Agent-Based Modeling and Its Trade-Offs: An Introduction and Examples

Abstract

Agent-based modeling is a computational dynamic modeling technique that may be less familiar to some readers. Agent-based modeling seeks to understand the behavior of complex systems by situating agents in an environment and studying the emergent outcomes of agent-agent and agent-environment interactions. In comparison with compartmental models, agent-based models offer simpler, more scalable, and flexible representation of heterogeneity, the ability to capture dynamic and static network and spatial context, and the ability to consider history of individuals within the model. In contrast, compartmental models offer faster development time with less programming required, lower computational requirements that do not scale with population, and the option for concise mathematical formulation with ordinary, delay, or stochastic differential equations supporting derivation of properties of the system behavior. In this chapter, basic characteristics of agent-based models are introduced; advantages and disadvantages of agent-based models, as compared with compartmental models, are discussed; and two example agent-based infectious disease models are reviewed.