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Betting and Belief: Prediction Markets and Attribution of Climate Change

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  • John J. Nay
  • Martin Van der Linden
  • Jonathan M. Gilligan
Abstract
Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational test-bed for climate prediction markets. Traders adapt their beliefs about future temperatures based on the profits of other traders in their social network. We simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and a hypothesis advanced by prominent skeptics. We conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect traders' beliefs about the cause of global climate change. Market participation causes most traders to converge quickly toward believing the "true" climate model, suggesting that a climate market could be useful for building public consensus.

Suggested Citation

  • John J. Nay & Martin Van der Linden & Jonathan M. Gilligan, 2016. "Betting and Belief: Prediction Markets and Attribution of Climate Change," Papers 1603.08961, arXiv.org, revised Jul 2016.
  • Handle: RePEc:arx:papers:1603.08961
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    References listed on IDEAS

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    1. Paul J. Healy & Sera Linardi & J. Richard Lowery & John O. Ledyard, 2010. "Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders," Management Science, INFORMS, vol. 56(11), pages 1977-1996, November.
    2. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    3. Wolfers, Justin & Zitzewitz, Eric, 2006. "Prediction Markets in Theory and Practice," CEPR Discussion Papers 5578, C.E.P.R. Discussion Papers.
    4. Tseng, Jie-Jun & Lin, Chih-Hao & Lin, Chih-Ting & Wang, Sun-Chong & Li, Sai-Ping, 2010. "Statistical properties of agent-based models in markets with continuous double auction mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1699-1707.
    5. Hanson, Robin & Oprea, Ryan & Porter, David, 2006. "Information aggregation and manipulation in an experimental market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(4), pages 449-459, August.
    6. Dan M. Kahan & Hank Jenkins-Smith & Donald Braman, 2011. "Cultural cognition of scientific consensus," Journal of Risk Research, Taylor & Francis Journals, vol. 14(2), pages 147-174, February.
    7. Jie-Jun Tseng & Chih-Hao Lin & Chih-Ting Lin & Sun-Chong Wang & Sai-Ping Li, 2010. "Statistical properties of agent-based models in markets with continuous double auction mechanism," Papers 1002.0917, arXiv.org.
    8. Reinhard Selten, 1998. "Axiomatic Characterization of the Quadratic Scoring Rule," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 43-61, June.
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