Wang Jiasheng, Zhang Yichao, Guan Jihong, Zhou Shuigeng
Department of Computer Science and Technology, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education, Shanghai, 200092, China.
Sci Rep. 2017 Nov 14;7(1):15484. doi: 10.1038/s41598-017-15616-x.
In social gaming networks, previous studies extensively investigated the influence of a variety of strategies on reciprocal behaviors in the prisoner's dilemma game. The studied frameworks range from the case that an individual uniformly cooperates or defects with all social contacts, to the recently reported divide-and-conquer games, where an individual can choose a particular move to play with each neighbor. In this paper, we investigate a divide-and-conquer tournament among 14 well-known strategies on social gaming networks. In the tournament, an individual's fitness is measured by accumulated and average payoff aggregated for a certain number of rounds. On the base of their fitness, the evolution of the population follows a local learning mechanism. Our observation indicates that the distribution of individuals adopting a strategy in degree ranking fundamentally changes the frequency of the strategy. In the divide-and-conquer gaming networks, our result suggests that the connectivity in social networks and strategy are two key factors that govern the evolution of the population.
在社交游戏网络中，以往的研究广泛探讨了各种策略对囚徒困境博弈中互惠行为的影响。所研究的框架范围从个体对所有社会联系人统一合作或背叛的情况，到最近报道的分治博弈，即个体可以选择特定的行动与每个邻居进行博弈。在本文中，我们研究了社交游戏网络中14种著名策略之间的分治锦标赛。在锦标赛中，个体的适应度通过一定轮数累计的平均收益来衡量。基于它们的适应度，种群的进化遵循局部学习机制。我们的观察表明，采用某种策略的个体在度排名中的分布从根本上改变了该策略的频率。在分治博弈网络中，我们的结果表明，社交网络中的连通性和策略是控制种群进化的两个关键因素。
