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Categorization and Cooperation across Games

Marco LiCalzi () and Roland Mühlenbernd ()
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Roland Mühlenbernd: Department of Management, Ca’ Foscari University of Venice, 30121 Venice, Italy

Games, 2019, vol. 10, issue 1, 1-21

Abstract: We study a model where agents face a continuum of two-player games and categorize them into a finite number of situations to make sense of their complex environment. Agents need not share the same categorization. Each agent can cooperate or defect, conditional on the perceived category. The games are fully ordered by the strength of the temptation to defect and break joint cooperation. In equilibrium agents share the same categorization, but achieve less cooperation than if they could perfectly discriminate games. All the equilibria are evolutionarily stable, but stochastic stability selects against cooperation. We model agents’ learning when they imitate successful players over similar games, but lack any information about the opponents’ categorizations. We show that imitation conditional on reaching an intermediate aspiration level leads to a shared categorization that achieves higher cooperation than under perfect discrimination.

Keywords: cognition; imitation; learning; evolutionary stability; prisoners’ dilemma; stag hunt (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
Date: 2019
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Working Paper: Categorization and cooperation across games (2018) Downloads
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