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Uncertainty and Decision-Making During a Crisis: How to Make Policy Decisions in the COVID-19 Context?

Author

Listed:
  • Loïc Berger

    (University of Lille - IESES School of Management; CNRS; RFF-CMCC European Institute on Economics and the Environment)

  • Nicolas Berger

    (London School of Hygiene and Tropical Medicine - Faculty of Public Health and Policy; Sciensano)

  • Valentina Bosetti

    (RFF-CMCC European Institute on Economics and the Environment’ Bocconi University - Department of Economics and IGIER)

  • Itzhak Gilboa

    (HEC; Tel Aviv University - Eitan Berglas School of Economics)

  • Lars Peter Hansen

    (University of Chicago - Department of Economics; University of Chicago - Department of Statistics; University of Chicago - Booth School of Business)

  • Christopher Jarvis

    (London School of Hygiene and Tropical Medicine - Department of Infectious Disease Epidemiology)

  • Massimo Marinacci

    (University of Bacconi - Department of Decision Sciences and IGIER)

  • Richard D. Smith

    (University of Exeter - College of Medicine and Health; London School of Hygiene and Tropical Medicine - Faculty of Public Health and Policy)

Abstract

Policymaking during a pandemic can be extremely challenging. As COVID-19 is a new disease and its global impacts are unprecedented, decisions need to be made in a highly uncertain, complex and rapidly changing environment. In such a context, in which human lives and the economy are at stake, we argue that using ideas and constructs from modern decision theory, even informally, will make policymaking more a responsible and transparent process.

Suggested Citation

  • Loïc Berger & Nicolas Berger & Valentina Bosetti & Itzhak Gilboa & Lars Peter Hansen & Christopher Jarvis & Massimo Marinacci & Richard D. Smith, 2020. "Uncertainty and Decision-Making During a Crisis: How to Make Policy Decisions in the COVID-19 Context?," Working Papers 2020-95, Becker Friedman Institute for Research In Economics.
  • Handle: RePEc:bfi:wpaper:2020-95
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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    2. Ghirardato, Paolo & Maccheroni, Fabio & Marinacci, Massimo, 2004. "Differentiating ambiguity and ambiguity attitude," Journal of Economic Theory, Elsevier, vol. 118(2), pages 133-173, October.
    3. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    4. Peter Klibanoff & Massimo Marinacci & Sujoy Mukerji, 2005. "A Smooth Model of Decision Making under Ambiguity," Econometrica, Econometric Society, vol. 73(6), pages 1849-1892, November.
    5. Ilke Aydogan & Loic Berger & Valentina Bosetti & Ning Liu, 2018. "Three layers of uncertainty: an experiment," Working Papers 623, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    6. Marcus Keogh-Brown & Richard Smith & John Edmunds & Philippe Beutels, 2010. "The macroeconomic impact of pandemic influenza: estimates from models of the United Kingdom, France, Belgium and The Netherlands," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(6), pages 543-554, December.
    7. Lars Peter Hansen, 2014. "Nobel Lecture: Uncertainty Outside and Inside Economic Models," Journal of Political Economy, University of Chicago Press, vol. 122(5), pages 945-987.
    8. Marisa Beck & Tobias Krueger, 2016. "The epistemic, ethical, and political dimensions of uncertainty in integrated assessment modeling," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 7(5), pages 627-645, September.
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    Cited by:

    1. Bose, Subir & Daripa, Arup, 2023. "Eliciting second-order beliefs," Journal of Mathematical Economics, Elsevier, vol. 107(C).
    2. Colo, Philippe, 2021. "Expert-based Knowledge: Communicating over Scientific Models," MPRA Paper 110434, University Library of Munich, Germany.

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    Keywords

    Model uncertainty; ambiguity; robustness; decision rules;
    All these keywords.

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