Evidence Markets

Current prediction markets face two limitations that restrict their broader applicability: (1) they reveal what the crowd believes but not the evidence or reasoning behind those beliefs, and (2) they require an exogenous event with an external ground truth that resolves at a known future date. We address these twin challenges by introducing evidence markets, a generalization of prediction markets that (1) incentivizes the submission of evidence alongside beliefs and (2) can resolve endogenously from that evidence when exogenous resolution is unavailable. At its core is a logarithmic market scoring rule whose liquidity parameter decreases as accumulated evidence quality increases. We prove that platform loss here is bounded, that evidence is rewarded in proportion to current market uncertainty, and that the mechanism admits an equivalent automated market maker. Importantly, truthful belief and full evidence reporting is a dominant strategy equilibrium under exogenous resolution and an $\epsilon$-subgame perfect Nash Equilibrium under endogenous resolution, where we also bound how withholding evidence shifts a trader's belief about resolution. Throughout the work, LLM evaluations–determining which model is best for a given task–is used as a running example for such a market.