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Decision making under deep uncertainty for pandemic policy planning

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  • Hadjisotiriou, Sophie
  • Marchau, Vincent
  • Walker, Warren
  • Rikkert, Marcel Olde

Abstract

Policymakers around the world were generally unprepared for the global COVID-19 pandemic. As a result, the virus has led to millions of cases and hundreds of thousands of deaths. Theoretically, the number of cases and deaths did not have to happen (as demonstrated by the results in a few countries). In this pandemic, as in other great disasters, policymakers are confronted with what policy analysts call Decision Making under Deep Uncertainty (DMDU). Deep uncertainty requires policies that are not based on 'predict and act' but on ‘prepare, monitor, and adapt’, enabling policy adaptations over time as events occur and knowledge is gained. We discuss the potential of a DMDU-approach for pandemic decisionmaking.

Suggested Citation

  • Hadjisotiriou, Sophie & Marchau, Vincent & Walker, Warren & Rikkert, Marcel Olde, 2023. "Decision making under deep uncertainty for pandemic policy planning," Health Policy, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:hepoli:v:133:y:2023:i:c:s0168851023001161
    DOI: 10.1016/j.healthpol.2023.104831
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    References listed on IDEAS

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    1. Herman G. van de Werfhorst, 2021. "Inequality in learning is a major concern after school closures," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(20), pages 2105243118-, May.
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