Strictly Proper Scoring Mechanisms Without Expected Arbitrage

TL;DR

This paper introduces two innovative scoring mechanisms that ensure truthful expert forecasts even under collusion, using randomization to prevent dishonest coordination and eliminate arbitrage opportunities.

Contribution

The paper proposes novel scoring mechanisms that are strictly proper and robust against collusion, addressing a longstanding open problem in forecast elicitation.

Findings

Mechanisms are strictly proper and prevent expected arbitrage.

They remain truthful even with colluding experts.

The approach introduces a randomization component to ensure robustness.

Abstract

When eliciting forecasts from a group of experts, it is important to reward predictions so that market participants are incentivized to tell the truth. Existing mechanisms partially accomplish this but remain susceptible to groups of experts colluding to increase their expected reward, meaning that no aggregation of predictions can be fully trusted to represent the true beliefs of forecasters. This paper presents two novel scoring mechanisms which elicit truthful forecasts from any group of experts, even if they can collude or access each other's predictions. The key insight of this approach is a randomization component which maintains strict properness but prevents experts from coordinating dishonest reports in advance. These mechanisms are strictly proper and do not admit expected arbitrage, resolving an open question in the field.