Emergence of cooperation under punishment: A reinforcement learning perspective
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866912160924303360 |
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| author | Zhao, Chenyang Zheng, Guozhong Zhang, Chun Zhang, Jiqiang Chen, Li |
| author_facet | Zhao, Chenyang Zheng, Guozhong Zhang, Chun Zhang, Jiqiang Chen, Li |
| contents | Punishment is a common tactic to sustain cooperation and has been extensively studied for a long time. While most of previous game-theoretic work adopt the imitation learning where players imitate the strategies who are better off, the learning logic in the real world is often much more complex. In this work, we turn to the reinforcement learning paradigm, where individuals make their decisions based upon their past experience and long-term returns. Specifically, we investigate the Prisoners' dilemma game with Q-learning algorithm, and cooperators probabilistically pose punishment on defectors in their neighborhood. Interestingly, we find that punishment could lead to either continuous or discontinuous cooperation phase transitions, and the nucleation process of cooperation clusters is reminiscent of the liquid-gas transition. The uncovered first-order phase transition indicates that great care needs to be taken when implementing the punishment compared to the continuous scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emergence of cooperation under punishment: A reinforcement learning perspective Zhao, Chenyang Zheng, Guozhong Zhang, Chun Zhang, Jiqiang Chen, Li Populations and Evolution Disordered Systems and Neural Networks Adaptation and Self-Organizing Systems Physics and Society Punishment is a common tactic to sustain cooperation and has been extensively studied for a long time. While most of previous game-theoretic work adopt the imitation learning where players imitate the strategies who are better off, the learning logic in the real world is often much more complex. In this work, we turn to the reinforcement learning paradigm, where individuals make their decisions based upon their past experience and long-term returns. Specifically, we investigate the Prisoners' dilemma game with Q-learning algorithm, and cooperators probabilistically pose punishment on defectors in their neighborhood. Interestingly, we find that punishment could lead to either continuous or discontinuous cooperation phase transitions, and the nucleation process of cooperation clusters is reminiscent of the liquid-gas transition. The uncovered first-order phase transition indicates that great care needs to be taken when implementing the punishment compared to the continuous scenario. |
| title | Emergence of cooperation under punishment: A reinforcement learning perspective |
| topic | Populations and Evolution Disordered Systems and Neural Networks Adaptation and Self-Organizing Systems Physics and Society |
| url | https://arxiv.org/abs/2401.16073 |