Prediction Markets, Mechanism Design, and Cooperative Game Theory

TL;DR

This paper models prediction markets within a mechanism design framework, analyzing incentive-compatible mechanisms based on proper scoring rules and exploring their connections to cooperative game theory.

Contribution

It introduces a model for prediction markets aligned with mechanism design, analyzes incentive properties, and links mechanisms to cooperative game theory insights.

Findings

Mechanisms based on proper scoring rules are incentive compatible.

Connections between prediction market mechanisms and cooperative game theory are established.

Discussion on practical implementation of these mechanisms in real prediction markets.

Abstract

Prediction markets are designed to elicit information from multiple agents in order to predict (obtain probabilities for) future events. A good prediction market incentivizes agents to reveal their information truthfully; such incentive compatibility considerations are commonly studied in mechanism design. While this relation between prediction markets and mechanism design is well understood at a high level, the models used in prediction markets tend to be somewhat different from those used in mechanism design. This paper considers a model for prediction markets that fits more straightforwardly into the mechanism design framework. We consider a number of mechanisms within this model, all based on proper scoring rules. We discuss basic properties of these mechanisms, such as incentive compatibility. We also draw connections between some of these mechanisms and cooperative game theory. Finally, we speculate how one might build a practical prediction market based on some of these ideas.