In Young (1993, 1998) agents are recurrently matched to play a finite game and almost always play a myopic best reply to a frequency distribution based on a sample from the recent history of play. He proves that in a generic class of finite n-player games, as the mutation rate tends to zero,...

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...

In Young (1993, 1998) agents are recurrently matched to play a finite game and almost always play a myopic best reply to a frequency distribution based on a sample from the recent history of play. He proves that in a generic class of finite n-player games, as the mutation rate tends to zero,...

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...

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...