Computing Equilibria of Prediction Markets via Persuasion

TL;DR

This paper explores how to compute equilibria in simple two-player prediction markets by linking them to Bayesian persuasion, providing efficient algorithms and new insights into their strategic behavior.

Contribution

It establishes a novel connection between prediction markets and Bayesian persuasion, and offers computationally efficient algorithms for equilibrium computation in specific cases.

Findings

Prediction market equilibria are equivalent to a simpler signaling game.

Efficient algorithms are developed for certain parameter regimes.

The approach reveals new conceptual insights into prediction market strategies.

Abstract

We study the computation of equilibria in prediction markets in perhaps the most fundamental special case with two players and three trading opportunities. To do so, we show equivalence of prediction market equilibria with those of a simpler signaling game with commitment introduced by Kong and Schoenebeck (2018). We then extend their results by giving computationally efficient algorithms for additional parameter regimes. Our approach leverages a new connection between prediction markets and Bayesian persuasion, which also reveals interesting conceptual insights.