Dynamic aspiration based on Win-Stay-Lose-Learn rule in spatial prisoner's dilemma game

• Published in: PloS one Vol. 16; no. 1; p. e0244814
• Main Authors: Shi, Zhenyu, Wei, Wei, Feng, Xiangnan, Li, Xing, Zheng, Zhiming
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04-01-2021; Public Library of Science (PLoS)
• Subjects: Behavior; Big Data; Biology and Life Sciences; Boundary conditions; Brain research; Coexistence; Computer and Information Sciences; Cooperation; Cooperation (Economics); Cooperative Behavior; Education; Evolution; Game Theory; Games; Health risks; Human acts; Human behavior; Humans; Informatics; Laboratories; Learning; Mathematics; Models, Theoretical; Monte Carlo simulation; Motivation; Motivation (Psychology); Nodes; Physical Sciences; Political asylum; Prisoner Dilemma; Prisoners; Psychological research; Reciprocity; Research and analysis methods; Semantics; Social aspects; Social Sciences; China; Beijing China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0244814

Prisoner's dilemma game is the most commonly used model of spatial evolutionary game which is considered as a paradigm to portray competition among selfish individuals. In recent years, Win-Stay-Lose-Learn, a strategy updating rule base on aspiration, has been proved to be an effective model to promote cooperation in spatial prisoner's dilemma game, which leads aspiration to receive lots of attention. In this paper, according to Expected Value Theory and Achievement Motivation Theory, we propose a dynamic aspiration model based on Win-Stay-Lose-Learn rule in which individual's aspiration is inspired by its payoff. It is found that dynamic aspiration has a significant impact on the evolution process, and different initial aspirations lead to different results, which are called Stable Coexistence under Low Aspiration, Dependent Coexistence under Moderate aspiration and Defection Explosion under High Aspiration respectively. Furthermore, a deep analysis is performed on the local structures which cause defectors' re-expansion, the concept of END- and EXP-periods are used to justify the mechanism of network reciprocity in view of time-evolution, typical feature nodes for defectors' re-expansion called Infectors, Infected nodes and High-risk cooperators respectively are found. Compared to fixed aspiration model, dynamic aspiration introduces a more satisfactory explanation on population evolution laws and can promote deeper comprehension for the principle of prisoner's dilemma.