Trust-Aware Reinforcement Learning Mechanisms for Dynamic Capacity Sharing in Decentralized Supply Networks

Abstract

Decentralized supply networks have become the dominant architecture for producing and delivering goods, yet they face persistent coordination failures when capacity must be dynamically shared among autonomous firms. Traditional contract-based mechanisms and centralized optimization often prove brittle in the face of demand volatility, information asymmetry, and the absence of enforceable authority. This paper proposes a novel class of trust-aware reinforcement learning mechanisms that enable dynamic capacity sharing by synthesizing multi-agent learning with computational models of inter-organizational trust. We conceptualize trust not as a static binary variable but as a continuously updated, multidimensional representation of a partner’s reliability, reciprocity, and fairness, which directly shapes the reward structures and policy updates of RL agents. The system-level architecture is presented as a hybrid governance layer in which decentralized learning nodes interact through a shared trust ledger, while a lightweight digital coordination layer handles the broadcasting of capacity requests and reputation signals. We examine structural trade-offs between exploration for network efficiency and the preservation of trust capital, highlighting how myopic RL policies erode long-term collaboration potential. The discussion extends to infrastructure requirements for deploying such mechanisms at scale, including the role of industrial data spaces, digital twins for counterfactual training, and regulatory frameworks for algorithmic capacity allocation. Additionally, we analyze fairness and robustness, demonstrating that trust-aware mechanisms can mitigate bullwhip amplification and reduce the frequency of cascading shortages in disrupted supply networks. By integrating computational trust into the RL objective, the proposed mechanisms reframe capacity sharing as a cooperative sequential decision problem in which agents jointly learn to balance self-interest with systemic resilience. We conclude by outlining policy considerations for promoting algorithmic transparency and accountability in autonomous supply networks.