Author : Christopher Choi (@christopher_c)

This report presents an innovative paradigm to solve the fundamental governance problems faced by DAO: low participation, decision-making inefficiency, and a lack of accountability. The core thesis is the fusion of Futarchy principles, AI agents, and financial derivatives concepts to transform governance itself into a dynamic financial market. This model goes beyond simple binary prediction markets to build an ecosystem where sophisticated synthetic options, linked to governance proposals and KPI, are issued and traded.

Key findings are as follows. First, a governance-linked options market transforms a DAO's native token from a simple voting right into an essential collateral and medium of exchange for market participation: a 'casino chip.' This exponentially increases the token's trading volume and liquidity, driving an economic flywheel that creates genuine transactional demand beyond speculative interest.

Second, AI agents are a critical element for the expansion and popularization of these complex governance markets. AI performs roles such as information gathering, strategy automation, liquidity provision, and providing a customized UX, lowering the entry barrier for non-expert participants while optimizing micro-decisions at a scale beyond human capacity, all based on market principles.

Third, a reward structure that is scalar and option-like, where compensation varies continuously with the degree of KPI achievement rather than being 'winner-take-all,' allows for the design of more nuanced and performance-oriented incentives. This imposes real 'skin-in-the-game' for both proposers and participants, holding them financially accountable for poor decisions and clearly rewarding success, thereby preventing governance corruption and fostering a culture of responsibility.

Finally, this model offers practical solutions to new problems that are difficult to solve with existing governance systems, such as imbuing memecoins with purpose or managing the orderly process of a CTO(Community Takeover). By providing an in-depth analysis of this architecture's specific design, technical foundations, economic effects, and potential risks and mitigation strategies, this report offers a blueprint for DAO to evolve into information-based organizations that allocate capital and influence based on verifiable performance.

Despite being the ideal form of future organization proposed by blockchain technology, DAO face a fundamental set of three problems in reality, the 'Governance Trilemma.' The first is Apathy. Most token holders lack a direct incentive to invest time and effort in analyzing governance proposals and participating in votes, resulting in extremely low turnout. This is similar to the 'rational apathy' seen in shareholder meetings and leads to decision-making power being concentrated in the hands of a few whale investors or highly active participants.

The second is Inefficiency. Important decisions often go through long and intense debates without reaching a consensus, or they devolve into political arguments, failing to make optimal choices. This hinders organizational agility and weakens the ability to respond to market changes.

The third is a Lack of Accountability. In the existing '1-token-1-vote' system, even if a project's value declines due to a poor decision, the participants who voted in favor of that decision do not face direct financial repercussions. Proposers, too, are often free from responsibility for the outcomes of their grand promises. This absence of accountability encourages careless proposals and voting, becoming a key factor that impedes the long-term growth of the entire organization.

The Futarchy model, proposed by economist Robin Hanson, is gaining attention as a fundamental solution to these problems. The core philosophy of Futarchy is summarized by the motto, "Vote on Values, But Bet on Beliefs." This is an approach that clearly separates the governance process into two stages.

The first stage, 'Voting on Values,' is a democratic process to decide on the goals the community should pursue; that is, 'what we want.' For example, DAO members vote to define the organization's top-priority values with specific welfare metrics, such as TVL, increasing MAU, or boosting protocol revenue.

The second stage, 'Betting on Beliefs,' is the process of finding the optimal method to achieve these defined values through a market mechanism. Conditional prediction markets are opened, one assuming a policy proposal passes and the other assuming it fails. Participants then bet their money based on their 'belief' as to which outcome will better enhance the defined value (e.g., token price). After a certain period, the policy with the higher market price is adopted. This means that instead of expert opinions or political rhetoric, the policy with the highest 'expected value' as predicted by collective intelligence is chosen, making decision-making information-based.

The core argument of this report goes one step further. The next evolution of Web3 governance is not merely the introduction of prediction markets, but the creation of a complete derivatives market with the governance outcomes themselves as the underlying asset. This means modeling governance proposals not just as binary 'yes/no' betting subjects, but as synthetic financial instruments with sophisticated and varied payoff structures, much like options in Trad-Fi.

This approach fundamentally changes the nature of governance activity. Unlike traditional corporate governance, which operates mainly around legal and procedural costs, in this model, the act of governance itself creates a new asset class, 'governance performance', and the trading of this asset generates liquidity and volume, becoming a core financial activity. Governance is no longer a tax imposed on the system but an engine that drives the protocol's token economy. This new paradigm extends existing concepts like UMA Protocol's 'custom derivatives' or event derivatives into the realm of governance.

A 'winner-take-all' binary prediction market can only answer 'yes/no' questions like "Will the proposal pass?" However, the outcomes of many real-world governance decisions are not a simple dichotomy of success and failure; the degree of performance matters. This is where the importance of scalar prediction markets comes to the forefront. A scalar market is used when the outcome can take on a continuous value within a specific range, such as "What will the protocol's TVL be in three months?"

In this market, participants bet on specific figures, and the market price tends to converge on the average of the participants' predicted outcomes. When the final result is determined, participants are rewarded proportionally to the difference between their bet and the actual result. For example, in a scalar market for TVL with a range of $0 to $100 million, if the final TVL is determined to be $60 million, the long position token corresponding to $60 million would be redeemed for $0.60, and the short position token for $0.40. This enables a continuous payoff structure where the reward depends on 'how much' was achieved, which is very similar to the profit/loss structure of financial options. The Optimism Futarchy experiment, which predicted "How much will TVL increase?", is a prime example of such a scalar market.

By extending this scalar market concept, every governance proposal can be modeled as a tradable synthetic option. In this vision, a governance proposal is no longer a piece of text to be voted on, but a financial instrument representing a right to a future outcome (KPI) under a specific condition (proposal passage).

• A 'Yes' Bet as a 'Call Option': Betting on the success of a specific proposal (e.g., buying a 'pass' conditional token or a KPI-linked 'UP' token) is effectively like buying a call option. The participant is betting that if the proposal passes, the future KPI will exceed a certain target ('strike price'). If successful, the option's value increases, yielding a profit; if it fails, the investment is lost.

• A 'No' Bet as a 'Put Option': Betting on a proposal's failure is similar to buying a put option. This is the act of predicting that if the proposal passes, the KPI will actually decrease or fail to meet its target.

This structure is clearly illustrated in the Optimism experiment, where UP/DOWN tokens were compared to call/put options, respectively. Since the basic product of a prediction market, the binary contract, has the same payoff structure as a 'digital option', combining it with scalar markets opens up the possibility of implementing nearly all traditional finance option strategies (straddles, strangles, etc.) in the governance domain.

Human participation alone is insufficient to activate and scale such a complex and vast governance market. This is where autonomous Artificial Intelligence (AI) agents play an essential role, functioning not just as auxiliary tools but as core market participants and infrastructure.

• Market Creation and Information Analysis: AI can analyze vast on-chain and off-chain data to discover prediction topics or KPI targets that the community might find interesting, and automatically propose the opening of new governance markets. It can also collect and summarize real-time news, forum discussions, and social media trends related to a proposal, helping human participants make informed decisions.

• Automated Trading and Risk Management: AI trading bots can act as market makers, learning from price fluctuations in each market to provide optimal liquidity, or execute arbitrage trades using price differences between various conditional markets to increase market efficiency. Furthermore, they can automatically hedge portfolio risk for users who are overexposed to a particular position by short-selling related assets or taking opposing positions in other markets.

• Lowering Barriers to Entry and Activating Micro-Markets: The most innovative role of AI is to dramatically improve the user experience by abstracting complexity. AI can analyze a user's preferences and risk appetite and automatically convert their answer to a simple question like 'Do you support this proposal?' into a sophisticated option-buying strategy on the backend. This is the key to solving the complex UX problems pointed out in the Optimism experiment. Additionally, as Vitalik Buterin has envisioned, AI agents can automatically participate in small-scale 'micro-prediction markets' (e.g., "Will next week's developer meeting be more efficient on Monday or Tuesday?") that lack sufficient economic incentive for humans. This opens up the possibility of optimizing almost every aspect of DAO operations according to market principles.

In conclusion, AI functions as an 'equalizer' that lowers the barriers to entry into the governance market and as a 'scaler' that expands the market to a size beyond human limits. By bridging the knowledge gap required to handle complex financial derivatives and enabling all members to easily reflect their beliefs in the market, AI makes Futarchy governance more democratic and efficient.

To implement such a sophisticated governance market, a strong technical foundation is needed to support the conceptual vision. The current Web3 ecosystem has two key protocols that make this possible.

• Gnosis Conditional Tokens Framework (CTF): The CTF provides the fundamental infrastructure for building complex and interdependent prediction markets. The core of the CTF is its ability to represent outcomes of multiple combined conditions in a single token. For example, it can create an asset for a multi-conditional prediction like, "If proposal A passes AND proposal B is rejected, will the protocol's TVL exceed $100 million?" This enables sophisticated betting and hedging strategies that consider the interplay between governance proposals, supporting portfolio-level governance risk management beyond simple individual proposal predictions. The CTF utilizes the ERC-1155 standard to issue each conditional outcome ('position') as a unique token ID, ensuring free trade and transfer of ownership.

• UMA Protocol's KPI Options: The UMA Protocol offers a framework for issuing custom synthetic tokens, or 'KPI Options,' where the reward is determined by the achievement level of a specific Key Performance Indicator (KPI). For instance, if a DAO sets a goal to "achieve 100,000 Monthly Active Users (MAU) by the end of the quarter" and issues a KPI option, this option token can be designed to be redeemable for a larger reward (e.g., the DAO's native token) in proportion to the actual MAU figure achieved. UMA's greatest strength lies in its 'Optimistic Oracle.' This oracle plays the crucial role of securely and decentrally verifying and reporting various real-world data (TVL, MAU, trading volume, etc.) that do not have a direct on-chain price feed. This is the key technology that allows any hard-to-manipulate and measurable metric to become the underlying asset for a governance market.

In traditional financial markets, option prices are calculated by complex mathematical formulas like the Black-Scholes model, but in a Futarchy-based governance market, the price discovery mechanism is fundamentally different. The core of this market is that prices are formed in real-time through an Automated Market Maker (AMM).

• Collective Intelligence as Price Discovery: In a Futarchy market, the betting actions of market participants themselves determine the price. When sufficient participants and capital gather, the market price comes to reflect the collective intelligence's aggregate prediction of the event's probability. This replaces complex mathematical models with the 'invisible hand' of the market, avoiding the 'model risk' that arises from the assumptions and limitations of the model itself, and directly integrating the dispersed information held by market participants into the price.

• Liquidity Provision Mechanisms: Specially designed AMMs are used to ensure that trades can be made smoothly even in nascent markets with insufficient initial liquidity. A representative model is the Dynamic Pari-mutuel (DPM). DPM is a hybrid model that combines the pari-mutuel system—where all betting funds are pooled and distributed to the winners—with a dynamic pricing function. In a DPM, the price (payout ratio) is automatically adjusted with each trade, so traders can enter the market and place bets at any time (infinite buy-side liquidity), and they can gauge the predictive trends of other participants through real-time price changes to adjust their positions. Gnosis, for its part, provides liquidity using a Constant Product Market Maker (CPMM) similar to Uniswap or an AMM based on the Logarithmic Market Scoring Rule (LMSR), which is specialized for prediction markets.

A comparative analysis with traditional financial options is very useful for clearly understanding the innovation of Futarchy-based governance options. This helps protocol designers explore new design spaces based on familiar concepts.

The most profound difference in this comparison lies in the pricing method. In traditional finance, an option's price is the 'output' of a mathematical model that takes external variables like the underlying's volatility as inputs. In contrast, in a Futarchy market, the option's price is itself the 'source' of information aggregated by the market. The market price directly reveals the risk-neutral probability of the option being realized, which is like the market calculating its own implied volatility.

The key to moving beyond the 'winner-take-all' model lies in diversifying the payoff structures. Using the technologies of UMA and Gnosis, various non-binary reward systems can be designed.

• Linear Payouts: This is the most basic method used in UMA's KPI options, where the reward is directly proportional to the KPI achievement. For example, it can be designed as "pay 0.0001 reward tokens per $1 increase in TVL." This provides an intuitive reward based on contribution.

• Capped & Floored Payouts: By utilizing structures like Gnosis's conditional tokens or UMA's Range Bond tokens, it's possible to set upper and lower limits on the rewards. This prevents excessive rewards or losses from extreme outcomes, enhancing the financial stability of the system.

• Event-Based Expiry: UMA's milestone KPI options offer a feature where the contract expires immediately and rewards are paid out as soon as a specific goal is achieved early. For example, a goal could be "achieve $1 billion TVL within 2 years," and if the goal is met in just one year, the reward is paid out instantly. This provides a strong time-based incentive to the community.

• Combinatorial Payouts: Using the Gnosis CTF, complex payoffs can be created that combine multiple conditions, such as "pay a reward only if proposal A succeeds AND TVL is above X." This is effective for incentivizing the multi-faceted success of specific strategic goals.

Many governance tokens are primarily held (hodled) for voting rights, resulting in low velocity, which acts as a constraint on token value appreciation. A Futarchy-based derivatives market fundamentally solves this problem. In this market, the native token plays the role of essential collateral and a medium of exchange for participating in governance betting, in other words, a 'casino chip.'

Just as chips are the sole currency for participating in all games within a casino, the native token becomes the base currency for all prediction and hedging transactions within the governance market ecosystem. Participants must continuously buy and deposit the token to bet on a proposal's success or to hedge against the risk of its failure. This creates strong and continuous transactional demand for the token. According to the quantity theory of money (MV=PQ), when the money supply (M) is constant, an increase in velocity (V) signifies an increase in nominal output (PQ), i.e., an activation of economic activity, which positively impacts asset value. The activation of the governance market dramatically increases the token's velocity, endowing it with real economic utility beyond mere holding value.

It is difficult to secure deep liquidity with only a spot market. However, when a derivatives market is built on top of it, it becomes a powerful magnet attracting professional traders and institutional investors who employ sophisticated investment strategies.

• Arbitrage Opportunities: Temporary price discrepancies between different conditional markets (e.g., the market for 'token price if proposal A passes' vs. 'token price if proposal A is rejected') provide risk-free profit opportunities for arbitrageurs. Their active trading makes the market more efficient and supplies liquidity to the entire ecosystem.

• Hedging Demand: Long-term token holders and liquidity providers want to hedge against the risk of asset value decline that could occur if a governance proposal they oppose passes. They can protect their portfolios by betting on the failure of that proposal (buying a put option). This hedging demand provides stable sell-side liquidity to the market. Protocols like GMX, which distribute protocol revenue to token stakers, create a measurable cash flow, making valuation and hedging based on it even easier.

As participants with diverse motives—speculation, arbitrage, hedging—enter the market, the depth and breadth of trading increase, creating a virtuous cycle that dramatically improves the overall liquidity of the native token.

The most significant governance implication of the Futarchy derivatives model is the financialization of accountability. In traditional voting systems, the cost of a bad decision is distributed among all token holders, making it difficult for individual participants to feel a sense of responsibility. In this model, however, the outcome of a decision leads to direct financial profit or loss.

As observed, "when real money is on the line, people don't follow intuition or herd mentality but seriously analyze the long-term effects of a proposal." This system forces 'skin-in-the-game' on all participants. A proposer must bet on the market themselves to prove their conviction in their proposal's success, and they will suffer a financial loss if it fails. This acts as a natural penalty for underperforming proposers. Conversely, participants who accurately predict a proposal's success earn a financial reward.

This structure eradicates a culture of 'all talk' within DAOs and establishes a culture of accountability for actual execution and measurable performance. It also forces all proposals to have clear KPIs, reducing vague proposals and promoting transparent, data-driven governance.

This change signifies a paradigm shift in tokenomics. Whereas traditional tokenomics has focused mainly on monetary policy aspects like supply, issuance rates, and burn mechanisms, the Futarchy-derivatives model makes utility density the core of value creation. In this model, the most effective strategy to increase token value is not to control the supply, but to create as many attractive and active governance markets as possible where the token is indispensably used. In other words, the best tokenomics strategy is to build the best governance market platform.

Memecoins often operate without a clear roadmap or real utility, driven solely by community hype and speculative interest. This is the biggest threat to the sustainability of memecoin projects. A Futarchy-based governance model can be a powerful tool to bring order to this chaos and create community-driven purpose.

For example, a memecoin community could open a prediction market on a proposal like, "Will partnering with influencer X double our token holder count within 3 months?" In this process, community members participate in betting for fun, but in doing so, they collectively gather intelligence on the project's growth strategy. If the market predicts the partnership's effect will be negative, the community can avoid unnecessary expenditure and save resources. Conversely, if the market reacts positively, it becomes a strong signal of community support for the strategy, providing momentum for execution.

In this way, Futarchy can combine the powerful community engagement energy inherent in memecoins with gamified financial incentives to drive continuous participation and provide a real sense of direction for the project. This is a process of imbuing an asset that was inherently purposeless with intrinsic value through a market mechanism.

A CTO(Community Takeover) refers to the process where the community takes control to revive a project after the initial development team abandons or neglects it. As seen in success stories like TelegramSolana, CTOs are often chaotic and informal. Futarchy provides a playbook to manage this process in a systematic and objective way.

If there are several competing teams or leader candidates wanting to take over a project, a conditional market could be opened to predict, "If Team A leads versus if Team B leads, which will result in a higher token price in 6 months?" Granting leadership to the team predicted to create the most value based on the market's judgment is a far more objective method of leader selection than political debates or popularity contests.

Furthermore, the newly formed team can be forced to be accountable for its performance by being issued KPI options linked to clear KPIs (e.g., "achieve 70% of the roadmap within 6 months"). If they fail to meet the target, the options become worthless, and the new leadership faces a real financial penalty. This acts as a powerful trust mechanism, ensuring that the new leadership is evaluated on verifiable performance, not empty promises.

The essence of this approach is to artificially create a Schelling Point—a focal point for tacit coordination—through a market mechanism in a leaderless, chaotic organization. By setting a single, objective goal that all members can agree on—namely, 'maximizing token value'—and letting the market decide which strategy best aligns with that goal, it encourages a fragmented community to voluntarily rally around the most credible strategy. This is a powerful coordination mechanism that creates order out of chaos.

DAO treasury management is often decided opaquely by a small committee or distributed based on political influence. Futarchy can transform this into a transparent, data-driven capital allocation process. For example, when deciding whether to grant funds to a specific project, a prediction market could be opened: "If the DAO treasury provides a $100,000 grant to Project A, what will be the TVL(Total Value Locked) generated by Project A in one year?".

The market would predict the potential ROI(Return on Investment) of various projects, and the DAO could allocate funds to the project with the highest expected value. This shifts treasury management from subjective judgment to objective market prediction. The Optimism Futarchy experiment was a real-world attempt at such market-driven fund allocation, and although it had limitations in its choice of TVL as a metric, it clearly demonstrated the potential.

The biggest concern for Futarchy-based markets is the potential for market manipulation by 'whale' investors with significant capital. Attempts could be made to artificially inflate prices by investing large sums to force a particular proposal through.

The primary line of defense against this is the game-theoretic nature of the market itself. Manipulating the market requires betting a huge amount of capital on an incorrect prediction, so if there are participants on the other side with more information and capital, the manipulation attempt could lead to a significant loss. Indeed, in MetaDAO, there was a case where a whale tried to force a proposal through, and the community collectively counter-betted to thwart it.

Technical mitigation strategies include (1)setting betting amount caps, (2)introducing a slow mode to decelerate trading speed, (3)assigning weights through a reputation network, and (4)building an AI-based system to detect anomalous transactions.

The success or failure of a Futarchy system depends on the quality of the goal it seeks to optimize, i.e., the KPI. As the saying goes, "what gets measured gets managed," and a flawed KPI can lead to unintended negative consequences (Goodhart's Law).

A prime example of failure was the Optimism experiment, where using dollar-denominated TVL as a KPI meant that Ethereum price fluctuations distorted the TVL figures, regardless of the project's actual performance, thereby reducing the market's predictive accuracy. A good KPI must be (1)closely linked to the project's intrinsic success, (2)difficult to manipulate, and (3)objectively verifiable.

To solve this problem, a 'meta-governance' process is needed where the selection of the KPI itself is treated as an important governance agenda. It is also essential to ensure the reliability of outcome reporting by utilizing robust, decentralized oracle systems like UMA's Optimistic Oracle or Chainlink.

No matter how sophisticated a system is, it cannot be widely adopted if it is complex to use. As seen in the Optimism experiment, where participating in the prediction market required six transaction signatures, a complicated interface and high gas fees are the biggest barriers to entry for ordinary users.

Solutions to this problem include: (1)abstracting the complex trading process through a personalized, AI-based interface, enabling betting with simple interactions like a 'swipe', (2)minimizing gas fees and increasing transaction speed by utilizing Layer 2 solutions, and (3)improving wallet UX to simplify multi-signature procedures. Ultimately, making governance participation feel less like a financial transaction and more like using a social app is the key to mass adoption.

The fusion of Futarchy, AI, and financial derivatives gives birth to a new type of organization that is fluid, performance-driven, and capable of allocating capital and influence with unprecedented efficiency. This signifies a paradigm shift from governance that makes decisions through politics to governance that finds optimal solutions through information. In this model, an organization is no longer a rigid hierarchy but closer to an organic network where autonomous participants collaborate through a market (beliefs) around a common goal (values).

To make this vision a reality, stakeholders in the Web3 ecosystem should consider the following strategic approaches:

• For Protocol Architects: Do not try to design an overly complex system from the start. Begin with a single, powerful, and hard-to-manipulate KPI to concentrate liquidity in one market. Diversify the markets gradually after the system has stabilized.

• For DAO Leaders: Introduce Futarchy in phases. Start with non-binding advisory markets to build community trust and accumulate data on the market's predictive accuracy. Gradually transition to binding decisions as successful data accumulates.

• For Tool Developers: Focus on the UX and AI abstraction layer. The biggest opportunity to popularize this complex financial system lies in making it easy for anyone to use. The best opportunities exist in bridging the gap between technical complexity and user-friendliness.

The model presented in this report is still in its early stages and faces many challenges. However, it heralds the inevitable convergence of three major trends: governance, finance, and artificial intelligence. This experiment, starting in Web3, has the potential to influence all forms of collective decision-making, from corporate boardrooms to public policy, far beyond just solving the problems of DAOs.

Ultimately, this model paints a future that uses 'the market as a philosopher' to guide a community's decision-making. An organization that respects democratic principles in setting its values, but adopts the most efficient and meritocratic methods to realize those values. The convergence of Futarchy, AI, and financial engineering will be a crucial first step toward this future-oriented governance innovation.