |
| 90% Range (5th-95th percentile) | [149x, 830x] |
The histogram shows the distribution of Treaty Campaign Leverage vs Direct Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty Campaign Leverage vs Direct Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cumulative Trial Capacity Years Over 20 Years: 247 years
Note📊 Details
Cumulative trial-capacity-equivalent years over 20-year period
Inputs:
\[ \begin{gathered} Capacity_{20yr} \\ = k_{capacity} \times 20 \\ = 12.3 \times 20 \\ = 247 \end{gathered} \] 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
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 the distribution of Cumulative Trial Capacity Years Over 20 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative Trial Capacity Years Over 20 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Ratio of Type II Error Cost to Type I Error Benefit: 3,389:1
Note📊 Details
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 🔢: 8.77 billion DALYs
- Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) 🔢: 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
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 the distribution of Ratio of Type II Error Cost to Type I Error Benefit across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Type II Error Cost to Type I Error Benefit will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024): 2.59 million DALYs
Note📊 Details
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 🔢: 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
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 the distribution of Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Unexplored Therapeutic Frontier: 99.7%
Note📊 Details
Fraction of possible drug-disease space that remains unexplored (>99%)
Inputs:
- Tested Drug-Disease Relationships: 32,500 relationships (95% CI: 15,000 relationships - 50,000 relationships)
- Possible Drug-Disease Combinations 🔢: 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
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 the distribution of Unexplored Therapeutic Frontier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Unexplored Therapeutic Frontier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Pre-WW2 US Military Spending % Lower than Current: 96.7%
Note📊 Details
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) 📊: $29 billion
- US Military Spending in 2024 📊: $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📊 Details
Upper-bound advocacy cost to match career incentives for all 535 members of Congress
Inputs:
- US Congress Members: 535 members
- Post-Office Career Value (per politician) 📊: $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
US Federal Spending per Capita: $20,299
Note📊 Details
US federal spending per capita. $6.8T total federal spending divided by 335M population.
Inputs:
- US Federal Spending (FY2024) 📊: $6.8 trillion
- US Population in 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
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 the distribution of US Federal Spending per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Federal Spending per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
US Discretionary Efficiency: 40.5%
Note📊 Details
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 🔢: $1.01 trillion
- US Federal Discretionary Spending (FY2024) 📊: $1.7 trillion
\[ \begin{gathered} E_{US,disc} \\ = 1 - \frac{W_{cat1}}{Spending_{US,disc}} \\ = 1 - \frac{\$1.01T}{\$1.7T} \\ = 40.5\% \end{gathered} \] where: \[ \begin{gathered} W_{cat1} \\ = W_{military} + W_{corporate} + W_{drugs} + W_{fossil} \\ + W_{agriculture} \\ = \$615B + \$181B + \$90B + \$50B + \$75B \\ = \$1.01T \end{gathered} \] ~ 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
Simulation Results Summary: US Discretionary Efficiency
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 40.5% |
| Mean (expected value) | 40.4% |
| Median (50th percentile) | 40.5% |
| Standard Deviation | 4.67% |
| 90% Range (5th-95th percentile) | [32.7%, 48.1%] |
The histogram shows the distribution of US Discretionary Efficiency across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Discretionary Efficiency will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
US Governance Efficiency (GDP): 83%
Note📊 Details
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) 🔢: $4.9 trillion
- US GDP (2024) 📊: $28.8 trillion
\[ \begin{gathered} E_{US,GDP} \\ = 1 - \frac{W_{total,US}}{GDP_{US}} \\ = 1 - \frac{\$4.9T}{\$28.8T} \\ = 83\% \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ~ 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
Simulation Results Summary: US Governance Efficiency (GDP)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 83% |
| Mean (expected value) | 83% |
| Median (50th percentile) | 83.1% |
| Standard Deviation | 1.31% |
| 90% Range (5th-95th percentile) | [80.8%, 85.1%] |
The histogram shows the distribution of US Governance Efficiency (GDP) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Governance Efficiency (GDP) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Category 1: Direct Spending Waste: $1.01 trillion
Note📊 Details
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 📊: $615 billion (95% CI: $500 billion - $750 billion)
- Corporate Welfare Waste 📊: $181 billion (95% CI: $150 billion - $220 billion)
- Drug War Cost 📊: $90 billion (95% CI: $60 billion - $150 billion)
- Fossil Fuel Subsidies (Explicit) 📊: $50 billion (95% CI: $30 billion - $80 billion)
- Agricultural Subsidies Deadweight Loss 📊: $75 billion (95% CI: $50 billion - $120 billion)
\[ \begin{gathered} W_{cat1} \\ = W_{military} + W_{corporate} + W_{drugs} + W_{fossil} \\ + W_{agriculture} \\ = \$615B + \$181B + \$90B + \$50B + \$75B \\ = \$1.01T \end{gathered} \]
~ 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 |
|---|---|---|
| Military Overspend (USD) | 0.8656 | Strong driver |
| Drug War Cost (USD) | 0.3211 | Moderate driver |
| Agricultural Subsidies Deadweight Loss (USD) | 0.2640 | Weak driver |
| Corporate Welfare Waste (USD) | 0.2345 | Weak driver |
| Fossil Fuel Subsidies (Explicit) (USD) | 0.1684 | 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
Simulation Results Summary: Category 1: Direct Spending Waste
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1.01 trillion |
| Mean (expected value) | $1.01 trillion |
| Median (50th percentile) | $1.01 trillion |
| Standard Deviation | $79.4 billion |
| 90% Range (5th-95th percentile) | [$882 billion, $1.14 trillion] |
The histogram shows the distribution of Category 1: Direct Spending Waste across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Category 1: Direct Spending Waste will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Category 2: Compliance Burden: $1.13 trillion
Note📊 Details
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 📊: $546 billion (95% CI: $450 billion - $650 billion)
- Regulatory Red Tape Waste 📊: $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
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 the distribution of Category 2: Compliance Burden across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Category 2: Compliance Burden will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Category 3: GDP Loss: $1.56 trillion
Note📊 Details
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 📊: $1.4 trillion (95% CI: $500 billion - $2 trillion)
- Tariff Cost (GDP Loss) 📊: $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
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 the distribution of Category 3: GDP Loss across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Category 3: GDP Loss will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Category 4: System Inefficiency: $1.2 trillion
Note📊 Details
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 📊: $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
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 the distribution of Category 4: System Inefficiency across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Category 4: System Inefficiency will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
US Waste (% GDP): 17%
Note📊 Details
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) 🔢: $4.9 trillion
- US GDP (2024) 📊: $28.8 trillion
\[ \begin{gathered} W_{US,\%GDP} \\ = \frac{W_{total,US}}{GDP_{US}} \\ = \frac{\$4.9T}{\$28.8T} \\ = 17\% \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ~ 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
Simulation Results Summary: US Waste (% GDP)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 17% |
| Mean (expected value) | 17% |
| Median (50th percentile) | 16.9% |
| Standard Deviation | 1.31% |
| 90% Range (5th-95th percentile) | [14.9%, 19.2%] |
The histogram shows the distribution of US Waste (% GDP) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Waste (% GDP) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
US Waste (QALY Equivalents): 49 million QALYs
Note📊 Details
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) 🔢: $4.9 trillion
- Medical QALY Threshold 📊: $100,000
\[ \begin{gathered} W_{US,QALY} \\ = \frac{W_{total,US}}{QALY_{threshold}} \\ = \frac{\$4.9T}{\$100K} \\ = 49M \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ~ 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
Simulation Results Summary: US Waste (QALY Equivalents)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 49 million |
| Mean (expected value) | 48.9 million |
| Median (50th percentile) | 48.8 million |
| Standard Deviation | 3.77 million |
| 90% Range (5th-95th percentile) | [43 million, 55.4 million] |
The histogram shows the distribution of US Waste (QALY Equivalents) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Waste (QALY Equivalents) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
US Gov Waste (Raw Total): $4.9 trillion
Note📊 Details
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 📊: $1.2 trillion (95% CI: $1 trillion - $1.5 trillion)
- Housing/Zoning Restrictions Cost 📊: $1.4 trillion (95% CI: $500 billion - $2 trillion)
- Military Overspend 📊: $615 billion (95% CI: $500 billion - $750 billion)
- Regulatory Red Tape Waste 📊: $580 billion (95% CI: $290 billion - $1 trillion)
- Tax Compliance Waste 📊: $546 billion (95% CI: $450 billion - $650 billion)
- Corporate Welfare Waste 📊: $181 billion (95% CI: $150 billion - $220 billion)
- Tariff Cost (GDP Loss) 📊: $160 billion (95% CI: $90 billion - $250 billion)
- Drug War Cost 📊: $90 billion (95% CI: $60 billion - $150 billion)
- Fossil Fuel Subsidies (Explicit) 📊: $50 billion (95% CI: $30 billion - $80 billion)
- Agricultural Subsidies Deadweight Loss 📊: $75 billion (95% CI: $50 billion - $120 billion)
\[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \]
~ 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) | 0.7539 | Strong driver |
| Regulatory Red Tape Waste (USD) | 0.4796 | Moderate driver |
| Healthcare System Inefficiency (USD) | 0.3563 | Moderate driver |
| Military Overspend (USD) | 0.1822 | Weak driver |
| Tax Compliance Waste (USD) | 0.1279 | Weak driver |
| Tariff Cost (GDP Loss) (USD) | 0.1191 | Weak driver |
| Drug War Cost (USD) | 0.0676 | Minimal effect |
| Agricultural Subsidies Deadweight Loss (USD) | 0.0556 | Minimal effect |
| Corporate Welfare Waste (USD) | 0.0494 | Minimal effect |
| Fossil Fuel Subsidies (Explicit) (USD) | 0.0355 | 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
Simulation Results Summary: US Gov Waste (Raw Total)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $4.9 trillion |
| Mean (expected value) | $4.89 trillion |
| Median (50th percentile) | $4.88 trillion |
| Standard Deviation | $377 billion |
| 90% Range (5th-95th percentile) | [$4.3 trillion, $5.54 trillion] |
The histogram shows the distribution of US Gov Waste (Raw Total) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Gov Waste (Raw Total) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Recoverable Capital: $2.45 trillion
Note📊 Details
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) 🔢: $4.9 trillion
\[ \begin{gathered} W_{US,recoverable} \\ = W_{total,US} \times 0.5 \\ = \$4.9T \times 0.5 \\ = \$2.45T \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ? 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
Simulation Results Summary: Recoverable Capital
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2.45 trillion |
| Mean (expected value) | $2.45 trillion |
| Median (50th percentile) | $2.44 trillion |
| Standard Deviation | $189 billion |
| 90% Range (5th-95th percentile) | [$2.15 trillion, $2.77 trillion] |
The histogram shows the distribution of Recoverable Capital across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Recoverable Capital will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
US Government Waste (Total): $4.9 trillion
Note📊 Details
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) 🔢: $4.9 trillion
- Overlap Discount Factor: 1:1
\[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ~ 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
Simulation Results Summary: US Government Waste (Total)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $4.9 trillion |
| Mean (expected value) | $4.89 trillion |
| Median (50th percentile) | $4.88 trillion |
| Standard Deviation | $377 billion |
| 90% Range (5th-95th percentile) | [$4.3 trillion, $5.54 trillion] |
The histogram shows the distribution of US Government Waste (Total) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Government Waste (Total) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
US Waste (VSL Equivalents): 357 thousand people
Note📊 Details
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) 🔢: $4.9 trillion
- DOT VSL 📊: $13.7 million
\[ \begin{gathered} W_{US,VSL} \\ = \frac{W_{total,US}}{VSL_{DOT}} \\ = \frac{\$4.9T}{\$13.7M} \\ = 357{,}000 \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] ~ 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
Simulation Results Summary: US Waste (VSL Equivalents)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 357 thousand |
| Mean (expected value) | 357 thousand |
| Median (50th percentile) | 356 thousand |
| Standard Deviation | 27,547 |
| 90% Range (5th-95th percentile) | [314 thousand, 404 thousand] |
The histogram shows the distribution of US Waste (VSL Equivalents) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Waste (VSL Equivalents) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Efficiency Gap / Treaty Funding: 180:1
Note📊 Details
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) 🔢: $4.9 trillion
- Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2 billion
\[ \begin{gathered} k_{waste:treaty} \\ = \frac{W_{total,US}}{Funding_{treaty}} \\ = \frac{\$4.9T}{\$27.2B} \\ = 180 \end{gathered} \] where: \[ \begin{gathered} W_{total,US} \\ = W_{raw,US} \times \delta_{overlap} \\ = \$4.9T \times 1 \\ = \$4.9T \end{gathered} \] where: \[ \begin{gathered} W_{raw,US} \\ = W_{health} + W_{housing} + W_{military} + W_{regulatory} \\ + W_{tax} + W_{corporate} + W_{tariffs} + W_{drugs} \\ + W_{fossil} + W_{agriculture} \\ = \$1.2T + \$1.4T + \$615B + \$580B + \$546B + \$181B + \$160B \\ + \$90B + \$50B + \$75B \\ = \$4.9T \end{gathered} \] 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
Simulation Results Summary: Efficiency Gap / Treaty Funding
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 180:1 |
| Mean (expected value) | 180:1 |
| Median (50th percentile) | 179:1 |
| Standard Deviation | 13.9:1 |
| 90% Range (5th-95th percentile) | [158:1, 204:1] |
The histogram shows the distribution of Efficiency Gap / Treaty Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Efficiency Gap / Treaty Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Current US Military Spending vs Pre-WW2 Baseline (Multiplier): 30.6x
Note📊 Details
Ratio of current US military spending to pre-WW2 baseline in constant dollars ($886B / $29B)
Inputs:
- US Military Spending in 2024 📊: $886 billion
- US Military Spending in 1939 (Constant 2024 Dollars) 📊: $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📊 Details
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) 📊: $20 billion (95% CI: $18 billion - $22 billion)
- US Total Lobbying (2024) 📊: $4.4 billion (95% CI: $3.74 billion - $5.06 billion)
- Political Effort Multiplier (US): 0.7x (95% CI: 0.4x - 1.2x)
- 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
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 the distribution of US Political Reform Investment (Total) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that US Political Reform Investment (Total) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Expected Value of a Vote (US): $0.000338
Note📊 Details
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) 📊: 1 in 60 million
- 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
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 | $2.44e-06 |
| 90% Range (5th-95th percentile) | [$0.000334, $0.000343] |
The histogram shows the distribution of Expected Value of a Vote (US) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Value of a Vote (US) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual VICTORY Incentive Alignment Bond Payout: $2.72 billion
Note📊 Details
Annual VICTORY Incentive Alignment Bond payout (treaty funding × bond percentage)
Inputs:
- Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2 billion
- Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds: 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📊 Details
Annual return percentage for VICTORY Incentive Alignment Bondholders
Inputs:
- Annual VICTORY Incentive Alignment Bond Payout 🔢: $2.72 billion
- Total 1% Treaty Campaign 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
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 the distribution of Annual Return Percentage for VICTORY Incentive Alignment Bondholders across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Return Percentage for VICTORY Incentive Alignment Bondholders will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Lives Saved per Verified Voter: 2.6 lives
Note📊 Details
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 🔢: 10.7 billion deaths
- 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
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 the distribution of Lives Saved per Verified Voter across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Lives Saved per Verified Voter will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Suffering Hours Prevented per Verified Voter: 468 thousand hours
Note📊 Details
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 🔢: 1.93 quadrillion hours
- 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
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 the distribution of Suffering Hours Prevented per Verified Voter across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Suffering Hours Prevented per Verified Voter will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Children Killed in Wars Since 1900: 102 million deaths
Note📊 Details
Estimated children under 18 killed in wars, conflicts, genocides, and policy-induced famines since 1900
Inputs:
- Total War and Conflict Deaths Since 1900: 310 million deaths (95% CI: 200 million deaths - 340 million deaths)
- Child Share of War Deaths Since 1900: 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
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 the distribution of Children Killed in Wars Since 1900 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Children Killed in Wars Since 1900 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Cumulative War Costs over 20 Years (Current Trajectory): $227 trillion
Note📊 Details
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 🔢: $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
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 the distribution of Cumulative War Costs over 20 Years (Current Trajectory) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative War Costs over 20 Years (Current Trajectory) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
GDP per Capita in Peace Timeline: $333,636
Note📊 Details
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 📊: $3,150 (SE: ±$500)
- Peace Growth Boost (8 Channels, Overlap-Corrected): 2.6% (95% CI: 1.65% - 3.55%)
- Global Average Income (2025 Baseline) 🔢: $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
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 the distribution of GDP per Capita in Peace Timeline across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that GDP per Capita in Peace Timeline will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Peace Income Multiple (How Much Richer Without War): 23.2x
Note📊 Details
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 🔢: $333,636
- Global Average Income (2025 Baseline) 🔢: $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
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 the distribution of Peace Income Multiple (How Much Richer Without War) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Peace Income Multiple (How Much Richer Without War) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Lost GDP Global from War: $2.55 quadrillion
Note📊 Details
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 🔢: $319,261
- Global Population in 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
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 the distribution of Annual Lost GDP Global from War across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Lost GDP Global from War will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Annual Lost GDP per Capita from War: $319,261
Note📊 Details
Annual GDP per capita lost due to compound war effects since 1900
Inputs:
- GDP per Capita in Peace Timeline 🔢: $333,636
- Global Average Income (2025 Baseline) 🔢: $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
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 the distribution of Annual Lost GDP per Capita from War across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Lost GDP per Capita from War will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Life-Years Lost to War Since 1900: 8.37 billion life-years
Note📊 Details
Total life-years stolen by war since 1900 (deaths x avg years lost per death)
Inputs:
- Total War and Conflict Deaths Since 1900: 310 million deaths (95% CI: 200 million deaths - 340 million deaths)
- Average Years of Life Lost per War 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
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 the distribution of Total Life-Years Lost to War Since 1900 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Life-Years Lost to War Since 1900 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Medical Toolchain Prize Overpay Multiple: 341x
Note📊 Details
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: $20 trillion (95% CI: $5 trillion - $50 trillion)
- Observed Medical Toolchain 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📊 Details
Economic value of life-years destroyed by war since 1900, at $150K/QALY
Inputs:
- Total Life-Years Lost to War Since 1900 🔢: 8.37 billion life-years
- Standard Economic Value per QALY 📊: $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
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 the distribution of QALY Value of Life Lost to War Since 1900 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that QALY Value of Life Lost to War Since 1900 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Total Historical Cost of War Since 1900: $1.48 quadrillion
Note📊 Details
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): $170 trillion
- Cumulative Property Destruction from War Since 1900: $45 trillion (95% CI: $30 trillion - $60 trillion)
- Cumulative Environmental Destruction from War Since 1900: $5 trillion (95% CI: $2 trillion - $10 trillion)
- QALY Value of Life Lost to War 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
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 the distribution of Total Historical Cost of War Since 1900 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Total Historical Cost of War Since 1900 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War-Redirect Biological Aging Control Year: 1990
Note📊 Details
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:
\[ Y_{aging,redirect} = Y_{aging,plead} = 1{,}990 = 1{,}990 \] 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
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 the distribution of War-Redirect Biological Aging Control Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War-Redirect Biological Aging Control Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War-Redirect Aging Pleading Cutoff Year: 1990
Note📊 Details
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 🔢: 1950
- War-Redirect Aging Lag After Disease Control: 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
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 the distribution of War-Redirect Aging Pleading Cutoff Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War-Redirect Aging Pleading Cutoff Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War-Redirect Disease Pleading Cutoff Year: 1950
Note📊 Details
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: 1900
- War-Redirect Medical Toolchain Bootstrap Years: 14 years (95% CI: 0 years - 40 years)
- Therapeutic Space Exploration Time at Treaty-Scale Trial Capacity 🔢: 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
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 the distribution of War-Redirect Disease Pleading Cutoff Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War-Redirect Disease Pleading Cutoff Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Excess Military Spending Above 1900 Freeze: $135 trillion
Note📊 Details
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 📊: $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:163
? 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
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 the distribution of Excess Military Spending Above 1900 Freeze across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Excess Military Spending Above 1900 Freeze will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Excess Military Spending Above 1900 Freeze in Clinical Trial Years: 29,937 years
Note📊 Details
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 🔢: $135 trillion
- Annual Global Government Spending on Clinical Trials 📊: $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
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 the distribution of Excess Military Spending Above 1900 Freeze in Clinical Trial Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Excess Military Spending Above 1900 Freeze in Clinical Trial Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War-Redirect Infectious Disease Control Year: 1950
Note📊 Details
Counterfactual calendar year for practical infectious-disease control in the aggressive medical-redirect model. Uses the calculated disease pleading cutoff year.
Inputs:
\[ \begin{gathered} Y_{infectious,redirect} \\ = Y_{disease,plead} \\ = 1{,}950 \\ = 1{,}950 \end{gathered} \] 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
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 the distribution of War-Redirect Infectious Disease Control Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War-Redirect Infectious Disease Control Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Net Trial Budget After Toolchain Reserve: $150 trillion
Note📊 Details
Cumulative military spending since the Federal Reserve era after reserving the aggressive prosecutor’s medical-toolchain prize budget.
Inputs:
- Cumulative Military Spending (Fed Era): $170 trillion
- 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📊 Details
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:
\[ Y_{disease,redirect} = Y_{disease,plead} = 1{,}950 = 1{,}950 \] 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
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 the distribution of War-Redirect Major Non-Aging Disease Control Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War-Redirect Major Non-Aging Disease Control Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Patient Slots Funded: 161 billion patient-slots
Note📊 Details
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 🔢: $150 trillion
- 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
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 the distribution of War Trial Redirect Patient Slots Funded across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War Trial Redirect Patient Slots Funded will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Patient Slots Per Living Human: 20.2 patient-slots/person
Note📊 Details
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 🔢: 161 billion patient-slots
- Global Population in 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
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 the distribution of War Trial Redirect Patient Slots Per Living Human across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War Trial Redirect Patient Slots Per Living Human will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Post-Cutoff Aging Plaintiffs: 1.78 billion plaintiffs
Note📊 Details
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 🔢: 35 years
- Annual Deaths from All Diseases and Aging Globally 📊: 55 million deaths/year (SE: ±5 million deaths/year)
- Eventually Avoidable Death Percentage: 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
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 the distribution of War Trial Redirect Post-Cutoff Aging Plaintiffs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Aging Plaintiffs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Post-Cutoff Aging Years: 35 years
Note📊 Details
Inclusive number of years in the aggressive prosecutor aging-death intake window.
Inputs:
\[ T_{post,aging} = Y_{plead,end} - Y_{aging,plead} + 1 \] 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
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 the distribution of War Trial Redirect Post-Cutoff Aging Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Aging Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Post-Cutoff Disease DALYs: 200 billion DALYs
Note📊 Details
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 🔢: 75 years
- Global Annual DALY Burden 📊: 2.88 billion DALYs/year (SE: ±150 million DALYs/year)
- Eventually Avoidable DALY Percentage: 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
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 the distribution of War Trial Redirect Post-Cutoff Disease DALYs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Disease DALYs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Post-Cutoff Disease Plaintiffs: 3.82 billion plaintiffs
Note📊 Details
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 🔢: 75 years
- Annual Deaths from All Diseases and Aging Globally 📊: 55 million deaths/year (SE: ±5 million deaths/year)
- Eventually Avoidable Death Percentage: 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
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 the distribution of War Trial Redirect Post-Cutoff Disease Plaintiffs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Disease Plaintiffs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
War Trial Redirect Post-Cutoff Disease Years: 75 years
Note📊 Details
Inclusive number of years in the aggressive prosecutor disease-death plaintiff window.
Inputs:
\[ T_{post,disease} = Y_{plead,end} - Y_{disease,plead} + 1 \] 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
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 the distribution of War Trial Redirect Post-Cutoff Disease Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that War Trial Redirect Post-Cutoff Disease Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Global Patients Willing to Participate in Clinical Trials: 1.08 billion people
Note📊 Details
Global chronic disease patients willing to participate in trials (2.4B × 44.8%)
Inputs:
- Global Population with Chronic Diseases 📊: 2.4 billion people (95% CI: 2 billion people - 2.8 billion people)
- Patient Willingness to Participate in Clinical Trials 📊: 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
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 the distribution of Global Patients Willing to Participate in Clinical Trials across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Global Patients Willing to Participate in Clinical Trials will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Disease Cure Fraction (15yr, Full Implementation): 100%
Note📊 Details
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 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year)
- Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding 🔢: 12.3x
- Wishonia Military Reallocation Physical Max Share 🔢: 87.6%
- Maximum Trial Capacity Multiplier (Physical Limit) 🔢: 566x
- 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📊 Details
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 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year)
- Pragmatic Trial Capacity Multiplier at Treaty-Scale Funding 🔢: 12.3x
- Wishonia Military Reallocation Physical Max Share 🔢: 87.6%
- Maximum Trial Capacity Multiplier (Physical Limit) 🔢: 566x
- 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📊 Details
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) 🔢: 100%
- Best-Practice Life Expectancy Gain 🔢: 10.7 years
- Life Extension from Treaty Research Acceleration 📊: 20 years (95% CI: 5 years - 100 years)
- 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
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 the distribution of Wishonia Extra HALE Gain at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Extra HALE Gain at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia HALE Gain at Year 15: 26.8 years
Note📊 Details
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) 🔢: 100%
- Global Healthy Life Expectancy (HALE) 📊: 63.3 years (SE: ±1.5 years)
- Global Life Expectancy (2024) 📊: 73.4 years (SE: ±2 years)
- Wishonia Extra HALE Gain at Year 15 🔢: 16.7 years
\[ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{wish,extra,15} \end{gathered} \] 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
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 the distribution of Wishonia HALE Gain at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia HALE Gain at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia HALE Value Per Capita: $4.02 million
Note📊 Details
Economic value of Wishonia Trajectory HALE gains at year 15 using the standard QALY value.
Inputs:
- Wishonia HALE Gain at Year 15 🔢: 26.8 years
- Standard Economic Value per QALY 📊: $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: \[ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{wish,extra,15} \end{gathered} \] 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
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 the distribution of Wishonia HALE Value Per Capita across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia HALE Value Per Capita will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Personal Upside (Blended): $40.5 million
Note📊 Details
Blended personal upside under Wishonia Trajectory: lifetime income gain plus valued healthy-life gains.
Inputs:
- Wishonia Trajectory Lifetime Income Gain (Per Capita) 🔢: $36.4 million
- Wishonia HALE Value Per Capita 🔢: $4.02 million
\[ \begin{gathered} Upside_{blend,wish} \\ = \Delta Y_{lifetime,wish} + Value_{HALE,wish} \\ = \$36.4M + \$4.02M \\ = \$40.5M \end{gathered} \] 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: \[ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{wish,extra,15} \end{gathered} \] 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 (Blended)
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
Simulation Results Summary: Wishonia Personal Upside (Blended)
| 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 the distribution of Wishonia Personal Upside (Blended) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Personal Upside (Blended) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Projected HALE at Year 15: 90.1 years
Note📊 Details
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) 📊: 63.3 years (SE: ±1.5 years)
- Wishonia HALE Gain at 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: \[ \begin{gathered} \Delta HALE_{wish,15} \\ = f_{cure,15,wish} \times \text{GLOBAL\_HALE\_GAP} \\ + \Delta HALE_{wish,extra,15} \end{gathered} \] 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
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 the distribution of Wishonia Projected HALE at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Projected HALE at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory Average Income at Year 15: $503,790
Note📊 Details
Average income (GDP per capita) at year 15 under the Wishonia Trajectory.
Inputs:
- Wishonia Trajectory GDP at Year 15 🔢: $4.48 quadrillion
- 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
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 the distribution of Wishonia Trajectory Average Income at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory Average Income at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory Average Income at Year 20: $1.16 million
Note📊 Details
Average income (GDP per capita) at year 20 under the Wishonia Trajectory.
Inputs:
- Wishonia Trajectory GDP at Year 20 🔢: $10.7 quadrillion
- 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
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 the distribution of Wishonia Trajectory Average Income at Year 20 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory Average Income at Year 20 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory CAGR (20 Years): 25.4%
Note📊 Details
Compound annual growth rate implied by Wishonia Trajectory GDP trajectory over 20 years.
Inputs:
- Wishonia Trajectory GDP at Year 20 🔢: $10.7 quadrillion
- 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
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 the distribution of Wishonia Trajectory CAGR (20 Years) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory CAGR (20 Years) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory Cumulative Lifetime Income (Per Capita): $37.3 million
Note📊 Details
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) 🔢: $14,375
- Wishonia Trajectory Average Income at Year 20 🔢: $1.16 million
- Current Trajectory Average Income at Year 20 🔢: $20,483
- Average Remaining Years (Median Person) 🔢: 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
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 the distribution of Wishonia Trajectory Cumulative Lifetime Income (Per Capita) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory Cumulative Lifetime Income (Per Capita) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15): 26.9x
Note📊 Details
Wishonia Trajectory GDP at year 15 as a multiple of current trajectory GDP at year 15.
Inputs:
- Wishonia Trajectory GDP at Year 15 🔢: $4.48 quadrillion
- Current Trajectory GDP at 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
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 the distribution of Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 15) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20): 56.7x
Note📊 Details
Wishonia Trajectory GDP at year 20 as a multiple of current trajectory GDP at year 20.
Inputs:
- Wishonia Trajectory GDP at Year 20 🔢: $10.7 quadrillion
- Current Trajectory GDP at 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
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 the distribution of Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory vs Current Trajectory GDP Multiplier (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory GDP at Year 15: $4.48 quadrillion
Note📊 Details
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) 📊: $115 trillion
- Baseline Global GDP Growth Rate: 2.5%
- Wishonia Military Reallocation Physical Max Share 🔢: 87.6%
- GDP Growth Boost at 30% Military Reallocation: 5.5% (95% CI: 3.5% - 7.5%)
- R&D Spillover Multiplier: 2x (95% CI: 1.5x - 2.5x)
- Wishonia Disease Cure Fraction (15yr, Full Implementation) 🔢: 100%
- Disease Burden as % of GDP 📊: 13%
- Global Science Opportunity Cost 📊: $4 trillion (95% CI: $2 trillion - $10 trillion)
- Global Lead Poisoning Cost 📊: $6 trillion (95% CI: $4 trillion - $8 trillion)
- Global Migration Opportunity Cost 📊: $57 trillion (95% CI: $5 trillion - $170 trillion)
- Economic Multiplier for Healthcare Investment 📊: 4.3x (95% CI: 3x - 6x)
- Economic Multiplier for 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 Science Opportunity 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
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 the distribution of Wishonia Trajectory GDP at Year 15 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory GDP at Year 15 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory GDP at Year 20: $10.7 quadrillion
Note📊 Details
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) 📊: $115 trillion
- Baseline Global GDP Growth Rate: 2.5%
- Wishonia Military Reallocation Physical Max Share 🔢: 87.6%
- GDP Growth Boost at 30% Military Reallocation: 5.5% (95% CI: 3.5% - 7.5%)
- R&D Spillover Multiplier: 2x (95% CI: 1.5x - 2.5x)
- Wishonia Disease Cure Fraction (20yr, Full Implementation) 🔢: 100%
- Disease Burden as % of GDP 📊: 13%
- Global Science Opportunity Cost 📊: $4 trillion (95% CI: $2 trillion - $10 trillion)
- Global Lead Poisoning Cost 📊: $6 trillion (95% CI: $4 trillion - $8 trillion)
- Global Migration Opportunity Cost 📊: $57 trillion (95% CI: $5 trillion - $170 trillion)
- Economic Multiplier for Healthcare Investment 📊: 4.3x (95% CI: 3x - 6x)
- Economic Multiplier for 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 Science Opportunity 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
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 the distribution of Wishonia Trajectory GDP at Year 20 across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory GDP at Year 20 will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory Lifetime Income Gain (Per Capita): $36.4 million
Note📊 Details
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) 🔢: $37.3 million
- Current Trajectory Cumulative Lifetime Income (Per Capita) 🔢: $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
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 the distribution of Wishonia Trajectory Lifetime Income Gain (Per Capita) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory Lifetime Income Gain (Per Capita) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory Lifetime Income Multiplier: 41.3x
Note📊 Details
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) 🔢: $37.3 million
- Current Trajectory Cumulative Lifetime Income (Per Capita) 🔢: $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
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 the distribution of Wishonia Trajectory Lifetime Income Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory Lifetime Income Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Median After-Tax Consumable Income, Wishonia (Year 20): $194,130
Note📊 Details
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 🔢: $1.16 million
- Military Share of Global GDP 🔢: 2.37%
- Wishonia Military Reallocation Physical Max Share 🔢: 87.6%
- Global Median-to-Mean Income Ratio 🔢: 0.203:1
- Median Income Relief from Full Disease Cure: 10% (95% CI: 5% - 15%)
- Wishonia Disease Cure Fraction (20yr, Full Implementation) 🔢: 100%
- Effective Tax Rate on Median Earner: 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
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 the distribution of Median After-Tax Consumable Income, Wishonia (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Median After-Tax Consumable Income, Wishonia (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20): 33.2x
Note📊 Details
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 🔢: $10.7 quadrillion
- Treaty Trajectory GDP at 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
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 the distribution of Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
Exceedance Probability
This exceedance probability chart shows the likelihood that Wishonia Trajectory vs Treaty Trajectory GDP Multiplier (Year 20) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
External Data Sources
Parameters sourced from peer-reviewed publications, institutional databases, and authoritative reports.
ADAPTABLE Trial Cost per Patient: $929
Note📊 Details
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:1
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
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📊 Details
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:1
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
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 Terrorism Death Risk (1 in X): 30 million people
Note📊 Details
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:2
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Antidepressant Trial Exclusion Rate: 86.1%
Note📊 Details
Mean exclusion rate in antidepressant trials (86.1% of real-world patients excluded)
Source:3
✓ High confidence
Average Annual Stock Market Return: 10%
Note📊 Details
Bed Nets Cost per DALY: $89
Note📊 Details
GiveWell cost per DALY for insecticide-treated bed nets (midpoint estimate, range $78-100). DALYs (Disability-Adjusted Life Years) measure disease burden by combining years of life lost and years lived with disability. Bed nets prevent malaria deaths and are considered a gold standard benchmark for cost-effective global health interventions - if an intervention costs less per DALY than bed nets, it’s exceptionally cost-effective. GiveWell synthesizes peer-reviewed academic research with transparent, rigorous methodology and extensive external expert review.
Source:6
Uncertainty Range
Technical: 95% CI: [$78, $100] • Distribution: Normal
What this means: This estimate has moderate uncertainty. The true value likely falls between $78 and $100 (±12%). 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
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
Bullets Fired per Kill (Iraq/Afghanistan): 250 thousand rounds
Note📊 Details
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:8
Uncertainty Range
Technical: Distribution: Fixed
~ Medium confidence
Cost per 5.56mm NATO Round (Bulk): $0.4
Note📊 Details
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:7
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
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
Global Billionaire Count: 2,781 people
Note📊 Details
Number of billionaires globally (Forbes 2024 count)
Source:10
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Estimated Annual Global Economic Benefit from Childhood Vaccination Programs: $15 billion
Note📊 Details
Estimated annual global economic benefit from childhood vaccination programs (measles, polio, etc.)
Source:11
Uncertainty Range
Technical: Distribution: Lognormal (SE: $4.5 billion)
Input Distribution
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📊 Details
Disability Weight for Untreated Chronic Conditions: 0.35 weight
Note📊 Details
Disability weight for untreated chronic conditions (WHO Global Burden of Disease)
Source:5
Uncertainty Range
Technical: Distribution: Normal (SE: 0.07 weight)
Input Distribution
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📊 Details
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
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📊 Details
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:13
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
State Farm Constitutional-Ceiling Exposure Multiplier: 10x
Note📊 Details
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:14
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
CPI Multiplier: 1980 to 2024: 3.8:1
Note📊 Details
CPI inflation multiplier from 1980 to 2024 (280.48% cumulative inflation)
Source:15
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
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📊 Details
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:16
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Current Active Trials at Any Given Time: 10,000 trials
Note📊 Details
Current Clinical Trial Participation Rate: 0.06%
Note📊 Details
Global Population with Chronic Diseases: 2.4 billion people
Note📊 Details
Global population with chronic diseases
Source:19
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
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📊 Details
Average annual new drug approvals globally
Source:20
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
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📊 Details
Current global clinical trials per year
Source:23
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
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📊 Details
Annual Global Clinical Trial Participants: 1.9 million patients/year
Note📊 Details
Annual global clinical trial participants (IQVIA 2022: 1.9M post-COVID normalization)
Source:22
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
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📊 Details
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:24
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
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
Allied Military Primes Market Cap: $132 billion
Note📊 Details
Combined market capitalization of major allied European military primes (BAE Systems approx $75.8B + Thales approx $56.7B), as of June 2026
Source:25
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US Military Primes Market Cap: $836 billion
Note📊 Details
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:26
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
20th-Century Government Democide Total: 262 million deaths
Note📊 Details
Total people murdered by governments worldwide, 1900-1999 (Rummel’s democide estimate)
Source:27
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
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📊 Details
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:28
? Low confidence
Pragmatic Trial Cost per Patient: $929
Note📊 Details
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:1
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
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
Disease Burden as % of GDP: 13%
Note📊 Details
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:29
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
DOT VSL: $13.7 million
Note📊 Details
DOT Value of Statistical Life (2024). Used by federal agencies to evaluate safety regulations and quantify the economic value of mortality risk reductions.
Source:30
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Drug Development Cost (1980s): $194 million
Note📊 Details
Drug development cost in 1980s (compounded to approval, 1990 dollars)
Source:31
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
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📊 Details
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:32
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
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📊 Details
Percentage of drugs that gain at least one new indication after initial approval
Source:33
✓ High confidence
Economic Multiplier for Education Investment: 2.1x
Note📊 Details
Economic Multiplier for Healthcare Investment: 4.3x
Note📊 Details
Economic multiplier for healthcare investment (4.3x ROI). Literature range 3.0-6.0×.
Source:35
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
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📊 Details
Economic Multiplier for Military Spending: 0.6x
Note📊 Details
Economic multiplier for military spending (0.6x ROI). Literature range 0.4-1.0×.
Source:37
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
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📊 Details
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:32
Uncertainty Range
Technical: Distribution: Normal (SE: 2 years)
Input Distribution
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📊 Details
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:38
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
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📊 Details
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:16
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
FDA-Approved Drug Products: 20,000 products
Note📊 Details
FDA-Approved Unique Active Ingredients: 1,650 compounds
Note📊 Details
Unique active pharmaceutical ingredients in FDA-approved products (midpoint of 1,300-2,000 range)
Source:39
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
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📊 Details
FDA Generally Recognized as Safe (GRAS) substances (midpoint of 570-700 range)
Source:40
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
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📊 Details
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:32
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
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
Gallup Global Median Per-Capita Income (2013, PPP): $2,920
Note📊 Details
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:41
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
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 Cost per Life Saved (Maximum): $5,500
Note📊 Details
Givewell Cost per Life Saved (Minimum): $3,500
Note📊 Details
Annual Deaths from Active Combat Worldwide: 234 thousand deaths/year
Note📊 Details
Annual deaths from active combat worldwide
Source:42
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
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📊 Details
Annual deaths from state violence
Source:43
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
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📊 Details
Annual deaths from terror attacks globally
Source:44
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
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📊 Details
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:45
Uncertainty Range
Technical: Distribution: Normal (SE: 150 million DALYs/year)
Input Distribution
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📊 Details
Annual deaths from all diseases and aging globally
Source:5
Uncertainty Range
Technical: Distribution: Normal (SE: 5 million deaths/year)
Input Distribution
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📊 Details
Annual environmental damage and restoration costs from conflict
Source:46
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
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📊 Details
Annual infrastructure damage to communications from conflict
Source:46
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
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📊 Details
Annual infrastructure damage to education facilities from conflict
Source:46
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
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📊 Details
Annual infrastructure damage to energy systems from conflict
Source:46
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
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📊 Details
Annual infrastructure damage to healthcare facilities from conflict
Source:46
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
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📊 Details
Annual infrastructure damage to transportation from conflict
Source:46
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
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📊 Details
Annual infrastructure damage to water systems from conflict
Source:46
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
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📊 Details
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:48
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
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📊 Details
Annual lost productivity from conflict casualties
Source:49
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
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📊 Details
Annual PTSD and mental health costs from conflict
Source:50
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
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📊 Details
Annual refugee support costs (108.4M refugees × $1,384/year)
Source:51
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
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📊 Details
Annual trade disruption costs from currency instability
Source:52
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
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📊 Details
Annual trade disruption costs from energy price volatility
Source:52
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
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📊 Details
Annual trade disruption costs from shipping disruptions
Source:52
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
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📊 Details
Annual trade disruption costs from supply chain disruptions
Source:52
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
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📊 Details
Annual veteran healthcare costs (20-year projected)
Source:53
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
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📊 Details
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:54
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
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📊 Details
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:55
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
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📊 Details
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:56
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Cybercrime Costs (2025): $10.5 trillion
Note📊 Details
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:56
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Daily Deaths from Disease and Aging: 150 thousand deaths/day
Note📊 Details
Total global deaths per day from all disease and aging (WHO Global Burden of Disease 2024)
Source:5
Uncertainty Range
Technical: Distribution: Normal (SE: 7,500 deaths/day)
Input Distribution
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📊 Details
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:29
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
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📊 Details
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:29
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
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📊 Details
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:57
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global GDP per Capita in 1900: $3,150
Note📊 Details
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:58
Uncertainty Range
Technical: Distribution: Normal (SE: $500)
Input Distribution
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📊 Details
Annual global government spending on interventional clinical trials (~5-10% of total)
Source:59
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
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📊 Details
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:60
Uncertainty Range
Technical: Distribution: Fixed
~ Medium confidence
Global Healthy Life Expectancy (HALE): 63.3 years
Note📊 Details
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:5
Uncertainty Range
Technical: Distribution: Normal (SE: 1.5 years)
Input Distribution
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📊 Details
Total global household wealth (2022/2023 estimate)
Source:61
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Investable Financial Assets: $305 trillion
Note📊 Details
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📊 Details
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:5
Uncertainty Range
Technical: Distribution: Normal (SE: 2 years)
Input Distribution
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📊 Details
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:62
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
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📊 Details
Global median age in 2024 from UN World Population Prospects 2024 revision.
Source:64
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Government Medical Research Spending: $67.5 billion
Note📊 Details
Global government medical research spending
Source:63
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
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📊 Details
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:65
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Military Spending in 2024: $2.72 trillion
Note📊 Details
Global military spending in 2024
Source:66
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Military Spending Real CAGR (10-Year): 3.4%
Note📊 Details
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:65
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Nuclear Weapons Spending: $92 billion
Note📊 Details
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:68
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Population in 2024: 8 billion of people
Note📊 Details
Global population in 2024
Source:70
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
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📊 Details
UN World Population Prospects 2022 median projection for 2040. Interpolated midpoint between ~8.1B (2025) and 9.2B (2045).
Source:70
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Population 2045 (Projected): 9.2 billion of people
Note📊 Details
UN World Population Prospects 2022 median projection for 2045.
Source:70
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Critical Mass Threshold for Social Change: 3.5%
Note📊 Details
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:71
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
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📊 Details
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:72
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Retirement Assets: $70 trillion
Note📊 Details
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📊 Details
Global gross savings as share of GDP (World Bank, ~27% average 2023-2024)
Source:73
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
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📊 Details
Annual global spending on symptomatic disease treatment
Source:29
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
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📊 Details
Total global nuclear warhead inventory across nine nuclear-armed states. Includes deployed, reserve, and retired warheads awaiting dismantlement.
Source:74
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
YLD Proportion of Total DALYs: 0.39 proportion
Note📊 Details
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:45
Uncertainty Range
Technical: Distribution: Normal (SE: 0.03 proportion)
Input Distribution
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📊 Details
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:75
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
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📊 Details
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
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📊 Details
Percentage of human interactome (protein-protein interactions) targeted by drugs
Source:77
✓ High confidence
ICD-10 Total Codes: 14,000 codes
Note📊 Details
US Average IQ Loss from Leaded Gasoline: 2.6 IQ points
Note📊 Details
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:79
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
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📊 Details
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:80
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
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
Maximum Annual Lobbyist Salary Range: $2 million
Note📊 Details
Minimum Annual Lobbyist Salary Range: $500,000
Note📊 Details
Medical QALY Threshold: $100,000
Note📊 Details
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:83
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Medical Toolchain BRAIN Initiative Planned Budget: $4.5 billion
Note📊 Details
Planned NIH BRAIN Initiative commitment described in the BRAIN 2025 scientific vision.
Source:84
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Medical Toolchain NIH CRISPR Funding, FY2011-FY2018: $3.1 billion
Note📊 Details
Rounded NIH CRISPR-related research funding for FY2011-FY2018 from CRS Table 1.
Source:85
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Medical Toolchain Human Genome Project Cost: $2.7 billion
Note📊 Details
Approximate Human Genome Project cost used as an observed medical-toolchain anchor.
Source:76
Uncertainty Range
Technical: Distribution: Fixed
~ Medium confidence
Medical Toolchain HITECH EHR Incentive Estimated Spending: $30 billion
Note📊 Details
GAO estimate of Medicare and Medicaid EHR incentive program spending from 2011 through 2019.
Source:86
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Medical Toolchain Operation Warp Speed Potential Vaccine Awards: $18 billion
Note📊 Details
GAO-reported total potential estimated value of Operation Warp Speed vaccine candidate awards.
Source:87
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Medical Toolchain PCORnet Infrastructure Funding Anchor: $325 million
Note📊 Details
Rounded PCORI-funded PCORnet Studies amount from the Q4 2025 PCORnet dashboard. Used as a network-scale pragmatic-trial infrastructure anchor.
Source:88
Uncertainty Range
Technical: Distribution: Fixed
~ Medium confidence
GDP Growth Effect per National IQ Point: 0.11%
Note📊 Details
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:90
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
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📊 Details
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:92
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
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📊 Details
NIH annual budget (FY2024/2025)
Source:93
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
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📊 Details
Percentage of NIH budget spent on clinical trials (3.3%)
Source:94
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
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📊 Details
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:95
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
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📊 Details
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:96
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
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📊 Details
Oxford RECOVERY trial duration (found life-saving treatment in 3 months)
Source:97
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Patient Willingness to Participate in Clinical Trials: 44.8%
Note📊 Details
Patient willingness to participate in drug trials (44.8% in surveys, 88% when actually approached)
Source:98
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
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📊 Details
Funds the Department of Defense has failed to account for across seven consecutive failed audits
Source:99
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Pharma Drug Development Cost (Current System): $2.6 billion
Note📊 Details
Average cost to develop one drug in current system
Source:100
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
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📊 Details
Median lifetime revenue per successful drug (study of 361 FDA-approved drugs 1995-2014, median follow-up 13.2 years)
Source:101
✓ High confidence • 📊 Peer-reviewed
Annual Life-Years Saved by Pharmaceuticals: 149 million life-years
Note📊 Details
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:102
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
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
Pharma ROI (Current System): 1.2%
Note📊 Details
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:103
✓ High confidence • 📊 Peer-reviewed
Pharma Drug Success Rate (Current System): 10%
Note📊 Details
Percentage of drugs that reach market in current system
Source:104
✓ High confidence • 📊 Peer-reviewed
Phase I-Passed Compounds Globally: 7,500 compounds
Note📊 Details
Investigational compounds that have passed Phase I globally (midpoint of 5,000-10,000 range)
Source:32
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
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📊 Details
Phase 3 Trial Total Cost (Minimum): $20 million
Note📊 Details
Pragmatic Trial Median Cost per Patient (PMC Review): $97
Note📊 Details
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:106
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
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📊 Details
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:57
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
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📊 Details
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:57
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
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📊 Details
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:57
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
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📊 Details
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:57
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
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 Science Opportunity Cost: $4 trillion
Note📊 Details
Annual opportunity cost from underfunding high-ROI science (fusion, AI safety). Human Genome Project: $3.8B cost, $796B-1T impact (141:1 ROI). Fusion DEMO plant: $5-10B could solve energy/climate permanently. AI safety: <5% of capabilities spending despite existential stakes. Reallocating $200B from military waste at 20x multiplier = $4T foregone growth.
Source:57
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
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📊 Details
Estimated probability of treaty ratification and sustained implementation. Central estimate 1% is conservative. This assumes 99% chance of failure.
Source:108
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
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📊 Details
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:109
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
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📊 Details
Reduction in new drug approvals after 1962 Kefauver-Harris Amendment (70% drop from 43→17 drugs/year)
Source:110
✓ High confidence • Updated 1962-1970
Pre-1962 Drug Development Cost (1980 Dollars): $6.5 million
Note📊 Details
Average drug development cost before 1962 FDA efficacy regulations, adjusted to 1980 dollars (Baily 1972)
Source:111
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
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📊 Details
Pre-1962 drug development cost adjusted to 2024 dollars ($6.5M × 3.80 = $24.7M, CPI-adjusted from Baily 1972)
Source:111
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
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📊 Details
Estimated physicians conducting real-world efficacy trials pre-1962 (unverified estimate)
Source:112
? Low confidence
Total Number of Rare Diseases Globally: 7,000 diseases
Note📊 Details
Total number of rare diseases globally
Source:113
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
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📊 Details
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:114
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
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📊 Details
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:115
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
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📊 Details
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:97
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
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📊 Details
Mean age of preventable death from post-safety efficacy testing regulatory delay (Phase 2-4)
Source:5
Uncertainty Range
Technical: Distribution: Normal (SE: 3 years)
Input Distribution
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📊 Details
Pre-death suffering period during post-safety efficacy testing delay (average years lived with untreated condition while awaiting Phase 2-4 completion)
Source:5
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
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📊 Details
Total deaths in the September 11, 2001 attacks. 2,977 victims (excluding 19 hijackers). Used as a reference point for scale comparisons.
Source:118
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Global Listed Equity Market Capitalization: $115 trillion
Note📊 Details
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:116
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
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📊 Details
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:117
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
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📊 Details
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:121
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Return on Investment from Smallpox Eradication Campaign: 280:1
Note📊 Details
Standard Economic Value per QALY: $150,000
Note📊 Details
Standard economic value per QALY
Source:124
Uncertainty Range
Technical: Distribution: Normal (SE: $30,000)
Input Distribution
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📊 Details
Switzerland’s Defense Spending as Percentage of GDP: 0.7%
Note📊 Details
Switzerland GDP per Capita: $93,000
Note📊 Details
Switzerland Life Expectancy: 84 years
Note📊 Details
Switzerland life expectancy at birth. 6.5 years LONGER than US (84.0 vs 77.5) despite lower government spending as % of GDP.
Source:121
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Deaths from 9/11 Terrorist Attacks: 2,996 deaths
Note📊 Details
Deaths from 9/11 terrorist attacks
Source:2
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Thalidomide Cases Worldwide: 15,000 cases
Note📊 Details
Total thalidomide birth defect cases worldwide (1957-1962)
Source:130
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
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📊 Details
Disability weight for thalidomide survivors (limb deformities, organ damage)
Source:131
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
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📊 Details
Mortality rate for thalidomide-affected infants (died within first year)
Source:130
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
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📊 Details
Average lifespan for thalidomide survivors
Source:131
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
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📊 Details
US share of world population in 1960
Source:132
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
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📊 Details
Phase 3 cost per patient (median from FDA study)
Source:133
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
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📊 Details
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:134
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
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📊 Details
Annual US cost of Alzheimer’s disease (direct and indirect)
Source:135
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
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📊 Details
Annual US cost of cancer (direct and indirect)
Source:136
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
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 Diabetes Annual Cost: $327 billion
Note📊 Details
Annual US cost of diabetes (direct and indirect)
Source:138
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
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📊 Details
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:139
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US Federal Discretionary Spending (FY2024): $1.7 trillion
Note📊 Details
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:139
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US GDP (2024): $28.8 trillion
Note📊 Details
US GDP in 2024 dollars for calculating policy costs as percentage of GDP.
Source:140
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Agricultural Subsidies Deadweight Loss: $75 billion
Note📊 Details
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:141
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
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📊 Details
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:57
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
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📊 Details
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:142
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
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📊 Details
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:143
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
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📊 Details
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:144
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
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📊 Details
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:145
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
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
Military Overspend: $615 billion
Note📊 Details
US military spending above ‘Strict Deterrence’ baseline. Current budget ~$900B supports global power projection (750+ bases). Strict Deterrence (nuclear triad $95B, Coast Guard $14B, National Guard $33B, Missile Defense $28B, Cyber $15B, defensive Navy/Air Force $100B) = ~$285B. Delta: $900B - $285B = $615B ‘Hegemony Tax’. [CATEGORY 1: Direct Spending]
Source:57
Uncertainty Range
Technical: 95% CI: [$500 billion, $750 billion] • Distribution: Normal (SE: $75 billion)
What this means: This estimate has moderate uncertainty. The true value likely falls between $500 billion and $750 billion (±20%). 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
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📊 Details
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:57
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
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📊 Details
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:146
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
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📊 Details
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:147
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
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📊 Details
Annual US cost of heart disease and stroke (direct and indirect)
Source:148
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
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📊 Details
US military spending in 1939 (pre-WW2 baseline) in constant 2024 dollars
Source:153
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US Military Spending at WW2 Peak (Constant 2024 Dollars): $1.42 trillion
Note📊 Details
US military spending at WW2 peak (1945) in constant 2024 dollars
Source:153
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US Military Spending in 1947 (Constant 2024 Dollars): $176 billion
Note📊 Details
US military spending in 1947 (post-WW2 trough, 2 years after peak) in constant 2024 dollars
Source:153
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US Military Spending in 2024: $886 billion
Note📊 Details
US military spending in 2024 in constant dollars
Source:153
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US Military Spending as Percentage of GDP: 3.5%
Note📊 Details
US Population in 2024: 335 million people
Note📊 Details
US population in 2024
Source:155
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
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📊 Details
Senators needed for treaty ratification (2/3 majority per Article II, Section 2)
Source:156
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
US Federal Campaign Spending (2024): $20 billion
Note📊 Details
Total US federal election spending in 2024 cycle including presidential, congressional, party committees, and PACs. Source: FEC Statistical Summary 2024.
Source:157
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
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📊 Details
Total US federal lobbying expenditure in 2024 (record year). Source: OpenSecrets.
Source:158
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
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📊 Details
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:159
Uncertainty Range
Technical: Distribution: Fixed
✓ High confidence
Valley of Death Attrition Rate: 40%
Note📊 Details
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:160
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
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📊 Details
Value of Statistical Life (conservative estimate)
Source:161
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
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📊 Details
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
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📊 Details
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:162
~ Medium confidence
1900 Military Spending Freeze Baseline: $66.1 billion
Note📊 Details
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:163
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
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📊 Details
Cost-Effectiveness Threshold ($50,000/QALY): $50,000
Note📊 Details
Cost-effectiveness threshold widely used in US health economics ($50,000/QALY, from 1980s dialysis costs)
Source:165
✓ High confidence
Core Definitions
Fundamental parameters and constants used throughout the analysis.
ADAPTABLE Trial Patients Enrolled: 15,076 patients
Note📊 Details
Patients enrolled in ADAPTABLE trial (PCORnet 2016-2019). Enrolled across 40 clinical sites. Precise count from trial completion records.
Core definition
Annual Working Hours: 2,000 hours/year
Note📊 Details
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📊 Details
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
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
Average Life Extension per Beneficiary: 12 years
Note📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
G20 countries lobbying budget
Core definition
US Lobbying Campaign Budget: $50 million
Note📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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📊 Details
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
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📊 Details
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
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📊 Details
Number of sovereign heads of state/government
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Concentrated Interest Sector Market Cap: $5 trillion
Note📊 Details
Estimated combined market capitalization of concentrated interest opposition (defense, fossil fuel, etc.)
Core definition
Corporate Damages Drugs Never Developed Deaths: 300 million deaths
Note📊 Details
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📊 Details
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
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📊 Details
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
Percentage of Budget Military Sector Keeps Under 1% Treaty: 99%
Note📊 Details
Percentage of budget the military sector keeps under 1% treaty
Core definition
Acquisition Premium Multiplier: 1.8x
Note📊 Details
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
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📊 Details
Fraction of shares required for board control (50% + 1 share)
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Destructive Economy Base Year: 2025
Note📊 Details
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📊 Details
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📊 Details
Years to reach full pragmatic trial platform adoption
Core definition
Pragmatic Trial Platform Core Framework Annual OPEX: $18.9 million
Note📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
Target pragmatic trial cost per patient in USD
Core definition
Pragmatic Trial Platform One-Time Build Cost: $40 million
Note📊 Details
Pragmatic trial platform one-time build cost (central estimate)
Core definition
Pragmatic Trial Platform One-Time Build Cost (Maximum): $46 million
Note📊 Details
Pragmatic trial platform one-time build cost (high estimate)
Core definition
DIH Broader Initiatives Annual OPEX: $21.1 million
Note📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
Minimum investment for family offices
Core definition
Baseline Global GDP Growth Rate: 2.5%
Note📊 Details
Status-quo baseline annual global GDP growth rate.
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Activation Reward per Verified Participant: $5
Note📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
Global-to-US Political Cost Ratio: 5:1
Note📊 Details
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
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
Human Laughs per Day (Average Adult): 17 laughs
Note📊 Details
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
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📊 Details
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
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📊 Details
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
Activist Stake Fraction: 0.05:1
Note📊 Details
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 the universal-owner index funds that hold 60-75% of every prime 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
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📊 Details
Minimum investment for institutional investors
Core definition
Leaded Gasoline Era Duration: 73 years
Note📊 Details
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📊 Details
Maximum bond investment for lobbyist incentives
Core definition
P(Success | Loving Takeover Funded): 0.95:1
Note📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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📊 Details
Standard time horizon for NPV analysis
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Peace Dividend Conflict Elasticity: 1:1
Note📊 Details
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
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📊 Details
Direct fiscal savings from 1% military spending reduction (high confidence)
Core definition
Assumed Long-Term Real Return for Investment Compounding: 13%
Note📊 Details
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📊 Details
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
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
Pre-1962 Validation Years: 77 years
Note📊 Details
Years of empirical validation for physician-led pragmatic trials (1883-1960)
Core definition
Prize Pool Participation Rate: 1%
Note📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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📊 Details
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
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📊 Details
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
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📊 Details
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
Annualization horizon for the treaty health recovery GDP-drag term.
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Treaty Ratchet Terminal Redirect Share: 10%
Note📊 Details
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
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📊 Details
1% reduction in military spending/war costs from treaty
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Trial-Relevant Diseases: 1,000 diseases
Note📊 Details
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
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📊 Details
Total members of US Congress (100 senators + 435 representatives)
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Overlap Discount Factor: 1:1
Note📊 Details
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📊 Details
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
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📊 Details
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📊 Details
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📊 Details
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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
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📊 Details
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📊 Details
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📊 Details
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
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📊 Details
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
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