Futarchy Experiment Perspective: When Governance Meets Prediction Markets, The Game and Reflection of DAO Decision-Making Paradigms

In March 2025, Optimism launched a bold governance innovation experiment. Through a mechanism called futarchy, 5 million OP tokens were invested in a 21-day social experiment. This was not only an exploration of the feasibility of prediction markets in on-chain governance but also a direct confrontation with the deep-seated contradictions of decentralized decision-making mechanisms. What is futarchy? Simply put, it allows participants to exchange real assets for conditional tokens, using market pricing to decide whether a governance proposal passes—if you believe a proposal will increase the token price, buy “pass”; otherwise, buy “reject.” This concept sounds perfect: tightly binding personal interests with collective goals, theoretically inspiring deeper thinking and more accurate judgments. But when this mechanism is actually implemented on-chain, the complexities of reality far exceed expectations.

Theoretical Origins and Practical Dilemmas of Futarchy

Economist Robin Hanson proposed futarchy in the last century, and it was hailed as a “2008 buzzword” by The New York Times. His core idea is “vote on values, bet on beliefs”—the public democratically decides what they want (values), then uses prediction markets to determine which policies best achieve those goals (beliefs). In theory, this separation allows collective wisdom to outperform individual biases. However, economist Tyler Cowen sharply questioned this: “Values and beliefs are fundamentally inseparable. People claim to pursue social equality, but their support for policies is often based on ideological preferences rather than rational predictions of policy effects.”

Optimism’s experiment faces this fundamental paradox. The core question is: if a project receives 1 million OP incentives, which protocol’s total locked value (TVL) increases the most after three months? 23 projects compete, with participants trading using OP-PLAY (simulated tokens). Ultimately, Rocket Pool, SuperForm, Balancer & Beets, Avantis, and Polynomial secured funding based on market predictions. But the embarrassing results followed: data released by the official in April 2025 showed that the projects selected by futarchy did not actually see TVL growth—Rocket Pool’s increase was 0, SuperForm decreased by $1.2M, Balancer & Beets decreased by $13.7M. Overall, these five projects’ combined TVL declined by $15.8M.

Why Prediction Markets Fail: Design Flaws and Participant Dilemmas

The root of the problem lies in indicator design. TVL is denominated in USD, which is highly correlated with the price fluctuations of mainstream assets like ETH. When Ethereum’s price rises, protocols with large ETH lockups appear to have significant increases, even if the projects themselves do nothing. As participant @joanbp pointed out: “If ETH price goes up, protocols holding large amounts of ETH look like they have huge gains, but in reality, they haven’t done anything.” This means participants are effectively “betting on coin prices” rather than “evaluating projects.”

Beyond indicator issues, the use of simulated tokens also weakens prediction accuracy. Data shows that 41% of participants engaged in hedging operations in the last three days—buying both “up” and “down” tokens to hedge risk. This “last-minute” behavior reflects high cognitive costs and information asymmetry among users. A participant, Milo, admitted: “I don’t think I brought any special insights; instead, I diluted the influence of those who truly understand the projects.”

Poor user experience exacerbates this problem. A single prediction requires six on-chain interactions, with participants averaging only 13.6 transactions each. Despite 2,262 visitors, the conversion rate is only 19%, and participation among OP governance contributors is just 13.48%. Lack of transparency is another major obstacle: 45% of projects did not disclose development plans to predictors, leading to predictions that deviate significantly from reality—Balancer’s predicted increase was actually $26.4M higher than the project’s own estimate. These data points shatter the idealistic assumptions of futarchy: collective wisdom requires sufficient participation depth, transparency, and rational incentives, not just market mechanisms.

Self-Fulfilling Prophecies: The Paradox of Governance Decisions

The core contradiction of futarchy is that it blurs the boundary between prediction markets and decision-making. In traditional prediction markets, participants forecast external events. But in futarchy governance, prediction itself becomes decision-making; market prices directly determine resource allocation. This creates a self-fulfilling cycle: if most predict a project will succeed and are incentivized accordingly, the project is more likely to receive funding and thus realize its success. Conversely, even a promising project may fail if the market is bearish and resources are lacking.

This mechanism also induces a “dilemma” for participants: one is to follow the trend and fund popular projects (but with diluted returns), or to bet on niche projects seeking outsized gains (but risking misjudgment). Ironically, in the “best predictor” rankings of this experiment, Badge Holders—recognized as OP ecosystem experts—had the lowest win rate. An anonymous account, @joanbp, who conducted 404 high-frequency trades, ranked highest, indicating that ultimately, the winners are traders, not analysts.

The Future of Futarchy: From Casino to Co-Governance

Although this experiment exposed many issues, it also opened a window for governance innovation. Futarchy essentially combines “voting” and “betting,” leveraging financial incentives to make participants pay real costs for decisions. This feature can activate those in Web3 labeled as “Degen” (speculators), transforming them from mere profit-seeking spectators into ecosystem co-governors.

The key lies in institutional design. Good indicators should meet three criteria: high measurability, ability to point in the right direction, and resistance to manipulation through simple financial tricks. Denominating TVL in USD clearly fails the last criterion. Better options might include on-chain activity, protocol innovation, or user growth across multiple dimensions. Additionally, lowering participation barriers—reducing on-chain interactions, increasing transparency, and optimizing user interfaces—these seemingly technical improvements can turn participants from “tortured gamblers” into “thoughtful governors.”

The true value of futarchy is not in perfectly solving governance problems but in revealing a possibility: DAO governance need not be dull rational deliberation; it can also be deeply gamified consensus formation. When futarchy effectively channels Degen speculation into public interest, makes betting a form of judgment, and arbitrage into contribution, it can activate the regenerative spirit of Web3 governance. The deepest insight from this experiment may be: future DAO decisions might require hybrid models—combining the transparency of democratic voting with the incentives of market prediction—where both evolve in tension to ultimately form more resilient collective decision-making mechanisms.