### Adaptive Learning in ABMS
[58] presented mathematical models for understanding the dynamics of human behavior during infectious disease epidemics. In their work, [PERSON] et al. [58] proposed two models: an Information Forgetting Curve (IFC) model and a Memory Reception Fading and Cumulating (MRFC) model. These models explored how individuals forget and relearn information about diseases and how this process affects their emotions and behavior during epidemics. The IFC model employs a forgetting curve to describe how disease information fades over time, while the MRFC model captures stochastic memory changes regarding disease information based on learning and forgetting. Their research applies these models to the 2009 H1N1 influenza epidemic, utilizing historical infection data and population characteristics to simulate its real-world impact. They stress the importance of understanding the diverse perspectives and emotions of agents in the face of disease information. Their work offers not only valuable insights into how individuals process disease information, but also provides mathematical models to analyze and simulate these phenomena. Their research highlights the significance of memory and learning processes in epidemic modeling, contributing to the growing body of literature on ABMS that examines learning mechanisms and human behavior dynamics in various epidemic scenarios. In the same context, [PERSON] et al. [59] explored the impact of disease transmission and governmental interventions on COVID-19. Their work offers a fresh perspective in the realm of ABMS models by departing from traditional rule-based government agents and adopting Machine Learning (ML) algorithms for decision-making. In their study, governments engage in collaborative, data-driven decision processes, sharing experiences to combat disease spread. They evaluated several ML algorithms, with c4.5 and Random Forest proving effective in enhancing government risk perception. Their research underscores the potential of ML-guided government decision-making to optimize disease control efforts, complementing our exploration of adaptive learning techniques in ABMS for epidemic modeling. In summary, the literature review provides insights into the evolving landscape of ABMS, particularly its role in understanding and influencing agent behavior within complex systems, such as disease diffusion and government interventions. Integrating Reinforcement Learning into ABMS represents a significant advancement, enabling the modeling of dynamic human behavior during epidemics. This innovation allows agents to flexibly adjust their strategies in response to changing conditions and personal experiences. It aligns with the broader application of artificial intelligence, offering promising avenues for more precise and responsive epidemic modeling and contributing to enhancing public health strategies. It is important to note that there is limited research on combining ABMS and adaptive learning techniques for modeling epidemic scenarios, with existing literature often focusing on disaster-recovery scenarios and employing artificial intelligence methods. Existing research underscores the importance of adopting specific social rules based on agent-based learning for modeling long-term effects. While these studies provide valuable insights into social learning as a product of individual decision-making, certain aspects remain relatively unexplored. Many studies lack detailed explanations of their proposed ABMS, and could benefit from formal experimental designs featuring confidence intervals and statistical significance tests. Consequently, this study addresses these modeling gaps by employing a comprehensive experimental design that includes ANOVA tables and confidence interval analyses. Our agent-based model encompasses three fundamental processes: daily campus routines, transmission dynamics, and adaptive learning.