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Statistical prediction of the outcome of a noncooperative game

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Author Info

  • James W. Bono
  • David H. Wolpert

Abstract

Conventionally, game theory predicts that the mixed strategy profile of players in a noncooperative game will satisfy some equilibrium concept. Relative probabil- ities of the strategy profiles satisfying the concept are unspecified, and all strategies not satisfying it are implicitly assigned probability zero. As an alternative, we re- cast the prediction problem of game theory as statistically estimating the strategy profile, 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 strategy profiles 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 regula- tors can use PGT to make optimal decisions in situations where conventional game theory cannot provide advice.

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Bibliographic Info

Paper provided by American University, Department of Economics in its series Working Papers with number 2009-20.

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Date of creation: Oct 2009
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Handle: RePEc:amu:wpaper:2009-20

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Web page: http://www.american.edu/cas/economics/

Related research

Keywords: Quantal Response Equilibrium; Bayesian Statistics; Entropic prior; Maximum entropy;

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Cited by:
  1. James Costain & Anton Nakov, 2013. "Logit price dynamics," Banco de Espa�a Working Papers 1301, Banco de Espa�a.

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