References (93)


Citations (1)



Statistical Prediction of the Outcome of a Noncooperative Game

David Wolpert

Santa Fe Institute

James Bono

Economists Incorporated

September 25, 2008

Conventionally, game theory predicts that the joint mixed strategy of players in a noncooperative game will satisfy some equilibrium concept. Relative probabilities of the joint strategies satisfying the concept are unspecified, and all strategies not satisfying it are assigned probability zero. As an alternative, we recast the prediction problem of game theory as statistically estimating the joint strategy, from "data" that consists of the game specification. This replaces the focus of game theory, on specifying a set of "equilibrium" mixed strategies, with a new focus, on specifying a probability density over all mixed strategies. We explore a Bayesian version of such a Predictive Game Theory (PGT). We show that for some games the peaks of the posterior over joint strategies approximate quantal response equilibria. We also show how PGT provides a best single prediction for any noncooperative game, i.e., a universal refinement. We also show how regulators can use PGT to make optimal decisions in situations where conventional game theory cannot provide advice.

Number of Pages in PDF File: 66

Keywords: Multi-agent systems, Noncooperative Games, Quantal Response Equilibrium, Bayesian Statistics, Statistical Physics

JEL Classification: C02, C11, C70, C72

Open PDF in Browser Download This Paper

Date posted: July 28, 2008 ; Last revised: September 1, 2009

Suggested Citation

Wolpert, David and Bono, James, Statistical Prediction of the Outcome of a Noncooperative Game (September 25, 2008). Available at SSRN: https://ssrn.com/abstract=1184325 or http://dx.doi.org/10.2139/ssrn.1184325

Contact Information

David Wolpert (Contact Author)
Santa Fe Institute ( email )
1399 Hyde Park Road
Santa Fe, NM 897501
United States
James Bono
Economists Incorporated ( email )
100 Spear St.
Suite 1000
San Francisco, CA 94105
United States
415.975.3229 (Phone)
HOME PAGE: http://www.ei.com
Feedback to SSRN

Paper statistics
Abstract Views: 692
Downloads: 113
Download Rank: 194,641
References:  93
Citations:  1