Strategy-Proof Prediction Markets

TL;DR

This paper introduces new strategy-proof prediction markets that ensure truthful reporting by agents, overcoming limitations of traditional scoring rule based markets, despite increased worst-case losses.

Contribution

It develops novel strategy-proof scoring rule markets that guarantee truthful behavior without relying on restrictive conditions, a significant advancement over existing models.

Findings

New strategy-proof SRMs ensure truthful reporting.

Achieving strategy-proofness increases worst-case loss, which is shown to be minimal.

Traditional conditions for truthful behavior are restrictive and often unmet.

Abstract

Prediction markets aggregate agents' beliefs regarding a future event, where each agent is paid based on the accuracy of its reported belief when compared to the realized outcome. Agents may strategically manipulate the market (e.g., delay reporting, make false reports) aiming for higher expected payments, and hence the accuracy of the market's aggregated information will be in question. In this study, we present a general belief model that captures how agents influence each other beliefs, and show that there are three necessary and sufficient conditions for agents to behave truthfully in scoring rule based markets (SRMs). Given that these conditions are restrictive and difficult to satisfy in real-life, we present novel strategy-proof SRMs where agents are truthful while dismissing all these conditions. Although achieving such a strong form of truthfulness increases the worst-case loss in the new markets, we show that this is the minimum loss required to dismiss these conditions.