TY - JOUR
T1 - Information, incentives, and goals in election forecasts
AU - Gelman, Andrew
AU - Hullman, Jessica
AU - Wlezien, Christopher
AU - Morris, George Elliott
N1 - Funding Information:
We thank Joshua Goldstein, Merlin Heidemanns, Dhruv Madeka, Yair Ghitza, Annie Liang, Doug Rivers, Bob Erikson, Bob Shapiro, Jon Baron, and the anonymous reviewers for helpful comments, and the National Science Foundation, Institute of Education Sciences, Office of Naval Research, National Institutes of Health, Sloan Foundation, and Schmidt Futures for financial support. Copyright: © 2020. The authors license this article under the terms of the Creative Commons Attribution 3.0 License. ∗Department of Statistics and Department of Political Science, Columbia University, New York. Email: gelman@stat.columbia.edu. †Department of Computer Science & Engineering and Medill School of Journalism, Northwestern University. ‡Department of Government, University of Texas at Austin. §The Economist.
Publisher Copyright:
© 2020 The authors.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and communication. We highlight how incentives may shape forecasts, and particularly forecast uncertainty, in light of calibration challenges. We illustrate these challenges in creating, communicating, and evaluating election predictions, using the Economist and Fivethirtyeight forecasts of the 2020 election as examples, and offer recommendations for forecasters and scholars.
AB - Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and communication. We highlight how incentives may shape forecasts, and particularly forecast uncertainty, in light of calibration challenges. We illustrate these challenges in creating, communicating, and evaluating election predictions, using the Economist and Fivethirtyeight forecasts of the 2020 election as examples, and offer recommendations for forecasters and scholars.
KW - Elections
KW - Forecasting
KW - Polls
KW - Probability
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M3 - Article
AN - SCOPUS:85098813676
VL - 15
SP - 863
EP - 880
JO - Judgment and Decision Making
JF - Judgment and Decision Making
SN - 1930-2975
IS - 5
ER -