Modeling Community Resilience Under Prolonged Disruption: An Agent-Based Framework Integrating Social Connectivity, Migration, and Policy-Driven Allocation

Communities under prolonged disruptions operate as interconnected socio-technical systems in which the effectiveness of any response depends not only on local conditions but also on the structural relationships that link communities to one another. This study introduces an agent-based response framework for evaluating policy-driven intervention strategies across such systems. Each community is described by its population, economic conditions, and access to critical services, and is linked to other communities through a social connectivity network that defines the pathways for population movement and channels the spread of disruption stress between regions. The agent-based model then tracks how vulnerable each community is by combining its local conditions with the conditions of the communities it is most connected to, and it measures the toll of any disruption through a single social cost metric that weighs lost access to healthcare, retail, and food services. The framework is instantiated using county-level COVID-19 data for Illinois, treated as an exogenous hazard input, and evaluated through Monte Carlo simulation across risk-averse, risk-neutral, risk-seeking, adaptive, and no-aid policy regimes. Compared with the no-aid baseline, the highest-intensity (risk-averse) regime produced the lowest social cost and the highest level of assistance, while all intervention regimes resulted in lower migration. Adaptive managerial decision-making was shown to offer no consistent advantage over simple proactive rules, suggesting that consistency and speed of allocation, rather than sophistication, drive system-wide outcomes.