# Hanson’s Folly: How to (Actually) Achieve Futarchy

Source: https://x.com/noturhandle_/status/1993459861598732671?s=46&t=C4Nxr8QOmw__i8jotoVfJA
Kiwi News: https://news.kiwistand.com/stories/Hansons-Folly-How-to-Actually-Achieve-Futarchy?index=0x692dc46e6a8c78bf40681597916315a68100d948779b08bfd0c217da036f6f6879910607
Submitted by cerv1.eth on 2025-12-01 UTC, 1 upvote, 0 comments

## Summary

Butter argues that futarchy, a governance system where voters choose goals and speculative markets choose policies, is most likely to spread by first building a network of conditional markets that traders use for hedging, rather than by selling futarchy directly to institutions. The essay says futarchy has failed to gain mainstream adoption because decision-makers, decision-takers and traders each demand the others move first. Its proposed solution is a Conditionals exchange where traders price assets across event outcomes, such as BTC conditional on a Fed cut, hold or hike. Organizations could later opt in by pointing their decision rules at those prices. The essay is presented as part one of a two-part series.

## Article

We have a single goal at Butter: to achieve futarchy. This is part one of a two-part essay detailing how we plan to do it.

bogle

The Index Fund was introduced in 1976 by John C. Bogle, allowing investors, for the first time, to outsource portfolio allocation to the greatest stock picker of all time: the collective wisdom of every trader in-market.

Bogle initially raised just $11M of a $150M target, spawning the phrase, “Bogle’s Folly.” Yet by 2025, index mutual funds and ETFs together hold about $18.6 trillion, roughly 52% of all U.S. long‑term mutual fund and ETF assets.

Letting markets make investment decisions was a joke in 1976. Today, it looks obvious.

hanson

In his 2000 paper on futarchy¹, Robin Hanson proposed that we should vote on values, but bet on beliefs: use ordinary political or corporate processes to choose what we care about, and use speculative markets to decide which policies are most likely to achieve those goals. Unlike voters or politicians, markets have a decent track record of predicting the future and, thankfully, can be designed to stick to pre-agreed objectives.

Ballot boxes and shareholder votes are restricted to selecting goals and metrics. Traders then bet on which decision best achieves these interests; the more accurate the bet, the greater the reward.

Where traditional governance relies on the median voter, futarchy is a bet on the marginal trader. All it really needs is a few traders with enough information to have an opinion and enough capital to act when prices are wrong.

There have been numerous attempts (many spearheaded by Hanson himself), but the project has so far failed to achieve mainstream adoption. Few institutions are willing to pre-commit to binding decisions by an unproven mechanism, and so futarchy’s cold-start problem has proven particularly chilling:

Switching costs are high (market infrastructure and ops, liquidity subsidies, regulatory compliance, tooling, and education), and the initiator must wait a long time to receive a payoff, which they'll often have to share.

Decision use cases follow a power-law distribution. There are many decisions, but only a few truly matter (interest rates, budgets, wars, elections, big product bets). Futarchy requires high-stakes decisions to justify the costs, but these are exactly where incumbents have the strongest network lock-in.

Futarchy is a three-sided platform: Decision-makers (boards, governments, DAO councils) engage traders to produce forecasts that decision-takers (citizens, employees, tokenholders) must eventually live with. Onboarding all three sides at once is a complex coordination game.

Game Rules:
Decision-makers: “won’t delegate consequential decisions until prices are reliably accurate.”
Traders: “won’t bother unless they have an edge, decisions have a stake, and markets are liquid.”
Decision-takers: “won’t engage unless they’re confident the effort will actually produce better outcomes”

Most futarchy pilots never get off the ground because they demand a simultaneous leap of faith from decision-makers, takers, and traders, with little incentive for any side to go first.

But each problem also exposes an opportunity.

use case wedge

With blockchains, anyone with an internet connection and a high pain threshold can run 24/7 global markets on public infrastructure. Stablecoin adoption and improvements in latency over the last few years help explain, in part, the recent success of prediction markets and the growing cohort of futarchy projects.

Switching costs (1) have come down, but (2) use cases are still hard to find, and (3) the three-sided platform problem looms large.

The futarchy plays we’ve seen so far, including our own, are primarily bets on solving (2): the use‑case problem. They each leverage futarchy to provide a superior product for an underserved decision use case as a wedge. A few examples:

Butter’s Capital Allocation for DAOs (see here), where capital allocation decisions are bound to conditional markets that predict which proposals will grow the protocol’s KPIs.

MetaDAO’s Board of Directors for ICO investors (see here), where founders commit to follow conditional markets on capital allocation or roadmap decisions in exchange for investment.

percent’s Token Emissions for Token Issuers (see here), where token emissions are tied to conditional markets on future token value.

Note that in each, the benefit for early adopters is immediate and direct. The game here is to establish a beachhead as high up on the power law curve as possible, and hope network effects parlay you into the most valuable decisions in society.

But each organization that selects futarchy must still overcome its own cold start. They must each convince other decision-takers to trust a new mechanism and attract enough informed traders to participate in their markets. Each new futarchy chips away at the cold-start problem for the next, but brings with it a new coordination game.

But there’s a faster path: instead of solving many small three-sided platform problems inside individual orgs, we can instead solve a single large platform problem for one side.

supply-side network effects

Between decision-makers, decision-takers, and traders, traders have the strongest single-sided network effect. Each trade provides liquidity and contributes to price discovery.

Pockets of decision-makers and decision-takers receive most of futarchy's value at the edge of the network, when the handful of decisions they care about are being priced. Traders get value from the whole network at once.

Macro events, major political decisions, economic data releases, corporate earnings, and geopolitical engagements produce uncertainty in markets that only the most financially astute can offload or warehouse. Trading volumes can double or triple during these events as liquidity rotates into derivatives hunting for conditional exposure.

Conditional markets are the ideal venue to absorb this demand; they give traders a simple way to share event risk.

Traders don’t just care what happens; they care about their exposure if it happens. Conditional markets let them hold long positions in “BTC if the Fed cuts” and short positions in “BTC if the Fed holds,” rather than taking a single bet on the event itself.

From day one, traders can produce conditional prices on any and every event and decision, including those at the very top of our use-case curve. No complex sales process, no waiting for permission; we exchange a messy coordination problem for a clean liquidity constraint.

really?

Let's imagine a $200m crypto hedge fund with a large BTC/ETH position. A few times a month, they face real event risk: FOMC meetings, economic releases, ETF decisions, major protocol votes. They already care about these events and already trade them, but the tooling is sub-optimal.

Today, they hack “if FED hold then short BTC” exposure by combining perps, options, and sometimes prediction markets. They end up with blunt, indirect exposure in each scenario and extra basis risk between the post-event prices they care about and what they actually hold.

Conditionals give them the same exposure in a single primitive. Instead of “BTC perps + BTC options + Fed yes/no,” they trade BTC if the Fed cuts and BTC if the Fed holds as two explicit branches. A conditional is just asset exposure in each outcome of an event. You get to shape the payoff (like options) based on clean outcomes (like prediction markets) without having to build and maintain custom infrastructure.

That makes the fund strictly better off on simplicity alone. Hedges that currently require multi‑leg options structures and a separate event book collapse into a small set of conditional positions. That means less basis risk, fewer moving parts to monitor, and lower overhead for the trader.

As liquidity deepens and conditional prices earn trust among business leaders, politicians, and the media, the path to futarchy looks less like a leap and more like a ladder:

Advisory: decision-makers and stakeholders watch conditional prices as one input among many.

Soft commitments: orgs adopt internal norms, i.e., “we need an overwhelming argument to go against the market”.

Binding rules: orgs can, with no formal relationship with Butter, pre-commit to follow conditional prices for specific classes of decisions

That last step is the real unlock. Futarchy stops being a governance product that every institution has to design and implement on its own, and becomes an emergent property of a network of conditional markets. Traders across orgs share broadly similar needs, so we only have to solve product‑market fit once on the trader side. Any org can then opt in by pointing its internal decision rules at those prices, and futarchy “switches on” for them without new infra or coordination games.

conditionals

The fastest path to achieving futarchy², then, may not be to build and sell futarchy but to create the optimal conditions for futarchy to emerge. These include:

Conditional markets that provide immediate and direct benefits to traders through hedging and speculation

Strong supply-side network effects, i.e., every trader increases the utility for other traders

Accurate conditional prices for as many high-value decisions as possible

We're building a Conditionals exchange to do just this. Conditionals let traders price assets in every branch of the future “as if it happened.” BTC conditional on Fed cut/hold/hike, S&P 500 conditional on candidate A wins/candidate B wins/candidate C wins.

If you regularly trade macro events, crypto, or both, and you’re interested in joining our private beta, please sign up for the waitlist here: https://waitlist.butter.markets.

And if you're excited to build this with us, please reach out. DMs are open.

¹ Hanson reminded me that his first published work on the topic was in 1996.
² I’ve mostly set aside the "vote value" side hard problem of choosing good value metrics until we’re capable of producing reliable conditional prices.