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Joint-outcome prediction markets for climate risks

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  • Mark S Roulston
  • Kim Kaivanto

Abstract

Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. Predictions must account for climate-change mitigation policies which may depend on climate predictions. This interdependency, or “circularity”, means that climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range forecasts also suffer from information asymmetry because users cannot use track records to judge the skill of providers. The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with joint-outcome spaces, allowing simultaneous forecasts of GHG concentrations and temperature. The viability of prediction markets with highly granular, joint-outcome spaces was tested with markets for monthly UK rainfall and temperature. The experiments demonstrate these markets can aggregate the judgments of experts with relevant expertise, and suggest similarly structured markets, with longer horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and risk disclosure.

Suggested Citation

  • Mark S Roulston & Kim Kaivanto, 2024. "Joint-outcome prediction markets for climate risks," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-14, August.
    • Handle: RePEc:plo:pone00:0309164
    DOI: 10.1371/journal.pone.0309164
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    1. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2022. "Manipulation and (Mis)trust in Prediction Markets," Management Science, INFORMS, vol. 68(9), pages 6716-6732, September.
    2. Aaron L. Jackson & Scott Sumner, 2006. "Velocity Futures Markets: Does the Fed Need a Structural Model?," Economic Inquiry, Western Economic Association International, vol. 44(4), pages 716-728, October.
    3. Abraham Othman & Tuomas Sandholm, 2013. "The Gates Hillman prediction market," Review of Economic Design, Springer;Society for Economic Design, vol. 17(2), pages 95-128, June.
    4. Aliakbari, Elmira & McKitrick, Ross, 2018. "Information aggregation in a prediction market for climate outcomes," Energy Economics, Elsevier, vol. 74(C), pages 97-106.
    5. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    6. Ben S. Bernanke & Michael Woodford, 1997. "Inflation forecasts and monetary policy," Proceedings, Federal Reserve Bank of Cleveland, pages 653-686.
    7. Robin Hanson & Ryan Oprea, 2009. "A Manipulator Can Aid Prediction Market Accuracy," Economica, London School of Economics and Political Science, vol. 76(302), pages 304-314, April.
    8. Joel Katzav & Erica L. Thompson & James Risbey & David A. Stainforth & Seamus Bradley & Mathias Frisch, 2021. "On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives," Climatic Change, Springer, vol. 169(1), pages 1-20, November.
    9. Mark Roulston & Todd Kaplan & Brett Day & Kim Kaivanto, 2022. "Prediction-market innovations can improve climate-risk forecasts," Nature Climate Change, Nature, vol. 12(10), pages 879-880, October.
    10. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    11. Sailor, D.J & Hu, T & Li, X & Rosen, J.N, 2000. "A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change," Renewable Energy, Elsevier, vol. 19(3), pages 359-378.
    12. Katzav, Joel & Thompson, Erica L. & Risbey, James & Stainforth, David A. & Bradley, Seamus & Frisch, Mathias, 2021. "On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives," LSE Research Online Documents on Economics 112978, London School of Economics and Political Science, LSE Library.
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