An Experiment to Evaluate Bayesian Learning of Nash Equilibrium Play

Some recent theoretical approaches to the question of how players might converge over time to a Nash equilibrium have assumed that the players update their beliefs about other players via Bayes' Rule. Jordan has shown in a Bayesian model of this kind that play will (theoretically) always converge to a complete-information Nash equilibrium, even though individual players will not generally attain complete information. We report on an experiment designed to evaluate the empirical implications of Jordan's model. A finite version of the model is constructed which generates unique predictions of subjects' choices in nearly all periods. The experimental data reveals that the theory does reasonably well at predicting the equilbria that subjects eventually play, even when there are multiple equilibria. The results thus suggest that Jordan's Bayesian model can provide an empirically effective solution to the equilibrium selection problem when the players have beliefs with finite support. However, the model's predictions about the path of play over time are not consisitent with the experimental data