The Collaborative Evolution of Trust, Information Flow, and Social Cooperation: A Study on Network Stability Based on Dynamic Game Models

This study designed a dynamic game model based on trust algorithms to explore the processes of information flow, cooperation, and network formation within human social relationship networks. Through simulations, the results indicate that as the network evolves, the characteristics of nodes begin to differentiate from the outset of network formation. Higher-level nodes exhibit a significant advantage in information transmission capabilities compared to other nodes, demonstrating stronger collaborative behaviors and cooperative abilities. During the network formation process, instances of betrayal among nodes gradually decrease as the network stabilizes. Additionally, higher-level nodes display more pronounced prosocial behaviors, and the micro-preferences of these nodes can influence the informal norms of the network. The composition of human social relationship networks is achieved through continuous information flow, facilitating effective social connections. The structure and stability of human social relationship networks underscore the positive mutual reinforcement mechanisms between trust, information management, and social cooperation.