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

doi:10.1088/1674-1056/acf702

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.

Keywords: self-adapting interaction evolutionary game mentor spatial prisoner's dilemma

Received: 04 June 2023
Revised: 22 August 2023
Accepted manuscript online: 06 September 2023

PACS:	02.50.Le	(Decision theory and game theory)
	89.75.Fb	(Structures and organization in complex systems)
	05.45.Pq	(Numerical simulations of chaotic systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61963013).

Corresponding Authors: Jiaqi Li
E-mail: lijiaqi006@126.com

Cite this article:

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 2024 Chin. Phys. B 33 030202

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