Two aims of behavioral science, often pursued separately, are to model the evolutionary dynamics and cognitive mechanisms governing behavior in social conflicts. Combining these approaches, we show that the dynamics of proximate mechanisms such as reward learning can play a pivotal role in determining evolutionary outcomes. We focus on a widespread feature of human social life: People engage in retributive punishment with surprising disregard for its efficacy, yet they respond to punishment with behavioral flexibility finely tuned to costs and benefits. We explain this pattern and offer a general picture of when evolution favors rigid versus flexible social behaviors.

Abstract

Natural selection designs some social behaviors to depend on flexible learning processes, whereas others are relatively rigid or reflexive. What determines the balance between these two approaches? We offer a detailed case study in the context of a two-player game with antisocial behavior and retaliatory punishment. We show that each player in this game—a “thief” and a “victim”—must balance two competing strategic interests. Flexibility is valuable because it allows adaptive differentiation in the face of diverse opponents. However, it is also risky because, in competitive games, it can produce systematically suboptimal behaviors. Using a combination of evolutionary analysis, reinforcement learning simulations, and behavioral experimentation, we show that the resolution to this tension—and the adaptation of social behavior in this game—hinges on the game’s learning dynamics. Our findings clarify punishment’s adaptive basis, offer a case study of the evolution of social preferences, and highlight an important connection between natural selection and learning in the resolution of social conflicts.