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Evolution of Conditional Cooperation in Prisoner's Dilemma

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  • Saral, Ali Seyhun
Abstract
In this study, we investigate conditional types and their evolution in an iterated Prisoner's Dilemma, comparing different continuation probabilities, by using a computational model. In our setting, agents are characterized by their responses to each level of cooperation in a linearly extended Prisoner's Dilemma. By using repeated simulations, we estimate the likelihood of cooperation and the conditional strategies that are likely to succeed. Our results show that, when the continuation probability is sufficiently large, full cooperation is achieved. In this case, the most successful strategies are the ones who employ an all-or-none type of conditional cooperation, followed by perfect conditional cooperators. In the intermediate levels of continuation probability, however, hump-shaped contributor types are the ones that are most likely to exist, followed by imperfect conditional cooperators. Those agents cooperate in a medium level of cooperation within themselves and each other. Our results provide an explanation for the commonly observed hump-shaped strategy and imperfect conditional cooperators in experiments. Furthermore, a potential implication of our results is that the heterogeneity of conditional strategies might stem from the diverse interaction frequencies among real-world interactions.

Suggested Citation

  • Saral, Ali Seyhun, 2020. "Evolution of Conditional Cooperation in Prisoner's Dilemma," OSF Preprints wcpkz, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:wcpkz
    DOI: 10.31219/osf.io/wcpkz
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    References listed on IDEAS

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    Cited by:

    1. Geng, Yini & Liu, Yifan & Lu, Yikang & Shen, Chen & Shi, Lei, 2022. "Reinforcement learning explains various conditional cooperation," Applied Mathematics and Computation, Elsevier, vol. 427(C).

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