In this paper we model an evolutionary process with perpetual random shocks, where individuals sample population-specific strategy and payoff realizations and imitate the most successful behavior. For finite n-player games we prove that in the limit, as the perturbations tend to zero, only...

In this paper I define an evolutionary stability criterion for learning rules. Using Monte Carlo simulations, I then apply this criterion to a class of learning rules that can be represented by Camerer and Ho's (1999) model of learning. This class contains perturbed versions of reinforcement and...

In this paper, I analyze stochastic adaptation in finite n-player games played by heterogeneous populations of myopic best repliers, better repliers and imitators. In each period, one individual from each of n populations, one for each player role, is drawn to play and chooses a pure strategy...