Psychometric Measurement of Forecasters Using the Wisdom of Crowds
Bibliographic record
Abstract
Forecasting skill is often measured by the average error between forecasters’ predictions and the ground truth, but the inherent uncertainty in event outcomes adds an additional layer of measurement error onto skill estimation. Intersubjective measures offer an alternative approach to skill measurement: comparing forecasters’ predictions to those of their peers, typically to aggregated forecasts from groups of peers. A key advantage of this approach is that forecasts can be scored in real time, without having to wait for the ground truth outcome to be realized. However, it has another more subtle advantage as well: aggregate predictions can be less noisy than ground truth outcomes, leading to a potential reduction in this additionalmeasurement error. In a simulation study, we demonstrate conditions under which crowd aggregates provide a more reliable indicator of the optimal forecast for a given event than a single realized ground truth outcome, leading to skill measurement with reduced measurement error. We also demonstrate the effectiveness of intersubjective methods in a real-world forecasting study in which 894 participants made both forecasts and metapredictions, i.e., predictions of what others might forecast. As in our simulation, intersubjective measures capture forecasting ability more efficiently than do ground-truth scores, demonstrating their usefulness for identifying high-performing forecasters.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".