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Polar Bear Population Forecasts: A Public-Policy Forecasting Audit

Author

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  • J. Scott Armstrong

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Kesten C. Green

    (Business and Economic Forecasting, Monash University, Victoria 3800, Australia)

  • Willie Soon

    (Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138)

Abstract

Calls to list polar bears as a threatened species under the United States Endangered Species Act are based on forecasts of substantial long-term declines in their population. Nine government reports were written to help US Fish and Wildlife Service managers decide whether or not to list polar bears as a threatened species. We assessed these reports based on evidence-based (scientific) forecasting principles. None of the reports referred to sources of scientific forecasting methodology. Of the nine, Amstrup et al. [Amstrup, S. C., B. G. Marcot, D. C. Douglas. 2007. Forecasting the rangewide status of polar bears at selected times in the 21st century. Administrative Report, USGS Alaska Science Center, Anchorage, AK.] and Hunter et al. [Hunter, C. M., H. Caswell, M. C. Runge, S. C. Amstrup, E. V. Regehr, I. Stirling. 2007. Polar bears in the Southern Beaufort Sea II: Demography and population growth in relation to sea ice conditions. Administrative Report, USGS Alaska Science Center, Anchorage, AK.] were the most relevant to the listing decision, and we devoted our attention to them. Their forecasting procedures depended on a complex set of assumptions, including the erroneous assumption that general circulation models provide valid forecasts of summer sea ice in the regions that polar bears inhabit. Nevertheless, we audited their conditional forecasts of what would happen to the polar bear population assuming , as the authors did, that the extent of summer sea ice would decrease substantially during the coming decades. We found that Amstrup et al. properly applied 15 percent of relevant forecasting principles and Hunter et al. 10 percent. Averaging across the two papers, 46 percent of the principles were clearly contravened and 23 percent were apparently contravened. Consequently, their forecasts are unscientific and inconsequential to decision makers. We recommend that researchers apply all relevant principles properly when important public-policy decisions depend on their forecasts.

Suggested Citation

  • J. Scott Armstrong & Kesten C. Green & Willie Soon, 2008. "Polar Bear Population Forecasts: A Public-Policy Forecasting Audit," Interfaces, INFORMS, vol. 38(5), pages 382-405, October.
    • Handle: RePEc:inm:orinte:v:38:y:2008:i:5:p:382-405
    DOI: 10.1287/inte.1080.0383
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    References listed on IDEAS

    as
    1. JS Armstrong, 2004. "The Seer-Sucker Theory: The Value of Experts in Forecasting," General Economics and Teaching 0412009, University Library of Munich, Germany.
    2. Schnaars, Steven P. & Bavuso, R. Joseph, 1986. "Extrapolation models on very short-term forecasts," Journal of Business Research, Elsevier, vol. 14(1), pages 27-36, February.
    3. Kesten C. Green & J. Scott Armstrong, 2007. "Global Warming: Forecasts by Scientists Versus Scientific Forecasts," Energy & Environment, , vol. 18(7), pages 997-1021, December.
    4. Armstrong, J. Scott & Green, Kesten C. & Jones, Randall J. & Wright, Malcolm, 2008. "Predicting elections from politicians’ faces," MPRA Paper 9150, University Library of Munich, Germany.
    5. Robert Fildes & Paul Goodwin, 2007. "Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting," Interfaces, INFORMS, vol. 37(6), pages 570-576, December.
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    Cited by:

    1. Green, Kesten C & Armstrong, J Scott & Soon, Willie, 2008. "Benchmark forecasts for climate change," MPRA Paper 12163, University Library of Munich, Germany.
    2. Steven C. Amstrup & Hal Caswell & Eric DeWeaver & Ian Stirling & David C. Douglas & Bruce G. Marcot & Christine M. Hunter, 2009. "Rebuttal of “Polar Bear Population Forecasts: A Public-Policy Forecasting Audit”," Interfaces, INFORMS, vol. 39(4), pages 353-369, August.
    3. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.

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    More about this item

    Keywords

    adaptation; bias; climate change; decision making; endangered species; expert opinion; extinction; evaluation; evidence-based principles; expert judgment; forecasting methods; global warming; habitat loss; mathematical models; scientific method; sea ice;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H0 - Public Economics - - General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C0 - Mathematical and Quantitative Methods - - General
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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