Futarchy

#Definition

Futarchy is a proposed governance system where elected representatives define measures of national welfare, and prediction markets determine which policies will maximize those metrics. The slogan is "Vote on values, bet on beliefs": democracy chooses what to optimize, markets choose how.

Economist Robin Hanson introduced futarchy in 2000 as an alternative to traditional democratic decision-making. The system aims to harness the information-aggregating power of markets to make better policy decisions while retaining democratic input on fundamental values and goals.

#Why It Matters in Prediction Markets

Futarchy represents the most ambitious application of prediction market theory: using markets not just to forecast events but to guide collective decisions. Understanding futarchy illuminates both the potential and limitations of prediction markets.

Information aggregation at scale

Markets aggregate dispersed information better than committees, polls, or expert panels. Futarchy applies this insight to policy, theoretically producing decisions informed by the collective knowledge of all market participants.

Incentive alignment

In traditional politics, voters and politicians often lack incentive to be well-informed or truthful. Futarchy forces participants to put money behind their beliefs, filtering out cheap talk and rewarding accurate forecasting.

Separating values from predictions

Much political conflict conflates disagreements about values (what should we optimize?) with disagreements about facts (what will achieve our goals?). Futarchy separates these, allowing democracy to handle values while markets handle predictions.

Blueprint for DAOs and organizations

Even if national futarchy remains theoretical, the concept influences cryptocurrency governance (DAOs), corporate decision-making, and experimental communities. Prediction markets for organizational decisions draw directly from futarchy concepts.

#How It Works

#The Basic Process

1. Define welfare metric: Democratic process establishes what to maximize (GDP, life satisfaction index, composite score)

2. Propose policies: Candidates, legislators, or citizens propose policy options

3. Create conditional markets: For each policy option, create a market predicting the welfare metric if that policy is implemented

4. Trade period: Participants trade based on their beliefs about each policy's effects

5. Select policy: The policy whose conditional market shows the highest predicted welfare is automatically implemented

6. Resolution: After implementation, the chosen policy's market resolves on actual welfare outcomes; other markets void

#Conditional Market Mechanics

Futarchy requires paired conditional markets:

• Market A: "Welfare metric value if Policy X is adopted"
• Market B: "Welfare metric value if Policy Y is adopted"

If Market A shows higher expected welfare than Market B, Policy X is chosen. Only Market A then resolves (since Policy X was implemented); Market B is voided with all positions refunded.

#Numerical Example

A city considers two transit policies:

Policy A (Bus Expansion)

• Conditional market predicts: 73% approval rating in 2 years

Policy B (Rail Investment)

• Conditional market predicts: 68% approval rating in 2 years

Under futarchy, Policy A is automatically selected because the market predicts higher welfare (approval rating serves as the welfare metric in this example).

The city implements bus expansion. In 2 years, actual approval is measured:

• If approval is 75%: Yes bettors in the Policy A market win
• Policy B market is voided; those traders receive refunds

#Information Flow

Futarchy aggregates information from multiple sources:

• Historical precedents and empirical data
• Expert analysis and domain knowledge
• Market participant beliefs and private information
• Real-time updates as conditions change

#Futarchy Governance Loop

#Examples

#Example 1: National Economic Policy

A country adopts futarchy for fiscal policy with GDP growth as the welfare metric.

Conditional markets are created:

• "GDP growth if taxes are cut by 5%"
• "GDP growth if taxes are increased by 5%"
• "GDP growth if taxes remain unchanged"

After trading, the "taxes cut" market shows highest predicted growth. Tax cuts are implemented automatically. Two years later, actual GDP growth determines market resolution.

#Example 2: Corporate Strategy

A company uses internal futarchy for major decisions, with stock price as the metric.

Employees and designated traders participate in:

• "Stock price in 12 months if we acquire Company X"
• "Stock price in 12 months if we don't acquire"

If the acquisition market shows higher expected stock price, the acquisition proceeds. This aggregates employee knowledge about synergies, integration challenges, and market reception.

#Example 3: DAO Governance

A decentralized autonomous organization uses prediction markets for protocol upgrades. Token holders define "total value locked" (TVL) as the welfare metric.

Markets ask:

• "TVL in 6 months if Upgrade Proposal A passes"
• "TVL in 6 months if current protocol continues"

The market with higher predicted TVL determines whether the upgrade is implemented.

#Example 4: Municipal Zoning

A city uses futarchy for zoning decisions, with property values as the metric.

For a proposed development:

• "Average property values in district if development approved"
• "Average property values in district if development denied"

Property owners, developers, and residents trade based on their knowledge. The market decides whether development proceeds.

#Risks, Pitfalls, and Misunderstandings

Metric gaming (Goodhart's Law)

Once a metric is chosen for optimization, it becomes a target. Participants may manipulate or satisfy the metric in ways that don't improve actual welfare. GDP can be inflated through unsustainable debt; happiness surveys can be gamed.

Manipulation by wealthy actors

Those with large capital can potentially move market prices to favor policies that benefit them personally, even if those policies harm aggregate welfare. The cost of manipulation might be worth it for concentrated benefits.

Defining welfare is hard

Agreeing on a welfare metric is itself deeply political. Is it GDP? Life expectancy? Happiness? Carbon emissions? Composite indices? The "vote on values" step may be as contentious as traditional policy debates.

Thin markets produce poor signals

Futarchy requires liquid, well-traded conditional markets. Niche policy questions may attract too few traders, producing unreliable price signals. The information aggregation benefit disappears without informed participants.

Resolution timing problems

Policy effects unfold over years or decades. Markets resolving on 2-year outcomes may favor short-term thinking. Choosing appropriate resolution timeframes is difficult and contestable.

Correlated conditions

If policy choice is predictable before markets close, traders bet on what will be chosen rather than what would work best if chosen. Market prices then reflect prediction of policy selection, not policy effectiveness.

#Practical Tips for Traders

• Separate your policy preferences from predictions: In futarchy markets, your goal is accurate prediction of outcomes given policies, not advocacy for policies you prefer

• Trade on comparative advantage: Focus on policies where you have specialized knowledge. Let others with different expertise inform markets where you're uncertain

• Consider manipulation incentives: Who benefits from each policy? Large stakeholders may trade to influence outcomes rather than to profit from accurate forecasting

• Watch for metric manipulation potential: Policies that can game the welfare metric may show artificially high predictions. Discount accordingly

• Understand resolution timing: A policy that looks good over 2 years may look terrible over 10. Markets may misprice if resolution timeframes don't match actual policy impact horizons

• Account for thin liquidity: Conditional policy markets often have low liquidity. Your trading activity may move prices significantly, affecting both execution and the policy outcome

#Risks and Challenges

1. Defining the Metric: "Welfare" is hard to measure. If we optimize for GDP, we might hurt the environment. If we optimize for happiness, how do we measure it?
2. Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure." If policy is based on a specific metric, people may game that metric to win bets, rather than improving the actual outcome.
3. Market Manipulation: A wealthy actor could manipulate the prediction market to force a bad policy that benefits them personally.
4. Complexity: Explaining to voters that "markets decided this policy" is politically difficult.

#Related Terms

• Futarchy Markets
• Prediction Market
• Conditional Market
• Decision Market
• Governance
• Information Aggregation
• Efficient Market Hypothesis
• Skin in the Game

#FAQ

#Has futarchy ever been implemented?

No government has fully adopted futarchy. However, elements appear in experimental contexts: corporate prediction markets for strategy decisions, DAO governance mechanisms that incorporate market signals, and academic experiments testing futarchy concepts. Robin Hanson has proposed pilot programs, but political implementation remains theoretical.

#How does futarchy prevent manipulation?

In theory, manipulation is expensive: pushing prices away from true values creates profit opportunities for others who trade against the manipulator. Large capital is required to sustain artificial prices. However, critics argue that for concentrated benefits (like favorable regulations), wealthy actors might accept manipulation losses as a cost of doing business.

#What happens if the welfare metric is poorly chosen?

This is futarchy's Achilles' heel. If the metric doesn't capture actual welfare, or can be gamed, the system optimizes the wrong thing. Goodhart's Law ("when a measure becomes a target, it ceases to be a good measure") applies. Designing robust, manipulation-resistant welfare metrics is an unsolved problem.

#How does futarchy differ from direct democracy?

Direct democracy lets citizens vote on policies directly. Futarchy separates values (what to optimize, decided democratically) from predictions (how to achieve it, decided by markets). Citizens might vote that "economic growth" matters, but markets rather than votes determine which policy best achieves growth.

#Could futarchy work for small organizations?

Potentially better than for nations. Smaller groups can more easily agree on welfare metrics (revenue, user growth, employee satisfaction), face fewer manipulation concerns due to reputation effects, and can experiment without catastrophic downside risk. Corporate and DAO applications are more practical near-term tests of futarchy concepts.