How to Implement a Futarchy System for Decision-Making

Futarchy is a governance mechanism where decisions are made based on prediction markets. Proposed policies are evaluated by creating conditional markets that forecast a key metric, like a token's price. Voters don't vote on proposals directly; instead, they buy and sell shares in markets tied to different policy outcomes. The system then automatically implements the policy whose conditional market predicts the highest value for the chosen metric. This guide outlines the core components for implementing a basic futarchy system on-chain, focusing on a modular smart contract architecture.

The implementation requires three primary smart contracts: a Proposal Manager, a Conditional Market Factory, and an Oracle. The Proposal Manager handles the lifecycle of governance proposals, from creation to resolution. For each proposal, the Conditional Market Factory deploys two prediction markets: one where the outcome token pays out if the proposal passes, and another where it pays out if the proposal fails. These markets use a conditional token system, like those from Gnosis Conditional Tokens, to represent shares in future states.

A trusted or decentralized Oracle is critical for resolving the markets. After a proposal's voting period ends, the oracle reports the value of the tracked metric (e.g., a 30-day time-weighted average price). The market for the winning outcome settles at 1 (full value), and the losing market settles at 0. The key logic is in the execution function, which compares the pre-market and post-market metric values. The proposal is executed only if the winning market's condition (e.g., "price increases if proposal passes") is validated by the oracle data, formally linking market sentiment to real-world outcomes.

Here is a simplified code snippet for a core resolution function in Solidity, illustrating the conditional check:

solidityCopy
function resolveProposal(uint256 proposalId) external {
    Proposal storage p = proposals[proposalId];
    require(block.timestamp > p.endTime, "Voting active");
    require(!p.resolved, "Already resolved");

    // Get metric value from oracle (e.g., token price)
    uint256 currentMetric = oracle.getMetric();

    // Determine winning outcome based on market prices before resolution
    // In practice, this compares the final prices of the YES and NO markets
    bool proposalPasses = _determineMarketWinner(proposalId);

    // Execute proposal if the predicted condition holds
    if (proposalPasses && currentMetric > p.metricAtCreation) {
        _executeProposalLogic(p.target, p.calldata);
    }
    p.resolved = true;
}

Key challenges in futarchy implementation include oracle reliability, market liquidity, and speculative attacks. A malicious actor could manipulate a thin market to force a harmful decision or exploit oracle latency. Mitigations involve using robust decentralized oracles like Chainlink, requiring substantial collateral to create proposals, and implementing liquidity mining incentives for market makers. The proposal metric must also be carefully chosen; a token price can be gamed, whereas a broader metric like "total value locked in protocol" might be more resilient.

To experiment, developers can fork frameworks like UMA's Optimistic Oracle for dispute resolution or build on top of prediction market platforms like Polymarket. The final step is integrating the futarchy module with an existing DAO's governance framework, allowing token holders to signal which proposals should enter the futarchy process. This creates a hybrid system where community sentiment initiates proposals, but their ultimate execution is determined by capitalized belief in their measurable success.

A futarchy system is a governance mechanism where decisions are made based on prediction market outcomes. To implement one, you need a solid understanding of blockchain fundamentals and smart contract development. You should be proficient in a language like Solidity or Vyper and have experience with development frameworks such as Hardhat or Foundry. Familiarity with oracles like Chainlink or Pyth is also critical, as they provide the real-world data (e.g., token prices, event outcomes) that prediction markets depend on to settle.

Your core technical stack will revolve around a prediction market platform and a governance framework. You can build your own prediction market contracts, but leveraging an existing protocol like Gnosis Conditional Tokens or Polymarket's infrastructure can accelerate development. For governance, you'll integrate with a system like OpenZeppelin Governor or a DAO framework like Aragon. The system requires a native utility token for bonding in markets and voting, and a stablecoin (like DAI or USDC) for trading prediction shares to minimize volatility.

Key smart contracts you will develop or configure include: a Market Maker contract (e.g., using an Automated Market Maker model or a limit order book) to provide liquidity for prediction shares, an Oracle Adapter to fetch and verify external data for market resolution, and a Governance Connector that executes proposals based on market outcomes. You must also design the market creation parameters, such as the question format, trading period, resolution deadline, and the bonding curve formula that determines share prices.

For local development and testing, set up a forked mainnet environment using tools like Hardhat Network or Anvil. This allows you to simulate real oracle price feeds and mainnet state. Comprehensive testing is non-negotiable; write unit tests for all contract logic and integration tests that simulate the full futarchy flow: proposal creation, market trading, oracle resolution, and outcome execution. Consider edge cases like oracle failure, market manipulation, and failed proposal execution.

Deploying to a live network introduces additional requirements. You'll need a verifiable randomness function (VRF) if your system involves random elements for dispute resolution or jury selection. Plan for upgradeability using proxy patterns (e.g., Transparent or UUPS) to patch logic bugs post-deployment. Finally, you must establish a front-end interface for users to create proposals, trade prediction shares, and view results. A basic stack includes a web3 library like ethers.js or viem and a framework like Next.js or React.

Futarchy, proposed by economist Robin Hanson, is a governance system where decisions are made based on prediction markets. The core idea is to separate two questions: "What do we want?" and "How do we get it?" A community defines a measurable metric for success (e.g., protocol revenue, token price). For any proposed policy change, two prediction markets are created: one betting the metric will be higher if the policy passes, and another betting it will be higher if it fails. The policy is implemented only if the market predicts a higher expected value for the success metric.

Implementing futarchy requires a secure, decentralized platform for creating and resolving conditional prediction markets. Most implementations are built on smart contract platforms like Ethereum. The key components are: a governance module to propose and vote on metrics, a market factory to deploy conditional prediction markets for each proposal, and an oracle to report the final value of the success metric at a future date. The Gnosis Conditional Tokens Framework is a popular building block for creating these markets.

Here is a simplified workflow for a single proposal. First, governance defines a success metric M (e.g., TVL > $100M in 90 days). A proposal P is submitted. The system mints two sets of outcome tokens: PASS-Yes and PASS-No for if the policy passes and metric is met, and FAIL-Yes and FAIL-No for if it fails. Traders buy and sell these tokens, with prices representing the market's probability-weighted belief. If the price of PASS-Yes is higher than FAIL-Yes, the policy automatically executes.

A critical technical challenge is oracle design. The system needs a trusted data feed to resolve the success metric objectively and resist manipulation. This often requires a decentralized oracle like Chainlink or a custom optimistic oracle like UMA's. The resolution logic must be codified in the smart contract. For example, a contract could query a Chainlink price feed at time T0 (proposal creation) and T+90days, then calculate the percentage change to determine if the metric was met.

Beyond the basic mechanism, practical futarchy systems must address liquidity incentives and attack vectors. Thin markets are prone to manipulation. Solutions include seeding markets with liquidity or using automated market makers (AMMs). The circularity problem—where the market price of the governance token itself is the success metric—can create perverse incentives. Using a broader metric like total protocol revenue is often more robust. Additionally, a veto council or delay mechanism can be added as a circuit-breaker for clearly harmful proposals that markets might miss.

To experiment, you can fork open-source implementations like Commons Stack's Augmented Bonding Curve which incorporates futarchy-like elements, or build a minimal prototype using the Conditional Tokens Framework. Start with a non-financial, gamified metric to test mechanics safely. The key takeaway is that futarchy replaces debate with a financial commitment to truth, aligning decision-making with verifiable outcomes rather than rhetoric or voter sentiment.

Futarchy, proposed by economist Robin Hanson, formalizes the idea of "vote on values, bet on beliefs." In a DAO context, members first vote to define a success metric (e.g., "increase protocol TVL by 20%"). Then, instead of voting directly on proposals, prediction markets are created to forecast whether a given proposal will achieve that metric. The market prices, which aggregate collective belief, determine which proposals are executed. This shifts decision-making from potentially biased voting to a financially incentivized forecasting mechanism. Key components include a proposal manager, conditional prediction markets, and an oracle to resolve the outcome.

The core implementation requires a set of smart contracts. Start with a FutarchyGovernor contract that manages the lifecycle: proposal submission, market creation, and execution. For each proposal, you must deploy two prediction markets: one that pays out if the proposal succeeds (YES token) and one if it fails (NO token). These are typically implemented as conditional tokens or binary options markets using a framework like Gnosis Conditional Tokens or a DEX like Polymarket. The market's liquidity and resulting price for the YES token represent the perceived probability of the proposal's success.

Integrating a reliable oracle is critical for resolving the market. After a predefined execution period, an oracle (like Chainlink, UMA, or a custom DAO oracle) must report on whether the success metric was met. The FutarchyGovernor contract will query this oracle to settle the conditional markets. The proposal is executed only if the YES market price is above a predefined threshold (e.g., >0.5) at the decision deadline and the oracle later confirms success. This two-stage process separates belief aggregation from truth verification. Ensure your oracle has strong security guarantees to prevent manipulation of the final outcome.

Here's a simplified code snippet for a core function using a hypothetical conditional token framework:

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function createProposalMarkets(uint proposalId) public {
    // Get proposal details
    Proposal storage p = proposals[proposalId];
    // Create condition ID based on proposal and metric
    bytes32 conditionId = keccak256(abi.encodePacked(p.metricOracle, proposalId));
    // Deploy conditional tokens for YES (outcome 1) and NO (outcome 2)
    conditionalTokens.prepareCondition(address(this), conditionId, 2);
    // Initialize market liquidity via a fixed-product market maker
    _initializeLiquidity(conditionId, p.bondAmount);
}

This function sets up the financial instruments needed for traders to bet on the proposal's outcome.

Consider key design parameters and security pitfalls. The proposal bond must be high enough to prevent spam but not prohibitive. The market resolution delay must balance giving enough time for informed trading with the need for timely decisions. Major risks include oracle failure/malfeasance, low liquidity in prediction markets leading to uninformative prices, and attempts to manipulate the market price just before the decision deadline. Mitigations include using decentralized oracles with dispute periods, seeding initial liquidity, and potentially using a time-weighted average price (TWAP) for the final decision price instead of a single block snapshot.

To deploy a futarchy system, start with a testnet implementation using existing primitives. Use the Gnosis Conditional Tokens contract for market creation and a DEX like Polymarket's framework or Omen for trading. For governance integration, fork a Compound Governor contract and modify its voting logic to check a market price threshold. Thoroughly test the oracle integration and market settlement logic. While futarchy is experimentally promising for objective decisions, it is less suited for subjective value judgments. Its effective use cases include parameter tuning, treasury investment decisions, or technical upgrade choices where outcomes are measurable on-chain.

You now have the foundational knowledge to implement a basic futarchy system. The core workflow involves: creating a prediction market for each proposal using a DEX like Gnosis Conditional Tokens, setting a resolution date based on a verifiable oracle (e.g., Chainlink), and executing the decision rule—funding proposals where the "yes" market price exceeds a predefined threshold, like 0.6 DAI. Your smart contract must manage the lifecycle: market creation, token bonding, oracle resolution, and conditional treasury disbursement.

For development, start with a testnet implementation. Use Foundry or Hardhat to write and test your contracts. Key functions to implement include createMarket(uint256 proposalId, uint256 resolutionTime) to spawn prediction markets, and executeDecision(uint256 proposalId) which checks the final market price via the oracle and triggers funding from a Gnosis Safe treasury module if the condition is met. Thoroughly test edge cases, such as oracle failure or market manipulation attempts.

Consider these advanced research directions to improve your system. Scalability: Explore layer-2 solutions like Arbitrum or Optimism to reduce market creation and trading costs. Mechanism Design: Implement LMSR (Logarithmic Market Scoring Rule) market makers for better liquidity in low-volume markets. Privacy: Use zk-SNARKs (e.g., via Aztec) to allow private voting on proposal creation without revealing individual stakes until market resolution.

Further your understanding by studying existing implementations and academic work. Review the Augur v2 protocol for decentralized oracle and market design. Examine research papers on Futarchy by Robin Hanson. Engage with the DAOstack community, as their Alchemy platform has experimented with futarchy-like modules. These resources provide critical insights into real-world challenges and solutions.

The final step is to deploy a minimum viable product (MVP) for a specific use case, such as allocating a DAO's grant fund or deciding on a protocol parameter change. Start with a small, controlled treasury and a community familiar with prediction markets. Gather feedback on usability, market liquidity, and the perceived quality of decisions made. This iterative testing is essential for refining the mechanism before any mainnet deployment with significant value at stake.