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

2025-12-21

AI Summary

Traditional models of networked collective behavior neglect the dynamic co-evolution between individual cooperation strategies and the underlying game structure, assuming fixed game types regardless of strategy changes. Method: We propose a Markov decision chain–based mechanism for game-state transitions, wherein the type of social dilemma (e.g., Prisoner’s Dilemma, Snowdrift) evolves endogenously in response to agents’ strategy distributions. Strategy imitation is extended to multi-round interactions to capture bounded rationality. Contribution/Results: This work establishes the first integrated framework jointly modeling strategy evolution and game-type switching. Large-scale Monte Carlo simulations demonstrate that strategy-driven game transitions significantly enhance cooperation emergence; increasing the Markov transition rate accelerates cooperative evolution; and the resulting framework provides an interpretable, parameter-tunable dynamical model for studying collective intelligence and cooperative coordination in adaptive networks.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Applications and emergent phenomena

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.

Problem

• Modeling dynamic game transitions in networked populations
• Extending strategy imitation to multiple rounds for non-rational behavior
• Exploring transition probabilities' impact on cooperation evolution

Innovation

• Markov decision chain models game transitions
• Multiple-round strategy imitation captures non-rational behavior
• Strategy-driven transitions accelerate cooperation in networks