  The COVID-19 pandemic highlighted the critical role of human behavior in
influencing infectious disease transmission and the need for models capturing
this complex dynamic. We present an agent-based model integrating an
epidemiological simulation of disease spread with a cognitive architecture
driving individual mask-wearing decisions. Agents decide whether to mask based
on a utility function weighting factors like peer conformity, personal risk
tolerance, and mask-wearing discomfort. By conducting experiments
systematically varying behavioral model parameters and social network
structures, we demonstrate how adaptive decision-making interacts with network
connectivity patterns to impact population-level infection outcomes. The model
provides a flexible computational framework for gaining insights into how
behavioral interventions like mask mandates may differentially influence
disease spread across communities with diverse social structures. Findings
highlight the importance of integrating realistic human decision processes in
epidemiological models to inform policy decisions during public health crises.
