Institute of Systems Engineering, Dalian University of Technology, Dalian, China.
Center for Big Data and Intelligent Decision-Making, Dalian University of Technology, Dalian, China.
Nat Comput Sci. 2022 Oct;2(10):677-686. doi: 10.1038/s43588-022-00334-w. Epub 2022 Oct 20.
Reciprocity is a simple principle for cooperation that explains many of the patterns of how humans seek and receive help from each other. To capture reciprocity, traditional models often assume that individuals use simple strategies with restricted memory. These memory-1 strategies are mathematically convenient, but they miss important aspects of human reciprocity, where defections can have lasting effects. Here we instead propose a strategy of cumulative reciprocity. Cumulative reciprocators count the imbalance of cooperation across their previous interactions with their opponent. They cooperate as long as this imbalance is sufficiently small. Using analytical and computational methods, we show that this strategy can sustain cooperation in the presence of errors, that it enforces fair outcomes and that it evolves in hostile environments. Using an economic experiment, we confirm that cumulative reciprocity is more predictive of human behaviour than several classical strategies. The basic principle of cumulative reciprocity is versatile and can be extended to a range of social dilemmas.
互惠是合作的一个简单原则，它解释了人类寻求和相互帮助的许多模式。为了捕捉互惠，传统模型通常假设个体使用具有有限记忆的简单策略。这些记忆 1 策略在数学上很方便，但它们忽略了人类互惠的重要方面，即背叛可能会产生持久的影响。在这里，我们提出了一种累积互惠策略。累积互惠者会计算他们与对手之前互动中合作的不平衡程度。只要这种不平衡程度足够小，他们就会合作。我们使用分析和计算方法表明，这种策略可以在存在错误的情况下维持合作，它可以强制执行公平的结果，并且它可以在敌对环境中进化。通过经济实验，我们证实累积互惠比几种经典策略更能预测人类行为。累积互惠的基本原理具有通用性，可以扩展到一系列社会困境。
