Hybrid Reinforcement Learning and Contract Theory for Dynamic Capacity Sharing Decisions
Keywords:
capacity sharing, contract theory, reinforcement learning, multi-agent systems, dynamic resource allocation, governance, fairnessAbstract
The increasing fragmentation of production, logistics, and digital service infrastructures has elevated dynamic capacity sharing to a critical coordination challenge across contemporary large-scale systems. Traditional centralized optimization methods often fail to cope with the scale, uncertainty, and strategic behaviors inherent in multi-stakeholder environments. This paper presents a systematic investigation of a hybrid framework that integrates contract theory with reinforcement learning to govern capacity sharing decisions in real time. Contract theory provides a principled approach to aligning incentives and mitigating information asymmetries, while reinforcement learning enables adaptive, experience-driven policy improvement under stochastic demand. The proposed architecture treats contracts as both economic and computational constraints that shape the action space and reward structure of learning agents, thereby preserving strategic coherence while enabling autonomous adaptation. The discussion spans structural trade-offs in system architecture, the governance of decentralized infrastructures, deployment considerations for scalable learning, and policy implications surrounding fairness, sustainability, and regulatory compliance. By reframing capacity sharing not as a pure optimization problem but as a socio-technical governance challenge, the hybrid paradigm offers a path toward resilient and equitable resource coordination in next-generation industrial ecosystems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
