Fast convergence in evolutionary equilibrium selection

• 成果类型: Article
• 署名作者: Kreindler, Gabriel E.; Young, H. Peyton
• 署名单位: University of Oxford; University of Oxford
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2013.02.004
• 发表日期: 2013
• 页码: 39-67
• 关键词: stochastic stability; Logit learning; Markov chain; Convergence time

摘要

Stochastic best response models provide sharp predictions about equilibrium selection when the noise level is arbitrarily small. The difficulty is that, when the noise is extremely small, it can take an extremely long time for a large population to reach the stochastically stable equilibrium. An important exception arises when players interact locally in small close-knit groups; in this case convergence can be rapid for small noise and an arbitrarily large population. We show that a similar result holds when the population is fully mixed and there is no local interaction. Moreover, the expected waiting times are comparable to those in local interaction models. (C) 2013 Elsevier Inc. All rights reserved.