Center for Systems and Control, College of Engineering, Peking University, Beijing, China.
Center for Polymer Studies, Department of Physics, Boston University, Boston, Massachusetts, United States of America.
PLoS Comput Biol. 2019 Apr 1;15(4):e1006947. doi: 10.1371/journal.pcbi.1006947. eCollection 2019 Apr.
Evolutionary game dynamics in structured populations has been extensively explored in past decades. However, most previous studies assume that payoffs of individuals are fully determined by the strategic behaviors of interacting parties, and social ties between them only serve as the indicator of the existence of interactions. This assumption neglects important information carried by inter-personal social ties such as genetic similarity, geographic proximity, and social closeness, which may crucially affect the outcome of interactions. To model these situations, we present a framework of evolutionary multiplayer games on graphs with edge diversity, where different types of edges describe diverse social ties. Strategic behaviors together with social ties determine the resulting payoffs of interactants. Under weak selection, we provide a general formula to predict the success of one behavior over the other. We apply this formula to various examples which cannot be dealt with using previous models, including the division of labor and relationship- or edge-dependent games. We find that labor division can promote collective cooperation markedly. The evolutionary process based on relationship-dependent games can be approximated by interactions under a transformed and unified game. Our work stresses the importance of social ties and provides effective methods to reduce the calculating complexity in analyzing the evolution of realistic systems.
过去几十年来，结构种群中的进化博弈动态已经得到了广泛的研究。然而，大多数先前的研究假设个体的收益完全由相互作用的各方的策略行为决定，它们之间的社会关系仅作为相互作用存在的指标。这种假设忽略了人际社会关系所携带的重要信息，例如遗传相似性、地理接近性和社交亲密性，这些信息可能会对相互作用的结果产生至关重要的影响。为了模拟这些情况，我们提出了一个带有边多样性的图上进化多人游戏框架，其中不同类型的边描述了不同的社会关系。策略行为和社会关系共同决定了相互作用者的收益。在弱选择的情况下，我们提供了一个通用公式来预测一种行为相对于另一种行为的成功。我们将此公式应用于各种无法使用先前模型处理的示例，包括劳动分工和关系或边依赖游戏。我们发现劳动分工可以显著促进集体合作。基于关系依赖游戏的进化过程可以通过转换和统一游戏下的相互作用来近似。我们的工作强调了社会关系的重要性，并提供了有效的方法来降低分析现实系统进化时的计算复杂度。
