Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines

IF 4.1 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-06-06 DOI:10.1016/j.isci.2025.112833

Kinga Makovi , Jean-François Bonnefon , Mayada Oudah , Anahit Sargsyan , Talal Rahwan

引用次数: 0

Abstract

High levels of human-machine cooperation are required to combine the strengths of human and artificial intelligence. Here, we investigate strategies to overcome the machine penalty, where people are less cooperative with partners they assume to be machines, than with partners they assume to be humans. Using a large-scale iterative public goods game with nearly 2,000 participants, we find that peer rewards or peer punishments can both promote cooperation with partners assumed to be machines but do not overcome the machine penalty. Their combination, however, eliminates the machine penalty, because it is uniquely effective for partners assumed to be machines and inefficient for partners assumed to be humans. These findings provide a nuanced road map for designing a cooperative environment for humans and machines, depending on the exact goals of the designer.

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奖励和惩罚帮助人类克服对被认为是机器的合作伙伴的偏见

结合人类和人工智能的优势，需要高水平的人机合作。在这里，我们研究了克服机器惩罚的策略，在这种情况下，人们与他们认为是机器的伙伴比与他们认为是人类的伙伴更不合作。通过一个有近2000名参与者的大规模迭代公共物品博弈，我们发现同伴奖励或同伴惩罚都可以促进与假设为机器的伙伴的合作，但不能克服机器惩罚。然而，它们的组合消除了机器惩罚，因为它对假设为机器的伙伴是唯一有效的，而对假设为人类的伙伴是无效的。这些发现为设计人类和机器的合作环境提供了一个微妙的路线图，这取决于设计者的确切目标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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