Mathematical models play an important role in informing public policy decisions, and in recent months, Covid-19 has pushed these concepts and tools to the fore. The “Policy Models in Pandemic” essays, part of the “Covid-19 and the Social Sciences” series, take a close look at modeling and its implications for policymaking in the current moment. Curated by Michael Feuer (George Washington University), contributing authors—accomplished theorists and practitioners of the policy sciences—illuminate the underlying purposes, assumptions, and architecture of a subset of models; describe briefly how these methods and computational capacities have evolved; and offer recommendations for continued improvements. Rather than cheerlead haphazardly for any and all models, these essays underscore the utility models provide while acknowledging and appreciating their limits. Our hope is that readers infer the beginnings of a framework for choosing models appropriate to different kinds of decision situations, become energized to build new models, and are inspired to consider, and communicate, cautions and caveats in using them. It is perhaps obvious that embedded in these essays is an implicit (causal) model: Better understanding of how models are constructed will lead to better uses—subject, of course, to underlying assumptions, the quality of data, and the validity of inferences. Professor Feuer is grateful to Ron Kassimir for hearing and encouraging the idea for this series, to the contributors for joining the initiative on relatively short notice, and to Juni Ahari and Rodrigo Ugarte for their superb editorial assistance.

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