Agent-based models (ABMs) examine emergent phenomena that arise from individual agent rules. This work extends the basic ABM paradigm in three key areas: data integration, evaluation of policies, and incorporating machine learning techniques. The dissertation investigates how data-driven approaches can enhance the accuracy of ABMs, explores the practical applications of ABMs in developing policies for real-world issues, and examines the fusion of machine learning with ABMs to optimize model design and functionality.The dissertation begins by establishing the background and fundamental principles of agent-based models, highlighting their evolution from simple cellular automata to intricate systems encapsulating decision-making and adaptive behavior. It examines the role of these models in simulating dynamic interactions within systems, especially in scenarios where traditional methods may fall short of capturing the complexities of agent interactions. Following the introduction of key concepts, a series of projects that function in pairs demonstrate the versatility of ABMs and address the three key areas discussed above.The first pair addresses data integration of GPS deer movement data into a generalized Langevin model and its use in uncertainty quantification of disease spread. Exploratory data analysis revealed a discernible non-parametric trend in the GPS data with non-Gaussian statistics. This analysis led to a model that is consistent with the observed data. Subsequent incorporation of chronic wasting disease (CWD) and population dynamics were used to forecast the prevalence of CWD. This extended model was analyzed with a global sensitivity analysis that tied variance in disease prevalence to variance in the parameters of the model, providing predictions of future prevalence of the disease.The second pair examines policy evaluation, specifically strategies for mitigating disinformation in social networks. Multiple strategies were evaluated on various topologically diverse networks that led to policy recommendations. Simulations on these graphs revealed challenges associated with large network simulations, particularly in computational cost and influence of network topologies. These challenges led to a method to miniaturize real social networks while preserving key attributes, enabling more efficient and realistic simulations to run on artificial social networks.The final pair investigates the possibility of inverting the ABM paradigm to instead have agents learn their own rules through environmental interactions. Reinforcement learning was applied to a model of conflict based on capture the flag, where an agent learned in progressively difficult competitions. The emergence of deterrence was explored through adding asymmetries between competing teams, and differential equation-based models were created to help interpret results.
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