We consider a simple model of stochastic evolution in population games. In our model, each agent occasionally receives opportunities to update his choice of strategy. When such an opportunity arises, the agent selects a strategy that is currently optimal, but only after his payoffs have been randomly perturbed. We prove that the resulting evolutionary process converges to approximate Nash equilibrium in both the medium run and the long run in three general classes of population games: stable games, potential games, and supermodular games. We conclude by contrasting the evolutionary process studied here with stochastic fictitious play.
Similarly, REF consider evolutionary better-reply learning in population games with noisy payoffs and derive a process that converges to approximate Nash equilibrium in stable games, potential games, and supermodular games.
Evolution in games with randomly disturbed payoffs
J. Econ. Theory
J. Econ. Theory
Economics Computer Science