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A Precautionary Tale of Uncertain Tail Fattening

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  • Martin L. Weitzman

Abstract

Suppose that there is a probability density function for how bad things might get, but that the overall rate at which this probability density function slims down to approach zero in the tail is uncertain. The paper shows how a basic precautionary principle of tail fattening could then apply. The worse is the contemplated damage, the more should a decision maker consider the bad tail to be among the relatively fatter-tailed possibilities. A rough numerical example is applied to the uncertain tail distribution of climate sensitivity.

Suggested Citation

  • Martin L. Weitzman, 2012. "A Precautionary Tale of Uncertain Tail Fattening," NBER Working Papers 18144, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:18144
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    1. Johanna Etner & Meglena Jeleva & Jean‐Marc Tallon, 2012. "Decision Theory Under Ambiguity," Journal of Economic Surveys, Wiley Blackwell, vol. 26(2), pages 234-270, April.
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    Cited by:

    1. Hans-Jürgen Nantke & Alfred Endres & Frederik Schaff & Till Requate & Susanne Dröge, 2013. "Scheitern der Reform des Emissionshandels: Verliert Europa die Vorreiterrolle in der Klimapolitik?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(12), pages 03-15, June.
    2. Max Gillman & Michal Kejak & Michal Pakoš, 2015. "Learning about Rare Disasters: Implications For Consumption and Asset Prices," Review of Finance, European Finance Association, vol. 19(3), pages 1053-1104.
    3. Max Gillman & Michal Kejak & Michal Pakos, 2014. "Learning about Disaster Risk: Joint Implications for Consumption and Asset Prices," CERGE-EI Working Papers wp507, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    4. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Active Learning about Climate Change," Working Paper Series 6513, Department of Economics, University of Sussex Business School.
    5. Lopez, Ramon E. & Pastén, Roberto & Gutiérrez Cubillos, Pablo, 2022. "Climate change in times of economic uncertainty: A perverse tragedy of the commons?," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 209-225.
    6. Edward B. Barbier & Joanne C. Burgess, 2019. "Scarcity and Safe Operating Spaces: The Example of Natural Forests," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(3), pages 1077-1099, November.
    7. Sturla F. Kvamsdal & Ivan Belik & Arnt Ove Hopland & Yuanhao Li, 2021. "A Machine Learning Analysis of the Recent Environmental and Resource Economics Literature," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 79(1), pages 93-115, May.
    8. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
    9. Hwang, In Chang & Tol, Richard S.J. & Hofkes, Marjan W., 2016. "Fat-tailed risk about climate change and climate policy," Energy Policy, Elsevier, vol. 89(C), pages 25-35.

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    More about this item

    JEL classification:

    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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