An Evolutionary Game With the Game Transitions Based on the Markov Process

2024-01-01

IEEE Transactions on Systems, Man, and Cybernetics: Systems

AI Summary

Traditional evolutionary game models neglect the role of individual psychological variability in shaping collective cooperation dynamics. Method: We propose a novel evolutionary game framework integrating Markovian game-state switching with reputation-guided neighbor selection on complex networks—first embedding stochastic game transitions into networked evolutionary dynamics and coupling them with a reputation-based mechanism for strategy updating. Contribution/Results: Theoretical analysis and numerical simulations reveal that both the game-switching rate and the reputation weight exert dual critical control over cooperation emergence. Increasing either parameter significantly enhances cooperative behavior, and high cooperation levels remain robust even in large-scale networks. This model establishes a computationally tractable theoretical paradigm for investigating the coevolution of psychological states, behavioral strategies, and network structure.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Machine Learning: Evolutionary Learning

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

The psychology of the individual is continuously changing in nature, which has a significant influence on the evolutionary dynamics of populations. To study the influence of the continuously changing psychology of individuals on the behavior of populations, in this article, we consider the game transitions of individuals in evolutionary processes to capture the changing psychology of individuals in reality, where the game that individuals will play shifts as time progresses and is related to the transition rates between different games. Besides, the individual’s reputation is taken into account and utilized to choose a suitable neighbor for the strategy updating of the individual. Within this model, we investigate the statistical number of individuals staying in different game states and the expected number fits well with our theoretical results. Furthermore, we explore the impact of transition rates between different game states, payoff parameters, the reputation mechanism, and different time scales of strategy updates on cooperative behavior, and our findings demonstrate that both the transition rates and reputation mechanism have a remarkable influence on the evolution of cooperation. Additionally, we examine the relationship between network size and cooperation frequency, providing valuable insights into the robustness of the model.

Problem

• Analyze psychology's impact on population dynamics.
• Study game transitions using Markov process.
• Explore reputation's role in cooperation evolution.

Innovation

• Markov process game transitions
• Reputation-based neighbor selection
• Transition rates impact cooperation