Learning to Play Approximate Nash Equilibria in Games with Many Players

Edward John Cartwright ()

No 2004.85, Working Papers from Fondazione Eni Enrico Mattei

Abstract: We illustrate one way in which a population of boundedly rational individuals can learn to play an approximate Nash equilibrium. Players are assumed to make strategy choices using a combination of imitation and innovation. We begin by looking at an imitation dynamic and provide conditions under which play evolves to an imitation equilibrium; convergence is conditional on the network of social interaction. We then illustrate, through example, how imitation and innovation can complement each other; in particular, we demonstrate how imitation can .help. a population to learn to play a Nash equilibrium where more rational methods do not. This leads to our main result in which we provide a general class of large game for which the imitation with innovation dynamic almost surely converges to an approximate Nash, imitation equilibrium.

Keywords: Imitation; Best replay; Convergence; Nash equilibrium (search for similar items in EconPapers)
JEL-codes: C70 C72 C73 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-com and nep-evo
Date: 2004-05
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Related works:
Working Paper: LEARNING TO PLAY APPROXIMATE NASH EQUILIBRIA IN GAMES WITH MANY PLAYERS (2003)
Working Paper: Learning to play approximate Nash equilibria in games with many players (2002)
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Persistent link: http://EconPapers.repec.org/RePEc:fem:femwpa:2004.85

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