Context-based Imitation and the Evolution of Behavioral Rules

Enrique Urbano Arellano and Xinyang Wang

Papers from arXiv.org

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.

Date: 2023-10, Revised 2026-05
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