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.
Banner photo credit: NC Department of Public Safety/Flickr
Testing and models, medical and educational, have come under scrutiny during the Covid-19 pandemic. In particular, the coronavirus crisis has accelerated the conversation on the challenges of educational testing. Here, William Dardick looks at the reliability, validity, and fairness of educational assessments, and how these varied characteristics all factor into how policymakers employ testing and their results.
The Covid-19 pandemic has highlighted epidemiological reliance upon models and statistics for understanding the impact and spread of the disease. Given the convergence of health and economics, it proves worthwhile to explore the origins, techniques, and status of econometric models as tools for health policy. For the “Covid-19 and the Social Sciences” series, Bryan Dowd recounts the history of econometrics and describes recent developments, showing that it has become a standard tool for analyzing data and informing policy decisions.
For the “Covid-19 and the Social Sciences” series, Moshe Justman asks whether there may be tradeoffs in a model’s precision and its ability to inform policy. Justman explores the rise of randomized control trials (RCTs) in economics as the “gold standard” for inferring causality, and provides a detailed account of Project STAR—a landmark RCT study in education. A lesson for research informing policy on Covid-19, he argues, is that the messier models of epidemiologists are more useful to practitioners than the purportedly more rigorous RCTs designed by health economists.
Scott Page, author of The Model Thinker, turns his thinking to responses to Covid-19 through the eyes of a modeler. Page argues that the way societies have engaged with the pandemic may produce innovations with effects that last beyond addressing the pandemic into areas such as health care, political participation, education, and more. Page highlights a series of models that can help to identify these innovations and their potential impact in the short and longer term.
In the latest contribution to the “Covid-19 and the Social Sciences” series, Xi Song addresses how the models and methods that focus on social mobility can help us think about the effects of Covid-19 pandemic in the short and long-term. Song takes us through a brief history of social mobility research, and its focus on changes in mobility over time, across generations, and in different locations. She then explores the kinds of social mobility questions that Covid-19 raises, both in the short-term for households that have suffered from the virus, and the longer-term impacts of this “exogenous shock” on patterns of employment trajectories, income, consumption, and beyond.
This contribution to the “Policy Models in Pandemic” theme, part of the “Covid-19 and the Social Sciences” series, by Jessica Ho, explores the use and relevance of life expectancy models. As the death toll resulting from Covid-19 rises, this essay turns an eye toward understanding these models and how sensitive they are to sudden shocks. In doing so, Ho suggests that researchers need to recognize the strengths and limitations of data produced from these models in the short-term, and also appreciate the crucial role such models will play in understanding the evolution of population health in the long term.
In this inaugural “Policy Models in Pandemic” essay, part of the “Covid-19 and the Social Sciences” series, Michael Feuer broadly explains mathematical and theoretical models. Recent media coverage has brought the concept of “models” to the fore, as ways to predict and understand the spread of Covid-19. Feuer shows that models are fundamentally representations of complex phenomena, aimed at guiding rational action or providing useful information to decision makers. Since public policy and public health decisions are often made based on information from mathematical and theoretical models, he suggests it prudent to review the origins, purposes, benefits, and inherent imperfections of these models as well as their value.
