Imitators and Optimizers in a Changing Environment

Abstract

We analyze the dynamic interaction between imitation and myopic optimization in an environment of changing marginal payoffs. Focusing on finite irreducible environments, we unfold a trade-off between the degree of interaction and the size of environmental shocks. The optimizer outperforms the imitator if interaction is weak or if shocks are large. We use the example of Cournot duopoly to give economic meaning to this condition. To establish our main result, we rely on continuous state space Markov theory. In particular, it turns out that introducing a stochastic environment with finitely many states suffices to make an otherwise deterministic process ergodic.imitation; optimization; evolution; heterogeneous learning rules; changing environments