Coevolution of cognition and cooperation in structured populations under reinforcement learning

• Published in: Chaos, solitons and fractals Vol. 182; p. 114799
• Main Authors: Mastrandrea, Rossana, Boncinelli, Leonardo, Bilancini, Ennio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-05-2024
• Subjects: Agent based model; Cognition; Dual process cooperation; Network; Prisoner’s Dilemma; Structured populations; Cognition; Prisoner’s Dilemma; Structured populations; Agent based model; Dual process cooperation; Network
• ISSN: 0960-0779; 1873-2887
• DOI: 10.1016/j.chaos.2024.114799

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.
•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.