Learning Efficient Nash Equilibria in Distributed Systems

H. Young and Bary S. R. Pradelski

No 480, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: An individual's learning rule is completely uncoupled if it does not depend 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 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.

JEL-codes: C72 C73 (search for similar items in EconPapers)
Date: 2010-02-01
New Economics Papers: this item is included in nep-evo
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Learning efficient Nash equilibria in distributed systems (2012)
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