Learning dynamics explains human behavior in Prisoner's Dilemma on networks

Abstract

Cooperative behavior lies at the very basis of human societies, yet its evolutionary origin remains a key unsolved puzzle. Whereas reciprocity or conditional cooperation is one of the most prominent mechanisms proposed to explain the emergence of cooperation in social dilemmas, recent experimental findings on networked Prisoner's Dilemma games suggest that conditional cooperation also depends on the previous action of the player---namely on the `mood' in which the player currently is. Roughly, a majority of people behaves as conditional cooperators if they cooperated in the past, while they ignore the context and free-ride with high probability if they did not. However, the ultimate origin of this behavior represents a conundrum itself. Here we aim specifically at providing an evolutionary explanation of moody conditional cooperation. To this end, we perform an extensive analysis of different evolutionary dynamics for players' behavioral traits---ranging from standard processes used in game theory based on payoff comparison to others that include non-economic or social factors. Our results show that only a dynamic built upon reinforcement learning is able to give rise to evolutionarily stable moody conditional cooperation, and at the end to reproduce the human behaviors observed in the experiments.

References in corpus (4)

• Evolutionary games on graphs
• Dynamical Organization of Cooperation in Complex Topologies
• Paths to Synchronization on Complex Networks
• Stochasticity and evolutionary stability

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• Reinforcement learning account of network reciprocity
• Symmetry breaking in the prisoner's dilemma on two-layer dynamic multiplex networks
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• Cooperation transitions in social games induced by aspiration-driven players
• The emergence of segregation: from observable markers to group specific norms