Simulating Cooperation and Defection Dynamics in Social Networks Using Generative AI: A New Perspective from Evolutionary Game Theory

This article introduces an innovative open-source project that uses generative AI technology to simulate cooperation and defection behaviors in social networks. By combining the framework of evolutionary game theory, it provides computational tools for understanding human social interactions. The project aims to break through the limitations of traditional research methods, use AI to capture complex dynamic interactions in social networks, and promote interdisciplinary research.

Cooperative behavior in human society is a core issue in social sciences, but traditional mathematical modeling and small-scale experiments struggle to capture the complex dynamics of real social networks. The breakthrough of generative AI provides new tools for research; this project combines evolutionary game theory with AI to explore cooperation and defection dynamics.

Evolutionary game theory is a key framework for understanding the emergence of cooperation (such as the classic Prisoner's Dilemma model), but real social interactions are far more complex than two-person games. The project uses generative AI to drive a multi-agent simulation environment, where each agent is equipped with an AI model that can generate natural language responses. This simulates the evolution of strategies in social networks and captures the complexity and adaptability of behaviors.

Social network structures (such as small-world networks and scale-free networks) have a significant impact on the spread of cooperation. Reputation mechanisms form social norms by recording interaction history and spreading reputation information, effectively punishing defectors and rewarding cooperators. The project can simulate the impact of different network topologies and reputation systems on cooperative behaviors.

This technology can be applied in fields such as social science theory verification, public policy simulation (evaluating the impact of interventions on cooperation levels), online community behavior analysis, and AI safety research (multi-agent alignment issues).

In the future, we look forward to more innovative projects that further promote the deep integration of generative AI and social sciences, help understand the mysteries of human social behavior, and open up new possibilities for interdisciplinary research.

Applying generative AI to social science computational modeling represents a new direction in artificial intelligence research, demonstrating AI's potential to simulate complex social phenomena and providing new tools for interdisciplinary research.