The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability

Accurate predictions about the future are crucial for optimizing policy choices, but finding skilled forecasters is challenging. Large forecasting tournaments can identify generalist superforecasters who make reliably accurate predictions across domains, but such tournaments take months to complete and do little to explain why these forecasters perform at a consistently high level. We solve these problems by developing a one-hour Forecasting Proficiency Test (FPT) that explains 75% of the variance in forecasters' accuracy about a wide variety of events and provides new evidence about the mechanisms that make some people better forecasters than others. Specialized cognitive abilities, including base rate sensitivity and Bayesian reasoning, uniquely predict forecasters' accuracy even when controlling for more traditional measures of general intelligence. The FPT also includes carefully chosen real-world forecasting questions that produce interpretable and exchangeable test scores. The top 20% of testees made forecasts with accuracy comparable to previously identified superforecasters; and two-thirds of superforecasters had FPT scores above the 75th percentile. The creation of an effective test of forecasting proficiency expands our understanding of human intelligence and provides an important tool for decision-makers who rely on accurate probabilistic predictions to make high-stakes decisions that impact the lives of millions.

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Published
2025-07-18
DOI
https://doi.org/10.31234/osf.io/a7kdx_v6
Primary Topic
Forecasting Techniques and Applications
Type
article
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article

The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability

Jessica Helmer, Phil Tetlock, Nikolay Angelov, A. I. Bennett et al.
Forecasting Techniques and Applications
article

The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability

Jessica Helmer, Phil Tetlock, Nikolay Angelov, A. I. Bennett, Ezra Karger, Sophie Ma Zhu, Mark Himmelstein, Sivan Livnat, Page Hedley
article en

Abstract

Accurate predictions about the future are crucial for optimizing policy choices, but finding skilled forecasters is challenging. Large forecasting tournaments can identify generalist superforecasters who make reliably accurate predictions across domains, but such tournaments take months to complete and do little to explain why these forecasters perform at a consistently high level. We solve these problems by developing a one-hour Forecasting Proficiency Test (FPT) that explains 75% of the variance in forecasters' accuracy about a wide variety of events and provides new evidence about the mechanisms that make some people better forecasters than others. Specialized cognitive abilities, including base rate sensitivity and Bayesian reasoning, uniquely predict forecasters' accuracy even when controlling for more traditional measures of general intelligence. The FPT also includes carefully chosen real-world forecasting questions that produce interpretable and exchangeable test scores. The top 20% of testees made forecasts with accuracy comparable to previously identified superforecasters; and two-thirds of superforecasters had FPT scores above the 75th percentile. The creation of an effective test of forecasting proficiency expands our understanding of human intelligence and provides an important tool for decision-makers who rely on accurate probabilistic predictions to make high-stakes decisions that impact the lives of millions.

Georgia Institute of Technology (US), University of British Columbia (CA)
Peace, Justice and strong institutions
Openalex Percentile: Top 77%
Forecasting Techniques and Applications
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The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability — Jessica Helmer, Phil Tetlock, et al. · (2025) | TGRS Research Map | TGRS