The influence of experienced guider on cooperative behavior in the Prisoner’s dilemma game

Tao You, Hailun Zhang, Ying Zhang, Qing Li, Peng Zhang and Mei Yang

Applied Mathematics and Computation, 2022, vol. 426, issue C

Abstract: In game theory, it is an essential topic to study the emergence and maintenance of cooperative behavior in groups based on the theories of evolutionary game and complex network. Unfortunately, an in-depth analysis of cooperative behavior on maintenance and development is usually challenged by the diversity of groups in society, which is mainly caused by the single mechanism in traditional networks. More recent studies have shown that multi-layer coupled network based evolutionary game theory is promising in exploiting the transmission of cooperative behavior between individuals in the game. Meanwhile, inspired by the decisive ability of reinforcement learning in overcoming the limitation of replica, in this work, we propose to combine the game strategy of reinforcement learning with the traditional prisoner’s dilemma strategy based on multiple coupled networks. The most advantage of this model is the improved capability of intelligent decision making for group behaviors. With the simulation of game evolution, the influence of individual strategy change, as well as individual ability on cooperative behavior in reinforcement learning, is also explored. Substantial validations have verified that in social dilemmas, the cooperative behavior can be maintained by adjusting the group’s ability with effective guidance.

Keywords: Cooperation; Reinforcement learning; Multi-layer network; Prisoner’S dilemma game (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:426:y:2022:i:c:s0096300322001771

DOI: 10.1016/j.amc.2022.127093

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