Betting and Belief: Prediction Markets and Attribution of Climate Change

Nay, J. J., Van der Linden, M., Gilligan, J. (2016). “Betting and Belief: Prediction Markets and Attribution of Climate Change.” Proceedings of the 2016 Winter Simulation Conference, 1666–1677. IEEE Press.

11 Pages Posted: 13 Jul 2016 Last revised: 21 Sep 2017

See all articles by John Nay

John Nay

New York University (NYU); Skopos Labs, Inc.

Martin Van der Linden

Emory University

Jonathan M. Gilligan

Vanderbilt University - Department of Earth and Environmental Sciences; Department of Civil and Environmental Engineering; Vanderbilt Institute for Energy and Environment

Date Written: July 11, 2016

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.

Keywords: forecasting, prediction, prediction markets, beliefs, agent-based model, climate change

Suggested Citation

Nay, John and Van der Linden, Martin and Gilligan, Jonathan M., Betting and Belief: Prediction Markets and Attribution of Climate Change (July 11, 2016). Nay, J. J., Van der Linden, M., Gilligan, J. (2016). “Betting and Belief: Prediction Markets and Attribution of Climate Change.” Proceedings of the 2016 Winter Simulation Conference, 1666–1677. IEEE Press., Available at SSRN: https://ssrn.com/abstract=2808284 or http://dx.doi.org/10.2139/ssrn.2808284

John Nay (Contact Author)

New York University (NYU) ( email )

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Skopos Labs, Inc.

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Martin Van der Linden

Emory University ( email )

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Jonathan M. Gilligan

Vanderbilt University - Department of Earth and Environmental Sciences ( email )

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Department of Civil and Environmental Engineering ( email )

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Vanderbilt Institute for Energy and Environment ( email )

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