Coevolution of cognition and cooperation in structured populations under reinforcement learning

Abstract: 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.