Equilibrium selection in the stag hunt game under generalized reinforcement learning

Ratul Lahkar ()

Journal of Economic Behavior & Organization, 2017, vol. 138, issue C, 63-68

Abstract: We apply the generalized reinforcement (GR) learning protocol to the stag hunt game. GR learning combines positive and negative reinforcement. The GR learning rule generates the GR dynamic, which governs the evolution of the mixed strategy of agents in the population. We identify conditions under which the GR dynamic converges globally to one of the two pure strategy Nash equilibria of the game.

Keywords: Reinforcement learning; Generalized reinforcement dynamic; Stag hunt game (search for similar items in EconPapers)
JEL-codes: C72 C73 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:138:y:2017:i:c:p:63-68

DOI: 10.1016/j.jebo.2017.04.012

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