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We study the evolution of behavior under reinforcement learning in aPrisoner's Dilemma where agents interact in a regular network and can learnabout whether they play one-shot or repeatedly by incurring a cost ofdeliberation. With respect to other behavioral rules used in the literature,(i) we confirm the existence of a threshold value of the probability ofrepeated interaction, switching the emergent behavior from intuitive defectorto dual-process cooperator; (ii) we find a different role of the node degree,with smaller degrees reducing the evolutionary success of dual-processcooperators; (iii) we observe a higher frequency of deliberation.

Coevolution of cognition and cooperation in structured populations under reinforcement learning