Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

doi:10.1088/1674-1056/acf702

中国物理B ›› 2024, Vol. 33 ›› Issue (3): 30202-030202.doi: 10.1088/1674-1056/acf702

Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

Jiaqi Li(李佳奇)^1,†, Jianlei Zhang(张建磊)², and Qun Liu(刘群)¹

1 Institute of Intelligent Information, Hexi University, Gansu 734000, China;
2 College of Artificial Intelligence, Nankai University, Tianjin 300350, China

收稿日期:2023-06-04 修回日期:2023-08-22 接受日期:2023-09-06 出版日期:2024-02-22 发布日期:2024-02-29
通讯作者: Jiaqi Li E-mail:lijiaqi006@126.com
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 61963013).

Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

Jiaqi Li(李佳奇)^1,†, Jianlei Zhang(张建磊)², and Qun Liu(刘群)¹

1 Institute of Intelligent Information, Hexi University, Gansu 734000, China;
2 College of Artificial Intelligence, Nankai University, Tianjin 300350, China

Received:2023-06-04 Revised:2023-08-22 Accepted:2023-09-06 Online:2024-02-22 Published:2024-02-29
Contact: Jiaqi Li E-mail:lijiaqi006@126.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 61963013).

摘要/Abstract

摘要： We propose a computing model in which individuals can automatically adjust their interaction intensity with their mentor according to the learning effect. This model is designed to investigate the cooperative dynamics of the spatial prisoner's dilemma. More specifically, when the cumulative payoff of a learner is more than his reference earning, he will strengthen his interaction with his mentor; otherwise, he will reduce it. The experimental results indicate that this mechanism can improve the emergence of cooperation in a networked population and that the driving coefficient of interaction intensity plays an important role in promoting cooperation. Interestingly, under a certain social dilemma condition, there exists a minimal driving coefficient that leads to optimal cooperation. This occurs due to a positive feedback effect between the individual's satisfaction frequency and the number of effective neighbors. Moreover, we find that the experimental results are in accord with theoretical predictions obtained from an extension of the classical pair-approximation method. Our conclusions obtained by considering relationships with mentors can provide a new perspective for future investigations into the dynamics of evolutionary games within structured populations.

关键词: self-adapting interaction, evolutionary game, mentor, spatial prisoner's dilemma

Abstract: We propose a computing model in which individuals can automatically adjust their interaction intensity with their mentor according to the learning effect. This model is designed to investigate the cooperative dynamics of the spatial prisoner's dilemma. More specifically, when the cumulative payoff of a learner is more than his reference earning, he will strengthen his interaction with his mentor; otherwise, he will reduce it. The experimental results indicate that this mechanism can improve the emergence of cooperation in a networked population and that the driving coefficient of interaction intensity plays an important role in promoting cooperation. Interestingly, under a certain social dilemma condition, there exists a minimal driving coefficient that leads to optimal cooperation. This occurs due to a positive feedback effect between the individual's satisfaction frequency and the number of effective neighbors. Moreover, we find that the experimental results are in accord with theoretical predictions obtained from an extension of the classical pair-approximation method. Our conclusions obtained by considering relationships with mentors can provide a new perspective for future investigations into the dynamics of evolutionary games within structured populations.

Key words: self-adapting interaction, evolutionary game, mentor, spatial prisoner's dilemma

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

02.50.Le

89.75.Fb (Structures and organization in complex systems) 05.45.Pq (Numerical simulations of chaotic systems)

Jiaqi Li(李佳奇), Jianlei Zhang(张建磊), and Qun Liu(刘群). Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma[J]. 中国物理B, 2024, 33(3): 30202-030202.

Jiaqi Li(李佳奇), Jianlei Zhang(张建磊), and Qun Liu(刘群). Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma[J]. Chin. Phys. B, 2024, 33(3): 30202-030202.

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Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

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