Agents of collaborative adaptation: modelling temporal resilience in supply chains

Supply-chain disruptions vary in intensity, scale, and frequency, producing temporally heterogeneous impacts on network viability. The emphasis on exposure and topology has anchored resilience in network structure, obscuring how collaboration and trust govern its phase-specific temporal dynamics, including resistive capabilities, adaptation speed, containment, and recovery. This study thus develops an agent-based simulation of the Netherlands logistics network, in which nodes operate as autonomous agents that redistribute load under capacity, link, and trust constraints. Cascading failures are simulated with trust-gated, capacity-aware, and collaboration-dependent load reallocation, capturing both immediate contagion and multi-step redistribution dynamics. The framework compares rule-based heuristics with adaptive AI agents trained using supervised imitation, online multiplicative adaptation, and cross-entropy policy search. Temporal resilience is then measured across each phase specific metrics of absorptive, adaptive, and recovery phases across the collaboration structure. This points to the main contribution of this article, in which AI agents improve adaptation and cascade containment through targeted use of high-trust, high-capacity links, while recovery dynamics remain bounded by structural and exogenous constraints. This points to a layered view of resilience, where performance depends on the interaction between network configuration, trust-mediated coordination, and agent-level adaptation.