Evidence Markets

2026-06-05

AI Summary

This work proposes an “evidence market” that unifies belief forecasting and verifiable evidence submission within a single mechanism, addressing the limitations of traditional prediction markets—which merely aggregate crowd beliefs without supporting evidence and rely on external ground truth for settlement. By dynamically adjusting the liquidity parameter of the logarithmic market scoring rule, the mechanism incentivizes participants to jointly provide probabilistic forecasts and corroborating evidence, enabling endogenous settlement based on crowdsourced evidence even in the absence of external facts. The design incorporates an LLM-as-a-Judge verification framework and an asynchronous execution algorithm to enhance practicality. Theoretically, truthful reporting constitutes an ε-dominant strategy incentive-compatible (ε-DSIC) equilibrium, platform loss is bounded, and evidence rewards scale positively with market uncertainty. Feasibility is demonstrated through a case study on large language model capability evaluation.

Abstract

Modern prediction markets face two limitations that restrict their applicability in a range of settings:~(i)~they reveal what the crowd believes but not the evidence or reasoning behind those beliefs, and~(ii)~they require an 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 incentivizes the submission of evidence alongside beliefs and can be endogenously resolved using the crowd-sourced evidence if external resolution is not possible. At its core, the market uses a logarithmic market scoring rule whose liquidity parameter changes dynamically with the accumulated evidence quality. We prove that platform loss is bounded, evidence is rewarded proportional to the current market uncertainty, and can be equivalently implemented through an automated market maker. In the case where the marker resolves endogenously based on submitted evidence, we characterize how withholding evidence shifts a trader's belief about resolution and use it to prove truthful belief and evidence reporting is a always an $\varepsilon$-dominant strategy incentive compatible (DSIC) strategy. To address operational considerations, we propose evidence verification via an LLM-as-a-Judge framework with staking and give an asynchronous execution algorithm that is not bottle-necked by verification. Throughout the work, we use LLM evaluations -- determining which model is best for a given task -- as a salient and representative running example for our proposed market.

Problem

• prediction markets
• evidence elicitation
• endogenous resolution
• information aggregation
• crowdsourced evidence

Innovation

• Evidence Markets
• Prediction Markets
• Incentive Compatibility
• LLM-as-a-Judge
• Automated Market Making