Agent-based modeling is a computational dynamic modeling technique that may be less familiar to some readers. Agent-based modeling seeks to understand the behaviour 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 behaviour. 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.

翻译：智能体建模是一种计算动态建模技术，部分读者可能对此不甚熟悉。该技术通过在环境中设置智能体，研究智能体之间及智能体与环境交互所产生的涌现行为，从而理解复杂系统的运行机制。相较于分室模型，智能体模型能以更简单、可扩展性更强且更灵活的方式表征异质性，能够捕捉动态与静态网络及空间背景信息，并能考虑模型内个体的历史轨迹。相反，分室模型具有开发周期更短、所需编程工作量更少、计算需求不随群体规模扩增的优势，同时支持采用常微分方程、延迟微分方程或随机微分方程进行简明数学建模，便于推导系统行为特性。本章首先介绍智能体模型的基本特征，继而讨论其与分室模型相比的优缺点，最后剖析两个基于智能体的传染病模型实例。