Efficiency-enhancing mechanisms and implementation paths of AI-driven collaborative disaster information systems

With natural disasters occurring with increasing frequency, the gap between macro-level situational awareness and micro-level field information can substantially reduce the efficiency of emergency logistics. Drawing on differential game theory, this study develops a multi-stakeholder model involving the government, enterprises, and the public under a cross-sector disaster information-matching framework. It compares the optimal collaborative strategies and system-state evolution under a traditional model and an AI-enabled model. The results show that the AI-enabled bidirectional information-matching mechanism can effectively alleviate information silos and logistical blind spots. In addition, both the intensity of cross-sector disaster information matching and the level of public participation significantly increase collaborative investment by the government, enterprises, and the public, thereby generating a scale amplification effect. Further analysis reveals that the collaborative benefits of the system are constrained by the costs of AI adoption. Only when the net benefits generated by AI are sufficient to offset the costs of platform construction and application can participating stakeholders maintain incentives for sustained collaboration, thereby continuously improving overall emergency support performance. These findings provide a theoretical basis for breaking down information barriers in emergency logistics and optimizing emergency resource allocation and dispatch decisions.