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宽恕在交替囚徒困境中占优势。

Forgiver triumphs in alternating Prisoner's Dilemma.

机构信息

Program for Evolutionary Dynamics, Harvard University, Cambridge, Massachusetts, United States of America.

IST Austria (Institute of Science and Technology Austria), Klosterneuburg, Lower Austria, Austria.

出版信息

PLoS One. 2013 Dec 12;8(12):e80814. doi: 10.1371/journal.pone.0080814. eCollection 2013.

DOI:10.1371/journal.pone.0080814
PMID:24349017
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3861238/
Abstract

Cooperative behavior, where one individual incurs a cost to help another, is a wide spread phenomenon. Here we study direct reciprocity in the context of the alternating Prisoner's Dilemma. We consider all strategies that can be implemented by one and two-state automata. We calculate the payoff matrix of all pairwise encounters in the presence of noise. We explore deterministic selection dynamics with and without mutation. Using different error rates and payoff values, we observe convergence to a small number of distinct equilibria. Two of them are uncooperative strict Nash equilibria representing always-defect (ALLD) and Grim. The third equilibrium is mixed and represents a cooperative alliance of several strategies, dominated by a strategy which we call Forgiver. Forgiver cooperates whenever the opponent has cooperated; it defects once when the opponent has defected, but subsequently Forgiver attempts to re-establish cooperation even if the opponent has defected again. Forgiver is not an evolutionarily stable strategy, but the alliance, which it rules, is asymptotically stable. For a wide range of parameter values the most commonly observed outcome is convergence to the mixed equilibrium, dominated by Forgiver. Our results show that although forgiving might incur a short-term loss it can lead to a long-term gain. Forgiveness facilitates stable cooperation in the presence of exploitation and noise.

摘要

合作行为,即一个个体为帮助另一个个体而付出代价,是一种广泛存在的现象。在这里,我们研究了交替囚徒困境背景下的直接互惠。我们考虑了可以由单状态和双状态自动机实现的所有策略。我们计算了存在噪声时所有成对遭遇的收益矩阵。我们探索了有突变和无突变的确定性选择动力学。使用不同的错误率和收益值,我们观察到收敛到少数几个不同的平衡点。其中两个是非合作严格纳什均衡,分别代表总是背叛(ALLD)和 Grim。第三个平衡点是混合的,代表了几种策略的合作联盟,由我们称之为“宽恕者”的策略主导。宽恕者只要对手合作,就会合作;一旦对手背叛,就会背叛一次,但随后宽恕者会尝试重新建立合作关系,即使对手再次背叛。宽恕者不是一种进化稳定策略,但它所统治的联盟是渐近稳定的。对于广泛的参数值,最常见的结果是收敛到由宽恕者主导的混合平衡点。我们的结果表明,尽管宽恕可能会导致短期损失,但它可以带来长期收益。宽恕有助于在剥削和噪声存在的情况下稳定合作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/be27d07da72e/pone.0080814.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/874207552382/pone.0080814.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/9086ece63c9d/pone.0080814.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/d637dce2e4f1/pone.0080814.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/be27d07da72e/pone.0080814.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/874207552382/pone.0080814.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/9086ece63c9d/pone.0080814.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/d637dce2e4f1/pone.0080814.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1602/3861238/be27d07da72e/pone.0080814.g004.jpg

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