Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. Based on my discussion in this chapter, I conclude that ABMs can explain causal mechanisms but cannot provide emergence explanations, because they cannot provide information about exactly why low-level phenomena give rise to those emergent phenomena. This is also one reason why I believe that ABMs cannot help with causal inference. Another reason is that ABMs do not reflect real-world processes but the causal-mechanical intuitions of the modeler. However, ABMs can integrate “impossible” multi-scale interactions between initiators, mediators, moderators, and conditions, and may be useful as comprehensive etio-prognostic explanations of illness occurrence and outcome, which in turn can go a long way reducing the uncertainty in medicine about disease etiology and prognosis.