A Conditional Funding Market (CFM) is a mechanism to help organizations allocate funding among multiple proposals by leveraging prediction markets. For simplicity, this text uses the term “DAO” to refer to the deploying entity, but note that the deployer can be any organization (the “Funding Entity”) willing to distribute funds toward an objective. Instead of relying solely on traditional voting or grant committees, CFMs let traders buy and sell tokens whose prices reflect the expected performance of each proposal on some measurable metric chosen by the DAO. Over time, market participants with accurate predictions gain profits (and hence influence), while those with inaccurate predictions lose capital. This “skin in the game” design addresses concerns like voter apathy and favoritism in grant councils. As Hayek’s 1945 paper "The Use of Knowledge in Society" explained and later Robin Hanson formalized for Futarchy, markets aggregate diverse private information more effectively than committees or polls.
What Problem Do CFMs Solve?
DAOs (or any Funding Entity) often struggle with deciding which proposals deserve funding, how much capital each should receive, and how to ensure that incentives are aligned. In typical voting systems, community members may lack time or incentives to become informed, or committee members may become biased. CFMs solve this by:
Incentivizing informed participation: Traders profit when they accurately forecast the impact of a proposal, so there is a direct economic reason to investigate and provide truthful information.
Aggregating diverse information: Anyone with unique insights into a proposal can trade in the market, driving the price (forecast) toward an accurate prediction.
Scaling and evolving over time: As better traders profit and reinvest, the quality of the forecasts improves further.
Why CFMs Work
CFMs excel at aggregating dispersed information by allowing many individuals, each holding private insights, to trade on their beliefs. Because a trader’s potential gains depend on how much their private assessment differs from the current market price, they have a clear financial motive to trade as soon as they possess underappreciated or new information. By acting early, they capture more profit from that gap—and in doing so, their trades adjust the price to reflect their private knowledge. This process ensures that markets combine the diverse knowledge of experts, insiders, and informed amateurs rather than relying on a small group of decision-makers. Crucially, traders risk capital on their forecasts, which curbs bias: if they are wrong, they lose money, unlike in surveys or elections where people can express preferences without financial consequences.
CFMs also offer continuous real-time updates. Rather than capturing a single moment in time (as studies, reports, or surveys often do), these markets adjust almost instantly to new data or developments, reacting far faster than traditional forecasting methods. While expert forecasts may take days or weeks to revise, CFMs respond dynamically whenever participants trade on fresh insights. This ongoing price discovery not only delivers timely forecasts but also ensures that the final market signals reflect the most up-to-date collective judgment on each proposal’s potential performance.
Hanson, Robin. Futarchy: Vote Values, But Bet Beliefs. (2013). Link
Gnosis Conditional Tokens Documentation (Archived). Link
Conditional Funding Markets Post on ggResearch. Link
Hayek, Friedrich. The Use of Knowledge in Society. Link
You may also find general background on futarchy in Ethereum Foundation’s Introduction to Futarchy.