Social norms and cooperation in a collective-risk social dilemma: comparing reinforcing learning and norm-based approaches

N. Payette, Áron Székely, G. Andrighetto
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Abstract

Human cooperation is both powerful and puzzling. Large-scale cooperation among genetically unrelated individuals makes humans unique with respect to all other animal species. Therefore, learning how cooperation emerges and persists is a key question for social scientists. Recently, scholars have recognized the importance of social norms as solutions to major local and large-scale collective action problems, from the management of water resources to the reduction of smoking in public places to the change in fertility practices. Yet a well-founded model of the effect of social norms on human cooperation is still lacking.We present here a version of the Experience-Weighted Attraction (EWA) reinforcement learning model that integrates norm-based considerations into its utility function that we call EWA+Norms. We compare the behaviour of this hybrid model to the standard EWA when applied to a collective risk social dilemma in which groups of individuals must reach a threshold level of cooperation to avoid the risk of catastrophe. We find that standard EWA is not sufficient for generating cooperation, but that EWA+Norms is. Next step is to compare simulation results with human behaviour in large-scale experiments.
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集体风险社会困境中的社会规范与合作:比较强化学习和基于规范的方法
人类的合作既强大又令人费解。基因不相关的个体之间的大规模合作使人类在所有其他动物物种中独树一帜。因此,了解合作是如何产生和持续的是社会科学家的一个关键问题。最近,学者们已经认识到社会规范作为解决重大地方和大规模集体行动问题的重要性,从水资源管理到减少公共场所吸烟,再到改变生育习惯。然而,社会规范对人类合作的影响仍然缺乏一个有充分根据的模型。我们在这里提出了一个版本的经验加权吸引力(EWA)强化学习模型,该模型将基于规范的考虑集成到其效用函数中,我们称之为EWA+规范。在集体风险社会困境中,个体群体必须达到一定的合作阈值水平才能避免灾难风险,我们将这种混合模型的行为与标准EWA进行了比较。我们发现标准的EWA不足以产生合作,而EWA+规范则足以产生合作。下一步是将模拟结果与大规模实验中的人类行为进行比较。
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