Game-Theoretic Capacity Pooling under AI-Predicted Supply Chain Disruptions

Abstract

The growing frequency and severity of supply chain disruptions driven by geopolitical turbulence, climate change, and pandemics compel industrial systems to move beyond firm-level redundancy toward collaborative capacity pooling. This paper presents a system-level analysis of how artificial intelligence-based disruption forecasting can be fused with game-theoretic mechanisms to design stable, efficient, and fair capacity sharing arrangements across independent enterprises. We examine the structural trade-offs inherent in architectures that translate probabilistic AI predictions into cooperative capacity allocation rules, highlighting the interplay between predictive performance, incentive compatibility, strategic behavior, and operational robustness. The discussion develops a layered view of infrastructure, encompassing data integration fabrics, AI prediction services, game-theoretic coordination engines, and smart contract-based settlement layers. Key challenges are unpacked, including the governance of asymmetric information, the calibration of fairness criteria under uncertainty, adversarial manipulation of prediction models, and regulatory constraints on cross-border data sharing. We contrast formal mechanism design approaches with trust-based relational contracts, identifying complementarities that are essential for sustaining capacity pools over extended time horizons. The paper further considers the implications of such systems for sustainability, industrial policy, and the reconfiguration of global supply networks toward collective resilience. By integrating perspectives from operations management, machine learning, economic mechanism design, and digital infrastructure, the analysis provides a road map for building AI-mediated capacity pooling platforms that do not merely optimize short-term cost but also strengthen long-term systemic robustness and equity.