AI-Assisted Regional Collaborative Game of an Emergency Supplies Reserve Supply Chain

This study is devoted to the analysis of regional collaboration in emergency supply chain reserves. To address this critical research issue, we have developed an AI-assisted tripartite evolutionary game model involving governments, manufacturers, and suppliers across different regions under demand uncertainties and resource disparities. In this study, we employ replicator dynamic equations to derive strategic evolution paths and utilize numerical simulations enhanced by AI-powered global sensitivity analysis for subsequent parameter sensitivity analysis, enabling a systematic examination of equilibrium conditions and stability strategies. Our research findings demonstrate that when government incentive mechanisms provide greater benefits than speculative gains then supply chain enterprises evolve toward collaborative strategies, with the system achieving optimal stability at the equilibrium where collaboration benefits outweigh costs. Our AI-enhanced analysis results also reveal that while higher subsidies accelerate enterprise participation, they may reduce government motivation, necessitating carefully balanced penalty scales to sustain long-term cooperation—findings validated through sensitivity analyses of key parameters. The study’s integration of game theory with AI techniques offers both theoretical innovation in multi-agent decision modeling and practical value for strengthening national emergency management frameworks.