随机区组模型上的合作演化
Evolution of Cooperation on Stochastic Block Models
摘要：
合作是人类社会发展的一个重要因素。人类社交网络的结构影响着合作和其他人际现象的动态，具有共同的结构特征。这些特征之一是组织成团体的趋势。网络理论家用来模拟这一特征的生成模型之一是随机块模型（SBM）。本文研究了SBM网络上的演化博弈动力学。利用最近发现的演化博弈和合并随机游动之间的对偶性，我们得到了自然选择有利于合作而不是背叛的分析条件。我们计算每个社区的转变点，以支持合作。我们发现，一个关键的社区间的链接创建概率存在给定的组密度，这样的整体网络支持合作，即使个别社区抑制it.As的副产品，我们提出了平均场的解决方案的关键效益成本比，表现出显着的精度为不同的生成网络模型，包括那些与社区结构和重尾度分布。我们还证明了任意两个玩家游戏的结果的普遍性。
英文摘要：
Cooperation is a major factor in the evolution of human societies. The structure of human social networks, which affects the dynamics of cooperation and other interpersonal phenomena, have common structural signatures. One of these signatures is the tendency to organize as groups. Among the generative models that network theorists use to emulate this feature is the Stochastic Block Model (SBM). In this paper, we study evolutionary game dynamics on SBM networks. Using a recently-discovered duality between evolutionary games and coalescing random walks, we obtain analytical conditions such that natural selection favors cooperation over defection. We calculate the transition point for each community to favor cooperation. We find that a critical inter-community link creation probability exists for given group density, such that the overall network supports cooperation even if individual communities inhibit it. As a byproduct, we present mean-field solutions for the critical benefit-to-cost ratio which perform with remarkable accuracy for diverse generative network models, including those with community structure and heavy-tailed degree distributions. We also demonstrate the generalizability of the results to arbitrary two-player games.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
