Cushman, Fiery

Littman, Michael

MacGlashan, James

eScholarship - University of California

UC MercedProceedings of the Annual Meeting of the Cognitive Science SocietyTitleFlexible theft and resolute punishment: Evolutionary dynamics of social behavior among reinforcement-learning agentsPermalinkhttps://escholarship.org/uc/item/4z12c2fxJournalProceedings of the Annual Meeting of the Cognitive Science Society, 36(36)ISSN1069-7977AuthorsMacGlashan, JamesLittman, MichaelCushman, FieryPublication Date2014 Peer reviewedeScholarship.org Powered by the California Digital LibraryUniversity of CaliforniaFlexible theft and resolute punishment: Evolutionary dynamics of social behavioramong reinforcement-learning agentsJames MacGlashanBrown University, Providence, Rhode Island, USAMichael LittmanBrown University, Providence, Rhode Island, USAFiery CushmanBrown University, Providence, Rhode Island, USAAbstract: Existing models of the evolution of social behavior typically involve innate strategies such as tit-for-tat. Yet, bothbehavioral and neural evidence indicates a substantial role for learned social behavior. We explore the evolutionary dynamicsof two simple social behaviors among learning agents: Theft and punishment. In our simulation, agents employ Q-learning,a common reinforcement learning algorithm. Agents reproduce in proportion to the objective rewards they accrue, but thesubjective reward function that guides learning and action evolves by natural selection. We find that agents typically evolvea bias to punish thieves that is sufficiently strong that it cannot be unlearned. Agents also typically evolve a bias to abstainfrom theft, but this is weak enough to permit rapid learning. This flexibility allows would-be thieves to exploit non-punishers.Finally, we show qualitatively similar results in a behavioral experiment on human participants: Flexible theft, but resolutepunishment.3344

Flexible theft and resolute punishment: Evolutionary dynamics of social behavior among reinforcement-learning agents

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