Intelligence, Errors and Strategic Choices in the Repeated Prisoners' Dilemma

E. Proto, A. Rustichini, Andis Sofianos
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引用次数: 9

Abstract

A large literature in behavioral economics has emphasized in the last decades the role of individual differences in social preferences (such as trust and altruism) in influencing behavior in strategic environments. Here we emphasize the role of attention and working memory, and show that social interactions among heterogeneous groups are mediated by differences in cognitive skills. Our design uses a repeated prisoner’s dilemma; we compare rates of cooperation in groups of subjects separated according to their IQ, with those in integrated groups, where subjects of different IQ are pooled together. In integrated groups we observe higher aggregated cooperation rates and profits than in separated groups. There are gains in earnings among lower IQ subjects who learn how to cooperate faster than when they play separately, and smaller losses for higher IQ subjects. We also see that higher IQ subjects become less lenient when they are matched with lower IQ subjects than when they play separately. This pattern is an instance of a general phenomenon, which we demonstrate in an evolutionary game theory model, in which higher IQ among subjects induces –possibly thanks to better working memory– a lower frequency of errors in strategy implementation. We show that players indeed choose less-lenient strategies in environments in which subjects have higher error rates. Estimations of errors and strategies from the experimental data are consistent with the hypothesis and model’s predictions.
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重复囚徒困境中的智力、错误与战略选择
在过去的几十年里,大量的行为经济学文献强调了社会偏好(如信任和利他主义)中的个体差异在影响战略环境中的行为中的作用。本研究强调了注意力和工作记忆的作用,并表明异质群体之间的社会互动是由认知技能差异介导的。我们的设计使用了重复的囚徒困境;我们比较了按智商分组的实验对象的合作率,以及不同智商的实验对象聚集在一起的实验对象的合作率。我们观察到,在整合的群体中,总合作率和利润高于分离的群体。智商较低的人学习如何合作比单独玩时更快,他们的收入会增加,而智商较高的人的损失则较小。我们还发现,高智商的人与低智商的人在一起玩比单独玩时更不宽容。这种模式是一种普遍现象的一个例子,我们在进化博弈论模型中证明了这一点,在这种模型中,高智商的受试者可能由于更好的工作记忆而导致策略执行中出错的频率较低。我们发现,在犯错率较高的环境中,玩家确实会选择不那么宽松的策略。从实验数据中估计的误差和策略与假设和模型的预测一致。
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