Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

doi:10.1088/1674-1056/ad20d8

中国物理B ›› 2024, Vol. 33 ›› Issue (5): 50203-050203.doi: 10.1088/1674-1056/ad20d8

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

Zhi-Hao Yang(杨智昊) and Yan-Long Yang(杨彦龙)†

Mathematics and Statistics School, Guizhou University, Guiyang 550025, China

收稿日期:2023-11-08 修回日期:2024-01-05 接受日期:2024-01-22 出版日期:2024-05-20 发布日期:2024-05-20
通讯作者: Yan-Long Yang E-mail:yylong1980@163.com
基金资助:
Project supported by the Doctoral Foundation Project of Guizhou University (Grant No. (2019)49), the National Natural Science Foundation of China (Grant No. 71961003), and the Science and Technology Program of Guizhou Province (Grant No. 7223).

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

Zhi-Hao Yang(杨智昊) and Yan-Long Yang(杨彦龙)†

Mathematics and Statistics School, Guizhou University, Guiyang 550025, China

Received:2023-11-08 Revised:2024-01-05 Accepted:2024-01-22 Online:2024-05-20 Published:2024-05-20
Contact: Yan-Long Yang E-mail:yylong1980@163.com
Supported by:
Project supported by the Doctoral Foundation Project of Guizhou University (Grant No. (2019)49), the National Natural Science Foundation of China (Grant No. 71961003), and the Science and Technology Program of Guizhou Province (Grant No. 7223).

摘要/Abstract

摘要： 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 (PPSO) algorithms. 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 helps promote cooperation among SSUS individuals. Simulated by the PPSO algorithm, the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

关键词: evolutionary game dynamics, aspiration-driven update, structured populations

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 (PPSO) algorithms. 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 helps promote cooperation among SSUS individuals. Simulated by the PPSO algorithm, the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

Key words: evolutionary game dynamics, aspiration-driven update, structured populations

中图分类号: (Decision theory and game theory)

02.50.Le

05.45.Pq (Numerical simulations of chaotic systems)

Zhi-Hao Yang(杨智昊) and Yan-Long Yang(杨彦龙). Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations[J]. 中国物理B, 2024, 33(5): 50203-050203.

Zhi-Hao Yang(杨智昊) and Yan-Long Yang(杨彦龙). Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations[J]. Chin. Phys. B, 2024, 33(5): 50203-050203.

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Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

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