期刊
GAMES AND ECONOMIC BEHAVIOR
卷 75, 期 2, 页码 882-897出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.geb.2012.02.017
关键词
Stochastic stability; Completely uncoupled learning; Equilibrium selection; Distributed control
类别
An individual's learning rule is completely uncoupled if it does not depend directly on the actions or payoffs of anyone else. We propose a variant of log linear learning that is completely uncoupled and that selects an efficient (welfare-maximizing) pure Nash equilibrium in all generic n-person games that possess at least one pure Nash equilibrium. In games that do not have such an equilibrium, there is a simple formula that expresses the long-run probability of the various disequilibrium states in terms of two factors: (i) the sum of payoffs over all agents, and (ii) the maximum payoff gain that results from a unilateral deviation by some agent. This welfare/stability trade-off criterion provides a novel framework for analyzing the selection of disequilibrium as well as equilibrium states in n-person games. (C) 2012 Elsevier Inc. All rights reserved.
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