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在社会困境中,对称性保证了通过共同行动进行合理合作。

Symmetry warrants rational cooperation by co-action in Social Dilemmas.

作者信息

Sasidevan V, Sinha Sitabhra

机构信息

The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India.

出版信息

Sci Rep. 2015 Aug 12;5:13071. doi: 10.1038/srep13071.

Abstract

Is it rational for selfish individuals to cooperate? The conventional answer based on analysis of games such as the Prisoners Dilemma (PD) is that it is not, even though mutual cooperation results in a better outcome for all. This incompatibility between individual rationality and collective benefit lies at the heart of questions about the evolution of cooperation, as illustrated by PD and similar games. Here, we argue that this apparent incompatibility is due to an inconsistency in the standard Nash framework for analyzing non-cooperative games and propose a new paradigm, that of the co-action equilibrium. As in the Nash solution, agents know that others are just as rational as them and taking this into account lead them to realize that others will independently adopt the same strategy, in contrast to the idea of unilateral deviation central to Nash equilibrium thinking. Co-action equilibrium results in better collective outcomes for games representing social dilemmas, with relatively "nicer" strategies being chosen by rational selfish individuals. In particular, the dilemma of PD gets resolved within this framework, suggesting that cooperation can evolve in nature as the rational outcome even for selfish agents, without having to take recourse to additional mechanisms for promoting it.

摘要

自私的个体进行合作是否合理?基于对诸如囚徒困境(PD)等博弈的分析,传统答案是不合理,即便相互合作会给所有人带来更好的结果。个体理性与集体利益之间的这种不相容性是合作进化问题的核心,正如囚徒困境及类似博弈所表明的那样。在此,我们认为这种明显的不相容性是由于分析非合作博弈的标准纳什框架存在不一致性,并提出了一种新范式,即共同行动均衡。与纳什解一样,参与者知道其他人与他们一样理性,考虑到这一点会使他们意识到其他人会独立采用相同策略,这与纳什均衡思维中核心的单边偏离概念形成对比。对于代表社会困境的博弈,共同行动均衡会带来更好的集体结果,理性的自私个体选择相对“更友善”的策略。特别是,囚徒困境在这个框架内得到解决,这表明即使对于自私的个体,合作也可以作为理性结果在自然界中进化,而无需借助额外的促进机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e9d/4533599/5ec79cd90ba8/srep13071-f2.jpg

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