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Wishocracy

Keywords

war-on-disease, 1-percent-treaty, medical-research, public-health, peace-dividend, decentralized-trials, dfda, dih, victory-bonds, health-economics, cost-benefit-analysis, clinical-trials, drug-development, regulatory-reform, military-spending, peace-economics, decentralized-governance, wishocracy, blockchain-governance, impact-investing

Wishocracy

Wishocracy

Congratulations! You’ve passed a 1% treaty and now you’ve got $27.2B/year flowing into the 1% Treaty Fund.

But not all of it is available for allocation. First, the automatic deductions:

Allocation Share Amount Purpose
VICTORY Incentive Alignment Bond investors

10%

$2.72B

 Repay campaign funders
Political incentives (IABs)

10%

$2.72B

 Keep politicians aligned
Available for Wishocracy

80%

 $21.8B What you get to allocate

How do you avoid parasitic special interests from stealing that $21.8B or bureaucracy from wasting it all?

Wishocracy replaces committees with code and representatives with mathematics. It’s how you let 8 billion people collectively decide how to spend $21.8B without it turning into a bureaucratic nightmare.

How Wishocracy Allocates the 1% Treaty Fund: Decentralized Crowdfunding

Your decentralized institutes of health (DIH) network functions as a decentralized crowdfunding platform. Anyone can submit a campaign proposal. Wishocracy allocates funds from the 1% Treaty Fund across competing campaigns.

A diagram illustrating the flow of resources from the 1 percent Treaty Fund through the Wishocracy DIH network to various competing decentralized crowdfunding campaigns.

A diagram illustrating the flow of resources from the 1 percent Treaty Fund through the Wishocracy DIH network to various competing decentralized crowdfunding campaigns.

What your decentralized framework for drug assessment (dFDA) handles automatically:

  • Which specific treatments get tested → Companies register, patients choose
  • Which diseases get researched → Patients join trials for their conditions
  • Resource allocation within pragmatic clinical trials → Market prices and participant choices

What Wishocracy Actually Decides:

Allocation across campaign proposals competing for 1% Treaty Fund funds:

Infrastructure Campaigns

  • “Decentralized Framework for Drug Assessment Development” - $1B/year proposal
  • “Epic EHR Integration Project” - $5M one-time
  • “AWS Infrastructure Services” - $3M/year
  • “Security Audit Program” - $2M/year
  • “Alternative decentralized framework for drug assessment” - $8M/year (competing approach)

Public Goods (Market Failures)

  • “Patient Trial Subsidies Program” - $800B/year (automatic formula)
  • “Off-Patent Drug Research” - $20B/year
  • “Rare Disease Initiative” - $10B/year
  • “Negative Results Publishing Fund” - $5B/year

Service Provider Bids

  • “Data Storage Provider A” - $2B/year
  • “Data Storage Provider B” - $1.5B/year (competing)
  • “Pragmatic Clinical Trial Insurance Pool” - $10B reserve

How It Works: Pairwise Comparisons Between Campaigns

Instead of committees deciding “a framework for drug assessment gets $10B,” the global population votes using pairwise comparisons:

“What’s more important right now:”

  • “Framework for drug assessment development” vs “Alzheimer’s prize”?
  • “Epic integration” vs “Security audits”?
  • “Rare disease research” vs “An alternative drug assessment framework”?
  • “Patient subsidies” vs “Infrastructure spending”?

A visual representation of the pairwise voting process where individual users choose between two specific campaign options, which are then aggregated into a final funding distribution.

A visual representation of the pairwise voting process where individual users choose between two specific campaign options, which are then aggregated into a final funding distribution.

Millions of people make simple pairwise choices. The algorithm aggregates preferences into funding allocations.

Why This Is Actually Needed (Not “Just 3 Parameters”)

You can’t reduce this to simple formulas because:

  1. Multiple competing implementations - Which decentralized framework for drug assessment to fund? Which infrastructure provider?
  2. Trade-offs between prizes - $100B for aging reversal or $50B for Alzheimer’s + $50B for cancer?
  3. Public goods allocation - How much for off-patent drugs vs rare diseases?
  4. Service provider competition - Which companies get contracts for what amounts?

Your decentralized framework for drug assessment (dFDA) allocates resources within pragmatic clinical trials. Wishocracy allocates resources between campaigns.

The core of Wishocracy is a voting method so simple, even a congressman could do it. It’s called Aggregated Pairwise Preference Allocation, or simply, the Pairwise Slider Allocation.

Your brain can’t rank a list of 20 priorities. It gives up around item number seven138. But it’s fantastic at comparing two things.

Instead of asking, “Rank these 10,000 campaign proposals,” the system asks:

“What’s more important right now: dFDA platform development or security audits?”

Wishocracy slider interface showing pairwise comparison between proposals

Wishocracy slider interface showing pairwise comparison between proposals

You make a choice. That’s it. You do this a few times with different random pairs. It takes five minutes. Millions of other people do the same. An algorithm then takes these millions of simple, head-to-head comparisons and builds a representative, real-time ranking of infrastructure and public goods priorities.

(Remember: Wishocracy doesn’t allocate between diseases. Patients do that by choosing which trials to join. Wishocracy allocates between infrastructure campaigns and public goods that markets can’t handle.)

It’s the wisdom of crowds, but without the crowds having to talk to each other.

Why This Actually Works (Math Warning)

When millions of people make pairwise choices:

  • Random pairs prevent gaming
  • Statistical models (Bradley-Terry, PageRank)139 extract global preferences
  • Outliers cancel out
  • Wisdom of crowds emerges

Example with real numbers:

  • 5 million people vote
  • Each makes 20 comparisons
  • 100 million data points
  • Algorithm crunches numbers
  • Output: “Allocate 40% to patient subsidy programs, 25% to dFDA platform development, 15% to off-patent drug research, 10% to rare disease initiatives, 5% to security infrastructure, 5% to negative results publishing…”

A visual representation of the data aggregation process, showing how millions of individual pairwise comparisons are funneled through statistical models to generate a unified list of global priorities.

A visual representation of the data aggregation process, showing how millions of individual pairwise comparisons are funneled through statistical models to generate a unified list of global priorities.

It’s democracy without the stupidity. We still get the stupidity, but it’s evenly distributed and thus cancels out.

From Priorities to Projects

Once the Pairwise Slider Allocation tells you that “Curing Alzheimer’s” is a top priority, Wishocracy translates that priority into action.

  1. AI Breaks It Down: An AI takes the impossible goal of “Cure Alzheimer’s” and breaks it down into thousands of smaller, concrete, fundable tasks. “Cure Alzheimer’s” becomes “Map protein structures,” which becomes “Run AlphaFold on these sequences,” which becomes “Rent computing time.” Every impossible problem is just a series of possible steps.

  2. The Bounty Board: These tasks are posted to a global marketplace. It’s like eBay, but for saving humanity.

    • WANTED: A cure for Alzheimer’s. BOUNTY: $10 billion.
    • WANTED: A map of all protein misfolding patterns. BOUNTY: $500 million.

  3. The World Competes: The best teams from around the world, from MIT to some kid in a garage, bid on these tasks. Multiple approaches are funded in parallel. The ones that show promise get more funding. The ones that fail are defunded instantly. It’s venture capital, but for not dying.

Process flow illustrating how abstract priorities are converted into executed projects through AI decomposition and global competition.

Process flow illustrating how abstract priorities are converted into executed projects through AI decomposition and global competition.