Context-based Imitation and the Evolution of Behavioral Rules

Abstract

We study the evolution of behavioral rules in environments with multiple contexts. Agents copy rules used by better-performing peers in the same context and apply them across contexts. Multiple contexts turn discrete-time imitation dynamics into a context-weighted social choice problem: the population converges to consensus if and only if some rule is a Condorcet winner; otherwise, persistent non-convergence can occur. Among same-context imitation protocols, imitate-if-better uniquely minimizes envy. The framework provides a new account of belief evolution, characterizing when imitation selects rational expectations and showing how persistent belief and consumption fluctuations can arise in stationary environments.

substantially revised, and the title is updated. 37 pages. Comments are very welcomed