Coevolution of cognition and cooperation in structured populations under reinforcement learning

CRediT authorship contribution statement Declaration of competing interest Acknowledgments Data availability References Show full outline Figures (4) Unlabelled figure Unlabelled figure Fig. 1. Strategy evolution and network structure Fig. 2. Dual-process cooperation and network structure Elsevier Chaos, Solitons & Fractals Volume 182, May 2024, 114799 Chaos, Solitons & Fractals Coevolution of cognition and cooperation in structured populations under reinforcement learning Author links open overlay panelRossana Mastrandrea a, Leonardo Boncinelli b, Ennio Bilancini a Show more Add to Mendeley Share Cite https://doi.org/10.1016/j.chaos.2024.114799 Get rights and content Highlights • Interactions structure and mode of cognition affect the evolution of cooperation. • Reinforcement learning (RL): a behavioral rule requiring small amount information. • Low probabilities of repeated interaction favor the intuitive defector behavior under RL. • Dual-process cooperation is favored for high probabilities of repeated interaction under RL. • Novelty: reinforcement learning promote cooperation on regular graph with high degree. We study the evolution of behavior under reinforcement learning in a Prisoner’s Dilemma where agents interact in a regular network and can learn about whether they play one-shot or repeatedly by incurring a cost of deliberation. With respect to other behavioral rules used in the literature, (i) we confirm the existence of a threshold value of the probability of repeated interaction, switching the emergent behavior from intuitive defector to dual-process cooperator; (ii) we find a different role of the node degree, with smaller degrees reducing the evolutionary success of dual-process cooperators; (iii) we observe a higher frequency of deliberation.