Cognitively-plausible reinforcement learning in epidemiological agent-based simulations

Human behavior shapes the transmission of infectious diseases and determines the effectiveness of public health measures designed to mitigate transmission. To accurately reflect these dynamics, epidemiological simulation models should endogenously account for both disease transmission and behavioral dynamics. Traditional agent-based models (ABMs) often rely on simplified rules to represent behavior, limiting their ability to capture complex decision-making processes and cognitive dynamics.

Reinforcement Learning (RL) provides a framework for modeling how agents adapt their behavior based on experience and feedback. However, implementing cognitively plausible RL in ABMs is challenging due to high-dimensional state spaces. We propose a novel framework based on Adaptive Control of Thought-Rational (ACT-R) principles and Instance-Based Learning (IBL), which enables agents to dynamically adapt their behavior using nonparametric RL without requiring extensive training on large datasets.

To demonstrate this framework, we model mask-wearing behavior during the COVID-19 pandemic, highlighting how individual decisions and social network structures influence disease transmission. Simulations reveal that local social cues drive tightly clustered masking behavior (slope = 0.54, Pearson r = 0.76), while reliance on global cues alone produces weakly disassortative patterns (slope = 0.05, Pearson r = 0.09), underscoring the role of local information in coordinating public health compliance.

Our results show that this framework provides a scalable and cognitively interpretable approach to integrating adaptive decision-making into epidemiological simulations, offering actionable insights for public health policy.