Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic

Robin L. Dillon (), Vicki M. Bier (), Richard Sheffield John () and Abdullah Althenayyan ()
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Robin L. Dillon: Georgetown University, Washington, District of Columbia 20057
Vicki M. Bier: University of Wisconsin–Madison, Madison, Wisconsin 53706
Richard Sheffield John: University of Southern California, Los Angeles, California 90007
Abdullah Althenayyan: Columbia University, New York, New York 10027

Decision Analysis, 2023, vol. 20, issue 2, 109-132

Abstract: Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful.

Keywords: decision analysis; risk; pandemic; COVID-19 (search for similar items in EconPapers)
Date: 2023
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