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论公平的进化起源。

On the evolutionary origins of equity.

作者信息

Debove Stéphane, Baumard Nicolas, André Jean-Baptiste

机构信息

Institut de Biologie de l'Ecole normale supérieure (IBENS), INSERM 1024, CNRS 8197, Ecole normale supérieure - PSL Research University, Paris, France.

Institut Jean-Nicod (CNRS - EHESS - ENS), Département d'Etudes Cognitives, Ecole normale supérieure - PSL Research University, Paris, France.

出版信息

PLoS One. 2017 Mar 21;12(3):e0173636. doi: 10.1371/journal.pone.0173636. eCollection 2017.

Abstract

Equity, defined as reward according to contribution, is considered a central aspect of human fairness in both philosophical debates and scientific research. Despite large amounts of research on the evolutionary origins of fairness, the evolutionary rationale behind equity is still unknown. Here, we investigate how equity can be understood in the context of the cooperative environment in which humans evolved. We model a population of individuals who cooperate to produce and divide a resource, and choose their cooperative partners based on how they are willing to divide the resource. Agent-based simulations, an analytical model, and extended simulations using neural networks provide converging evidence that equity is the best evolutionary strategy in such an environment: individuals maximize their fitness by dividing benefits in proportion to their own and their partners' relative contribution. The need to be chosen as a cooperative partner thus creates a selection pressure strong enough to explain the evolution of preferences for equity. We discuss the limitations of our model, the discrepancies between its predictions and empirical data, and how interindividual and intercultural variability fit within this framework.

摘要

公平被定义为根据贡献给予回报,在哲学辩论和科学研究中都被视为人类公平的核心方面。尽管对公平的进化起源进行了大量研究,但公平背后的进化原理仍然未知。在这里,我们研究在人类进化的合作环境背景下,如何理解公平。我们对一群合作生产和分配资源的个体进行建模,并根据他们愿意如何分配资源来选择合作伙伴。基于代理的模拟、一个分析模型以及使用神经网络的扩展模拟提供了趋同的证据,表明在这样的环境中公平是最佳的进化策略:个体通过根据自己和伙伴的相对贡献按比例分配利益来最大化自身适应性。因此,被选为合作伙伴的需求创造了足够强大的选择压力,足以解释对公平偏好的进化。我们讨论了模型的局限性、其预测与实证数据之间的差异,以及个体间和文化间的变异性如何适应这个框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfc/5360236/088ee4d3b45e/pone.0173636.g001.jpg

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