The Value of Precision in Probability Assessment: Evidence from a Large-Scale Geopolitical Forecasting Tournament

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Authors Jeffrey A. Friedman, Joshua D. Baker, Barbara A. Mellers, Philip Tetlock, Richard J Zeckhauser
Journal/Conference Name INTERNATIONAL STUDIES QUARTERLY
Paper Category
Paper Abstract Scholars, practitioners, and pundits often leave their assessments of uncertainty vague when debating foreign policy, arguing that clearer probability estimates would provide arbitrary detail instead of useful insight. We provide the first systematic test of this claim using a data set containing 888,328 geopolitical forecasts. We find that coarsening numeric probability assessments in a manner consistent with common qualitative expressions – including expressions currently recommended for use by intelligence analysts – consistently sacrifices predictive accuracy. This finding does not depend on extreme probability estimates, short time horizons, particular scoring rules, or individual attributes that are difficult to cultivate. At a practical level, our analysis indicates that it would be possible to make foreign-policy discourse more informative by supplementing natural language-based descriptions of uncertainty with quantitative probability estimates. More broadly, our findings advance long-standing debates over the nature and limits of subjective judgment when assessing social phenomena, showing how explicit probability assessments are empirically justifiable even in domains as complex as world politics. Acknowledgments. Thanks to Pavel Atanasov, Michael Beckley, William Boettcher, David Budescu, Michael Cobb, Shrinidhi Kowshika Lakshmikanth, David Mandel, Angela Minster, Brendan Nyhan, Michael Poznansky, Jonah Schulhofer-Wohl, Sarah Stroup, Lyle Ungar, Thomas Wallsten and Justin Wolfers for valuable input on previous drafts. This work benefited from presentations at Dartmouth College, Middlebury College, the University of Pennsylvania, the University of Virginia, the 2015 meeting of the American Political Science Association, the 2015 ISSS-ISAC joint annual conference, and the 2015 National Bureau of Economic Research Summer Institute. Support through ANR Labex IAST is gratefully acknowledged. This research was supported by the Intelligence Advanced Research Projects Activity (IARPA) via the Department of Interior National Business Center (DoI/NBC) Contract No. D11PC20061. The views and conclusions expressed herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. government.
Date of publication 2018
Code Programming Language R
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