困境
计算机科学
囚徒困境
粒子群优化
选择(遗传算法)
人口
过程(计算)
机制(生物学)
群体行为
进化算法
进化动力学
人工智能
数学优化
机器学习
数学
认识论
社会学
人口学
哲学
几何学
操作系统
作者
Zhihao Yang,Yadong Yang
标识
DOI:10.1088/1674-1056/ad20d8
摘要
Abstract In evolutionary games, most studies on finite populations have focused on a single updating mechanism. However, given the differences in individual cognition, individuals may change their strategies according to different updating mechanisms. For this reason, we consider two different aspiration-driven updating mechanisms in structured populations: "Satisfied-Stay Unsatisfied-Shift" (SSUS) and "Satisfied-Cooperate Unsatisfied-Defect" (SCUD). To simulate the game player's learning process, this paper improves the particle swarm optimization algorithm, which will be used to simulate the game player's strategy selection, i.e., population particle swarm optimization algorithms (PPSO). We find that in the Prisoner's Dilemma, the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge. In contrast, SCUD conditions that promote the evolution of cooperation enable cooperation to emerge. In addition, the invasion of SCUD individuals helped promote cooperation among SSUS individuals. Simulated by the PPSO algorithm, the theoretical approximation results were found to be consistent with the trend of change in the simulation results.
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