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Fast convergence in evolutionary equilibrium selection (2011)
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BibTeX
@MISC{Kreindler11fastconvergence,
author = {Gabriel E. Kreindler and H. Peyton Young},
title = {Fast convergence in evolutionary equilibrium selection},
year = {2011}
}
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Abstract
Stochastic selection models provide sharp predictions about equilibrium selection when the noise level of the selection process is taken to zero. 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. Selection is sharp and convergence is fast for realistic noise levels and payoff values; moreover, the expected waiting times are comparable to those in local interaction models. 1. Stochastic stability and equilibrium selection Evolutionary models with random perturbations provide a useful framework for explaining how populations reach equilibrium from out-of-equilibrium conditions, and why some equilibria are more likely than others to be observed in the long run. Individuals in a large population interact with one another repeatedly to play a given game, and they update their strategies based on information about what others are doing. The updating rule is usually assumed to be a myopic best reply with a random component resulting from errors, unobserved utility shocks, or experiments. The long-run
Keyphrases
fast convergence evolutionary equilibrium selection large population local interaction model noise level important exception arises similar result local interaction unobserved utility shock waiting time stochastic selection model long time stochastic stability large population interact useful framework long run case convergence selection process out-of-equilibrium condition updating rule random perturbation payoff value stable equilibrium small close-knit group equilibrium selection evolutionary model small noise realistic noise level sharp prediction random component equilibrium selection
