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Do probabilistic expert elicitations capture scientists’ uncertainty about climate change?

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  • Antony Millner

    ()

  • Raphael Calel
  • David Stainforth
  • George MacKerron

Abstract

Expert elicitation studies have become important barometers of scientific knowledge about future climate change (Morgan and Keith, Environ Sci Technol 29(10), 1995 ; Reilly et al., Science 293(5529):430–433, 2001 ; Morgan et al., Climate Change 75(1–2):195–214, 2006 ; Zickfeld et al., Climatic Change 82(3–4):235–265, 2007 , Proc Natl Acad Sci 2010 ; Kriegler et al., Proc Natl Acad Sci 106(13):5041–5046, 2009 ). Elicitations incorporate experts’ understanding of known flaws in climate models, thus potentially providing a more comprehensive picture of uncertainty than model-driven methods. The goal of standard elicitation procedures is to determine experts’ subjective probabilities for the values of key climate variables. These methods assume that experts’ knowledge can be captured by subjective probabilities—however, foundational work in decision theory has demonstrated this need not be the case when their information is ambiguous (Ellsberg, Q J Econ 75(4):643–669, 1961 ). We show that existing elicitation studies may qualitatively understate the extent of experts’ uncertainty about climate change. We designed a choice experiment that allows us to empirically determine whether experts’ knowledge about climate sensitivity (the equilibrium surface warming that results from a doubling of atmospheric CO 2 concentration) can be captured by subjective probabilities. Our results show that, even for this much studied and well understood quantity, a non-negligible proportion of climate scientists violate the choice axioms that must be satisfied for subjective probabilities to adequately describe their beliefs. Moreover, the cause of their violation of the axioms is the ambiguity in their knowledge. We expect these results to hold to a greater extent for less understood climate variables, calling into question the veracity of previous elicitations for these quantities. Our experimental design provides an instrument for detecting ambiguity, a valuable new source of information when linking climate science and climate policy which can help policy makers select decision tools appropriate to our true state of knowledge. Copyright Springer Science+Business Media Dordrecht 2013

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Bibliographic Info

Article provided by Springer in its journal Climatic Change.

Volume (Year): 116 (2013)
Issue (Month): 2 (January)
Pages: 427-436

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Handle: RePEc:spr:climat:v:116:y:2013:i:2:p:427-436

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Web page: http://www.springer.com/economics/journal/10584

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  1. Lemoine, Derek M. & Traeger, Christian P., 2011. "Tipping points and ambiguity in the economics of climate change," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9nd591ww, Department of Agricultural & Resource Economics, UC Berkeley.
  2. Machina, Mark J & Schmeidler, David, 1992. "A More Robust Definition of Subjective Probability," Econometrica, Econometric Society, vol. 60(4), pages 745-80, July.
  3. Richard T. Woodward & Richard C. Bishop, 1997. "How to Decide When Experts Disagree: Uncertainty-Based Choice Rules in Environmental Policy," Land Economics, University of Wisconsin Press, vol. 73(4), pages 492-507.
  4. Camerer, Colin & Weber, Martin, 1992. " Recent Developments in Modeling Preferences: Uncertainty and Ambiguity," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 325-70, October.
  5. Antony Millner & Simon Dietz & Geoffrey Heal, 2013. "Scientific Ambiguity and Climate Policy," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 55(1), pages 21-46, May.
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Cited by:
  1. Geoffrey Heal & Antony Millner, 2013. "Uncertainty and decision in climate change economics," Grantham Research Institute on Climate Change and the Environment Working Papers 108, Grantham Research Institute on Climate Change and the Environment.

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