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Is Butter futarchy?

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Summary

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Butter is not binding futarchy by default, and conditional branch prices support advisory futarchy.

Butter is not a futarchy system in the narrow sense of “an institution that has pre-committed to letting markets make binding decisions.” Butter is a conditional markets exchange whose prices can support advisory futarchy now and binding futarchy when an institution chooses to pre-commit. Butter’s goal is to make that transition possible by building markets that produce decision-relevant prices before institutions are ready to delegate decisions to them.

Butter hosts conditional markets that traders already want to trade for hedging and speculation. When a conditional market’s outcomes correspond to decision options, the branch prices become forecasts of what the organization’s own token or share price will be under each decision. As those forecasts build a track record, decision-makers can adopt them first as an input, then as a soft commitment, and finally as a binding decision rule, without needing new market infrastructure or a formal integration with Butter.

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What futarchy is

Futarchy is a decision process that separates values from beliefs. For a for-profit organization, the “value” question is shareholder value, which decision-makers often approximate with a higher token or share price. The “beliefs” question is which decision will lead to a higher token or share price. Robin Hanson’s slogan is “vote on values, but bet on beliefs.”

The key mechanism is a decision market. It is a set of markets that trade the organization’s token or share price, one market per decision option, where each market settles based on the price after that option is taken.

This approach treats governance as a bet on the marginal trader, not an aggregation of the median voter, and it only requires a small number of traders with enough information and capital to act when prices are wrong.

In a binding futarchy, the organization commits in advance to pick the option with the higher market-implied price. For example, a token issuer can commit to “choose proposal A if the market implies a higher token price under A than under B.” In an advisory futarchy, the organization keeps formal authority but treats the market-implied prices as decision-relevant information.

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What Butter provides

Butter provides conditional markets. A conditional market trades one branch per mutually exclusive outcome of an event or decision, such as “ACME token price if proposal A passes” and “ACME token price if proposal B passes.” Each branch has a branch price, which is the market-implied future price of the asset in that outcome. See What are conditionals?, Why conditionals?, and the Glossary for the core definitions.

Conditionals offer clean event-specific exposure that improves hedging and risk sharing for traders and market makers.

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When Butter becomes futarchy

Butter becomes futarchy when people use decision-conditioned conditional markets as inputs to real decisions.

In this setting, the event is “which decision is taken,” and each outcome is one decision option. Traders trade the organization’s own token or share price in each outcome branch, producing a branch price for each option. When decision-makers and stakeholders treat those branch prices as forecasts worth consulting, the market is functioning as advisory futarchy.

An organization can use those prices without any formal relationship with Butter. It can define an internal rule such as, “choose the option with the higher implied token price,” or a weaker rule such as, “require an overwhelming argument to go against the market.”

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Why this avoids futarchy’s cold start

Futarchy has a cold-start problem because binding adoption requires a large up-front coordination step.

Institutions face high switching costs. They have to evaluate market infrastructure and operations, liquidity subsidies, regulatory compliance, tooling, and education, and they then wait a long time to see whether the mechanism improves outcomes. Futarchy also concentrates its value in a small number of high-stakes decisions, and those are the decisions where incumbents have the strongest lock-in and the lowest tolerance for unproven mechanisms.

Decision use cases follow a power-law distribution. There are many decisions, but only a small number matter enough to justify a binding commitment and the associated switching costs.

Futarchy is also a three-sided platform. Decision-makers (boards, governments, decentralized autonomous organization (DAO) councils) rely on traders to produce forecasts, and decision-takers (citizens, employees, tokenholders) live with the consequences. The platform fails when each side waits for the others.

  • Boards and councils do not delegate consequential decisions until prices are reliably accurate.

  • Traders do not participate unless they have an edge, decisions have a stake, and markets are liquid.

  • Stakeholders do not invest effort and resources or ascribe legitimacy to decision mechanisms unless they expect better outcomes.

Blockchains reduce market infrastructure costs, stablecoins reduce settlement friction, and latency improvements support large-scale always-on trading. These changes help explain the recent growth of prediction markets and the increase in projects that try to apply markets to governance. Switching costs have come down, but use cases are still hard to find, and the three-sided platform coordination problem remains.

Many futarchy projects pursue a wedge use case. They build a superior product for a specific decision class, use it to justify the initial switching costs, and then try to expand to broader governance over time. Examples include Butter’s conditional funding markets for capital allocation, MetaDAO’s futarchy-based launchpad for new projects, and percent’s futarchy-based markets for emissions and other protocol parameters.

Butter’s approach targets a different first step. Butter builds conditional markets that traders already want to trade, and then lets futarchy emerge as a use of those prices by organizations that choose to consult them.

This approach relies on supply-side network effects on the trader side. Each additional trade improves liquidity and price discovery for everyone else. Traders also care about conditional exposure, not only about which outcome happens. Conditionals let a participant hold explicit positions like “ACME token if proposal A passes” and “ACME token if proposal B passes,” instead of combining multiple instruments to approximate the same payoff with extra basis risk and operational overhead.

This adoption path is the core claim in Why conditionals?. Conditionals reduce risk around decision-driven trading, distribute exposure to those willing to hold it, and legitimize decision markets in public discourse by creating a track record of conditional prices.

Butter focuses on markets that traders already want, leans on trader-side network effects, and aims for accurate prices on high-value decisions.

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Why conditionals support advisory adoption

  • Conditionals have a natural hedger base because real event risk exists in portfolios today, so liquidity can come from hedging demand before any governance reform.

  • Many high-cadence events resolve to public, time-stamped facts, so resolution rules can stay exogenous and low-controversy while building a long series of settled markets.

  • Outcome-conditioned payoffs isolate the price impact within each branch, so a board can compare options without mixing outcome probabilities into a single spot price.

  • Treasury, risk, and governance workflows can use branch prices as decision inputs without any binding pre-commitment.

  • Framing decision markets as risk sharing, not betting, makes them easier to adopt in public-facing settings.

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Adoption stages

This is the sequence from conditional prices to futarchy.

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Advisory

Decision-makers and stakeholders watch branch prices as one input among many. The market’s role is informational, and formal authority stays where it is today.

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Soft commitments

Organizations adopt norms or policies that treat market prices as a default, in addition to potentially subsidising liquidity in said markets so as to increase accuracy. A concrete example is a rule that says there has to be an overwhelming argument to go against the market.

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Binding rules

Organizations pre-commit to follow conditional prices for specific classes of decisions, by writing internal governance rules that point at those prices. Binding futarchy becomes an emergent property of a network of conditional markets, rather than a bespoke governance product that every institution has to implement from scratch.

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The analogy to index funds

John C. Bogle introduced the index fund in 1976, and it was initially mocked as “Bogle’s Folly.” Bogle initially raised $11 million of a $150 million target. By 2025, index mutual funds and ETFs together hold about $18.6 trillion, which is about 52% of all U.S. long‑term mutual fund and ETF assets.

Delegating decisions to a market mechanism starts as a novelty, then becomes a default once the mechanism has built a track record and the surrounding infrastructure has matured. Butter’s strategy follows the same sequence.

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What Butter does not do by default

Butter does not set an organization’s objectives. It does not run the organization’s governance process. It does not force a binding decision rule on any institution. It provides tradable conditional markets and quotable branch prices that institutions and stakeholders can choose to use for decision-making.

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What about non-price measures?

Butter currently focuses on markets that settle to an asset price, rather than markets that settle to non-price measures.

  • Traders are more interested in trading asset prices than trading organization-specific measures, and they already know how to reason about pricing assets.

  • Asset prices can be observed immediately after the decision resolves. The efficient market hypothesis (EMH) says asset prices will almost immediately incorporate the expected impact of a decision after it occurs, while non-price measures require waiting for the impact to be observed, requiring collateral to be locked for extended periods.

  • Market makers can quote and hedge price-based markets more easily than markets on bespoke measures with potentially un-defined behaviour.

  • Asset prices are less subject to direct manipulation than most non-price measures.

  • Selecting the correct non-asset-price metric requires deep familiarity with an organisation and its various sub-goals, hence requiring institutional buy-in, whereas share price maximisation is by definition aligned with the objectives of all for-profit enterprises.

  • Non-price measures fit smaller and less impactful decisions, while the highest return on investment (ROI) improvements in decision quality come from large decisions that move token or share prices.

  • Traders are not interested in trading many small decisions, especially when evaluating them requires granular measures that are hard to standardize.

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References

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Wedge use cases referenced above

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