Literature DB >> 25987508

Identifying and cultivating superforecasters as a method of improving probabilistic predictions.

Barbara Mellers1, Eric Stone2, Terry Murray3, Angela Minster4, Nick Rohrbaugh2, Michael Bishop2, Eva Chen2, Joshua Baker2, Yuan Hou2, Michael Horowitz2, Lyle Ungar2, Philip Tetlock2.   

Abstract

Across a wide range of tasks, research has shown that people make poor probabilistic predictions of future events. Recently, the U.S. Intelligence Community sponsored a series of forecasting tournaments designed to explore the best strategies for generating accurate subjective probability estimates of geopolitical events. In this article, we describe the winning strategy: culling off top performers each year and assigning them into elite teams of superforecasters. Defying expectations of regression toward the mean 2 years in a row, superforecasters maintained high accuracy across hundreds of questions and a wide array of topics. We find support for four mutually reinforcing explanations of superforecaster performance: (a) cognitive abilities and styles, (b) task-specific skills, (c) motivation and commitment, and (d) enriched environments. These findings suggest that superforecasters are partly discovered and partly created-and that the high-performance incentives of tournaments highlight aspects of human judgment that would not come to light in laboratory paradigms focused on typical performance.
© The Author(s) 2015.

Entities:  

Keywords:  expertise; forecasts; predictions; probability training; teams

Mesh:

Year:  2015        PMID: 25987508     DOI: 10.1177/1745691615577794

Source DB:  PubMed          Journal:  Perspect Psychol Sci        ISSN: 1745-6916


  11 in total

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6.  Pooling decisions decreases variation in response bias and accuracy.

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7.  Effects of Choice Restriction on Accuracy and User Experience in an Internet-Based Geopolitical Forecasting Task.

Authors:  Colin L Widmer; Amy Summerville; Ion Juvina; Brandon S Minnery
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Journal:  Sci Adv       Date:  2019-11-20       Impact factor: 14.136

10.  Quantifying machine influence over human forecasters.

Authors:  Andrés Abeliuk; Daniel M Benjamin; Fred Morstatter; Aram Galstyan
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