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. This paper focuses on three philosophical aspects of ABMs as models of causal mechanisms, as generators of emergent phenomena, and as providers of explanation. Based on my discussion, I conclude that while ABMs cannot help much with causal inference, they can be viewed as etio-prognostic explanations of illness occurrence and outcome.
Computational modeling and simulation of real-life scenarios have become a mainstay in health research and the biosciences. My goal in this interdisciplinary paper, written from my personal perspective as physician, epidemiologist, and philosopher, is to provide an analysis of the explanatory scope of agent-based models (ABMs), one particular kind of modeling technique employed in the context of the COVID-19 pandemic…
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