This study delves into the complex dynamics of human behavior in the context of pandemic crowding, employing agent-based models with adaptive learning mechanisms. The abstract explores the innovative approach of integrating adaptive learning into agent-based models to simulate and understand how individuals respond to crowded environments during pandemics. By combining insights from behavioral science and computational modeling, this research aims to unravel nuanced patterns in human decision-making, contributing to the development of more robust strategies for managing and mitigating the impact of pandemics on crowded spaces.
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