Catalytic evolution of cooperation in a population with behavioral bimodality.

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 and investigate the impact of the mode mixture on the evolution of cooperation. While players in a Q-learning mode aim to maximize their accumulated payoffs, players within a 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 the mode mixture greatly promotes the overall cooperation prevalence. The promotion is even more significant in the probabilistic mixing, where players randomly select one of the two rules at each step. Finally, this promotion is robust when players adaptively choose the two modes by a real-time comparison. In all three scenarios, players within the Q-learning mode act as catalyzers that turn the TFT players to be more cooperative and as a result drive the whole population to be highly cooperative. The analysis of Q-tables explains the underlying mechanism of cooperation promotion, which captures the "psychological evolution" in the players' minds. Our study indicates that the variety of behavioral modes is non-negligible and could be crucial to clarify the emergence of cooperation in the real world.