Rationality-bounded adaptive learning in multi-agent dynamic games

Abstract

This paper presents an adaptive learning framework that aims to enable agents with bounded rationality to learn from dynamic games and making decisions accordingly. Within this framework, agents lack complete knowledge of future payoffs but are capable of forming beliefs based on their recognitive abilities and past experiences. Each region involved dynamically adjusts its strategy by considering the tradeoff between the current payoff and belief updated. Based on this premise, we propose the Boundedly Rational Multiagent Learning (BRML) algorithm and provide proof of its convergence under given conditions. To demonstrate the practicality of the BRML algorithm, we employ it to two critical dynamic games: repeated cooperative and non-cooperative games. Our results indicate that groups with bounded rationality can achieve coordination through the behavior of agents with high recognitive ability. Furthermore, Pareto optimality is successfully attained in the coordination problems through the application of this multiagent adaptive learning framework.