Statistical Prediction of the Outcome of a Noncooperative Game

          @misc{ scivideos_PIRSA:08110047,
            doi = {10.48660/08110047},
            url = {https://pirsa.org/08110047},
            author = {Wolpert, David},
            keywords = {Quantum Foundations},
            language = {en},
            title = {Statistical Prediction of the Outcome of a Noncooperative Game},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2008},
            month = {nov},
            note = {PIRSA:08110047 see, \url{https://scivideos.org/index.php/pirsa/08110047}}
          }
          

Abstract

Many statistics problems involve predicting the joint strategy that will be chosen by the players in a noncooperative game. Conventional game theory predicts that the joint strategy will satisfy an ``equilibrium concept\'\'. The relative probabilities of the joint strategies satisfying the equilibrium concept are not given, and all joint strategies that do not satisfy it are given probability zero. As an alternative, I view the prediction problem as one of statistical inference, where the ``data\'\' includes the details of the noncooperative game. This replaces conventional game theory\'s focus on how to specify a set of equilibrium joint strategies with a focus on how to specify a density function over joint strategies. I explore a Bayesian version of such a Predictive Game Theory (PGT) that provides a posterior density over joint strategies. It is based on the the entropic prior and on a likelihood that quantifies the rationalities of the players. The Quantal Response Equilibrium (QRE) is a popular game theory equilibrium concept parameterized by player rationalities. I show that for some games the local peaks of the posterior density over joint strategies approximate the associated QRE\'s, and derive the associated correction terms. I also discuss how to estimate parameters of the likelihood from observational data, and how to sample from the posterior. I end by showing how PGT can be used to specify a {it{unique}} equilibrium for any noncooperative game, thereby providing a solution to a long-standing problem of conventional game theory.