The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability
Bibliographic record
Abstract
How can we identify accurate forecasters? Current gold standard approaches draw from two sources of information: simple cognitive assessments of reasoning ability and past forecasting accuracy. Cognitive assessments are well-studied and normed psychometric instruments, but have lower predictive value. Real-world forecasting problems are more challenging to norm a priori: they typically require a large comparison sample of forecasters to make predictive inferences. This paper develops the Forecasting Proficiency Test (FPT): a one-hour test that predicts more than 60% of the variation in out-of-sample forecasting accuracy, double the predictive utility of prior work. We highlight several key results: (1) Eliciting forecasts in a quantile format is considerably more psychometrically reliable, meaning it is more efficient at reducing measurement error about forecasters' expected accuracy with fewer items, than forecasts elicited using the standard probability format common to most judgmental forecasting tournaments. (2) Several cognitive tasks, novel to forecasting research, are highly predictive of forecasting ability. These include denominator neglect, Bayesian updating, and adherence to decision rules. (3) Although our methods do best at separating out the worst forecasters from the population, they are also remarkably effective at identifying elite talent in the upper range of the skill distribution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".