Evolutionary Cooperation with Game Transitions via Markov Decision Chain in Networked Population

TL;DR

This paper models the evolution of cooperation in networked populations using Markov decision chains to analyze game transitions and their effects on strategy dynamics, revealing that strategic transitions can promote cooperation.

Contribution

It introduces a Markov decision chain based model for game transitions and extends strategy imitation to multiple rounds, providing new insights into cooperation evolution.

Findings

Game transitions driven by strategies promote cooperation

Higher transition rates accelerate cooperative behavior

Different Markov chains offer practical guidance for swarm intelligence

Abstract

Individual cooperative strategy influences the surrounding dynamic population, which in turn affects cooperative strategy. To better model this phenomenon, we develop a Markov decision chain based game transitions model and examine the dynamic transitions in game states of individuals within a network and their impact on the strategy's evolution. Additionally, we extend single-round strategy imitation to multiple rounds to better capture players' potential non-rational behavior. Using intensive simulations, we explore the effects of transition probabilities and game parameters on game transitions and cooperation. Our study finds that strategy-driven game transitions promote cooperation, and increasing the transition rates of Markov decision chains can significantly accelerate this process. By designing different Markov decision chains, these results provide simulation based guidance for practical applications in swarm intelligence, such as strategic collaboration.