Evolution of cooperation facilitated by reinforcement learning with adaptive aspiration levels

TL;DR

This paper demonstrates through modeling and simulations that reinforcement learning with adaptive aspiration levels promotes the evolution of cooperation in social dilemma games, illustrating the Baldwin effect where learning accelerates evolution.

Contribution

It introduces a reinforcement learning model with adaptive aspiration levels to explain how cooperation evolves in repeated social dilemmas, highlighting the role of learning in evolution.

Findings

Learning enables the evolution of cooperation.

Adaptive dynamics predict evolutionary outcomes.

Learning accelerates evolution to optimal cooperation.

Abstract

Repeated interaction between individuals is the main mechanism for maintaining cooperation in social dilemma situations. Variants of tit-for-tat (repeating the previous action of the opponent) and the win-stay lose-shift strategy are known as strong competitors in iterated social dilemma games. On the other hand, real repeated interaction generally allows plasticity (i.e., learning) of individuals based on the experience of the past. Although plasticity is relevant to various biological phenomena, its role in repeated social dilemma games is relatively unexplored. In particular, if experience-based learning plays a key role in promotion and maintenance of cooperation, learners should evolve in the contest with nonlearners under selection pressure. By modeling players using a simple reinforcement learning model, we numerically show that learning enables the evolution of cooperation. We also show that numerically estimated adaptive dynamics appositely predict the outcome of evolutionary simulations. The analysis of the adaptive dynamics enables us to capture the obtained results as an affirmative example of the Baldwin effect, where learning accelerates the evolution to optimality.