  Dynamical models of equilibrium selection differ along several dimensions. Does the model depict learning by individual players or aggregate trends in the population as a whole? How much rationality does the model attribute to players? Is the model deterministic or stochastic? Do players have limited or unlimited memory of past events? How much common knowledge do players have about the game’s payoff structure? Can players learn about the results of interactions in which they do not participate? Do the same players participate in each round of play, or are the players repeatedly drawn anew from a larger population? Is that larger population modeled as finite or infinite? An overview of the burgeoning and forbiddingly technical literature on these questions falls beyond the scope of this article. We confine attention here to three developments: replicator dynamics; fictitious play; and sophisticated Bayesian learning. Interested readers should consult the detailed surveys offered by Drew Fudenberg and David Levine (1998) and H. Peyton Young (2004).