An experiment on learning in a multiple games environment

We study experimentally how players learn to make decisions if they face many different (normal–form) games. Games are generated randomly from a uniform distribution in each of 100 rounds. We find that agents do extrapolate between games but learn to play strategically equivalent games in the same way. If either there are few games or if access to information about the opponents’ behavior is easy (or both) convergence to the unique Nash equilibrium generally occurs. Otherwise this is not the case and play converges to a distribution of actions which is Non–Nash and which can be rationalized by theoretical models of categorization. Estimating different learning models we find that Nash choices are best explained by finer categorizations than Non–Nash choices. Furthermore participants scoring better in the ”Cognitive Reflection Test” choose Nash actions more often than other participants. JEL-classification: C70, C91