The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".