Catalytic evolution of cooperation in a population with behavioural bimodality
Anhui Sheng, Jing Zhang, Guozhong Zheng, Jiqiang Zhang, Weiran Cai,, and Li Chen

TL;DR
This study explores how behavioral bimodality, combining Q-learning and Tit-for-Tat strategies, promotes cooperation in populations playing the prisoner's dilemma, revealing that mixed behavioral modes significantly enhance cooperative outcomes.
Contribution
It introduces a model integrating two behavioral modes and demonstrates that their mixture promotes cooperation more effectively than single modes.
Findings
Mode mixture greatly increases cooperation prevalence.
Probabilistic mixing enhances cooperation promotion.
Adaptive mode switching sustains high cooperation levels.
Abstract
The remarkable adaptability of humans in response to complex environments is often demonstrated by the context-dependent adoption of different behavioral modes. However, the existing game-theoretic studies mostly focus on the single-mode assumption, and the impact of this behavioral multimodality on the evolution of cooperation remains largely unknown. Here, we study how cooperation evolves in a population with two behavioral modes. Specifically, we incorporate Q-learning and Tit-for-Tat (TFT) rules into our toy model, where prisoner's dilemma game is played and we investigate the impact of the mode mixture on the evolution of cooperation. While players in Q-learning mode aim to maximize their accumulated payoffs, players within TFT mode repeat what their neighbors have done to them. In a structured mixing implementation where the updating rule is fixed for each individual, we find that…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Opinion Dynamics and Social Influence
MethodsFocus · Q-Learning
