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Categorization and Cooperation across Games

Department of Management, Ca’ Foscari University of Venice, 30121 Venice, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Games 2019, 10(1), 5;
Received: 1 December 2018 / Revised: 30 December 2018 / Accepted: 8 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue The Evolution of Cooperation in Game Theory and Social Simulation)
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. View Full-Text
Keywords: cognition; imitation; learning; evolutionary stability; prisoners’ dilemma; stag hunt cognition; imitation; learning; evolutionary stability; prisoners’ dilemma; stag hunt
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MDPI and ACS Style

LiCalzi, M.; Mühlenbernd, R. Categorization and Cooperation across Games. Games 2019, 10, 5.

AMA Style

LiCalzi M, Mühlenbernd R. Categorization and Cooperation across Games. Games. 2019; 10(1):5.

Chicago/Turabian Style

LiCalzi, Marco, and Roland Mühlenbernd. 2019. "Categorization and Cooperation across Games" Games 10, no. 1: 5.

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