Zhang Chunyan, Zhu Yuying, Chen Zengqiang, Zhang Jianlei
Department of Automation, College of Computer and Control Engineering, Nankai University, Tianjin 300071, PR China; Tianjin Key Laboratory of Intelligent Robotics, College of Computer and Control Engineering, Nankai University, Tianjin 300071, PR China.
Department of Automation, College of Computer and Control Engineering, Nankai University, Tianjin 300071, PR China; Tianjin Key Laboratory of Intelligent Robotics, College of Computer and Control Engineering, Nankai University, Tianjin 300071, PR China.
J Theor Biol. 2017 May 7;420:128-134. doi: 10.1016/j.jtbi.2017.03.006. Epub 2017 Mar 9.
One phenomenon or social institution often observed in multi-agent interactions is the altruistic punishment, i.e. the punishment of unfair behavior by others at a personal cost. Inspired by the works focusing on punishment and the intricate mechanism behind it, we theoretically study the strategy evolution in the framework of two-strategy game models with the punishment on defectors, moreover, the cost of punishing will be evenly shared among the cooperators. Theoretical computations suggest that larger punishment on defectors or smaller punishment cost incurred by cooperators will enhance the fixation of altruistic cooperation in the population. Through the replicate dynamics, the group size of the randomly selected individuals from the sufficiently large population will notably affect the strategy evolution in populations nested within a dilemma. By theoretical modeling the concept of shared cost for punishment from one point of view, our findings underscore the importance of punishment with shared cost as a factor in real-life decisions in an evolutionary game context.
在多智能体交互中经常观察到的一种现象或社会制度是利他惩罚，即个人以自身代价惩罚他人的不公平行为。受关注惩罚及其背后复杂机制的研究启发，我们在具有对背叛者惩罚的两策略博弈模型框架下从理论上研究策略演化，此外，惩罚成本将由合作者平均分担。理论计算表明，对背叛者更大的惩罚或合作者产生的更小惩罚成本将增强群体中利他合作的固定性。通过复制动态，从足够大的群体中随机选择的个体的群体规模将显著影响嵌套在两难困境中的群体的策略演化。从一个角度对惩罚的共享成本概念进行理论建模，我们的研究结果强调了共享成本惩罚作为进化博弈背景下现实生活决策中的一个因素的重要性。
