Elicitation of Probabilities Using Competitive Scoring Rules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several forecasters predict the probability of an event, and then make or receive payments contingent on their predictions and on whether the event actually occurs. The payment functions generalize the concept of scoring rule to a competitive setting. We allow for exogenously determined subsidies to each forecaster, and require that the scheme be anonymous, neutral, and truth-inducing. By centering each forecaster's payment at the average payment to all other forecasters, we construct competitive scoring rules that reward the better predictors. Applications include multiparty betting and fixed-budget surveys to determine subjects' truthful probability assessments. We discuss when forecasters would voluntarily participate in such a competition, and relate our results to the scoring rules proposed by De Finetti (1974) for eliciting probabilities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it