Q-learning-based migration leading to spontaneous emergence of segregation. (1st December 2022)

• Record Type: Journal Article
• Authors: He, Zhixue; Geng, Yini; Du, Chunpeng; Shi, Lei; Wang, Zhen
• Is Part Of: New journal of physics. Volume 24:Number 12(2022)
• Publication Date: 2022-12-01
• Subjects: segregation -- evolutionary game -- cooperation -- reinforcement learning -- migration; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1367-2630/acadfd
• Languages: English
• ISSNs: 1367-2630

Abstract

Abstract: Understanding population segregation and aggregation is a critical topic in social science. However, the mechanisms behind segregation are not well understood, especially in the context of conflicting profits. Here, in the context of evolutionary game theory, we study segregation by extending the prisoner's dilemma game to mobile populations. In the extended model, individuals' types are distinguished by their strategies, which may change adaptively according to their associated payoffs. In addition, individuals' migration decisions are determined by the Q-learning algorithm. On the one hand, we find that such a simple extension allows the formation of three different types of spontaneous segregation: (a) environmentally selective segregation; (b) exclusionary segregation; and (c) subgroup segregation. On the other hand, adaptive migration enhances network reciprocity and enables the dominance of cooperation in a dense population. The formation of these types of segregation and the enhanced network reciprocity are related to individuals' peer preference and profit preference. Our findings shed light on the importance of adaptive migration in self-organization processes and contribute to the understanding of segregation formation processes in evolving populations.