Too soon to tell if the US intelligence community prediction market is more accurate than intelligence reports: Commentary on
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
Abstract Stastny and Lehner (2018) reported a study comparing the forecast accuracy of a US intelligence community prediction market (ICPM) to traditionally produced intelligence reports. Five analysts unaffiliated with the intelligence reports imputed forecasts from the reports after stating their personal forecasts on the same forecasting questions. The authors claimed that the accuracy of the ICPM was significantly greater than that of the intelligence reports and suggest this may have been due to methods that harness crowd wisdom. However, additional analyses conducted here show that the imputer’s personal forecasts, which were made individually, were as accurate as ICPM forecasts. In fact, their updated personal forecasts (made after reading the intelligence reports) were marginally more accurate than ICPM forecasts. Imputed forecasts are also strongly correlated with the imputers’ personal forecasts, casting doubt on the degree to which the imputation was in fact a reliably inter-subjective assessment of what intelligence reports implied about the forecasting questions. Alternative methods for comparing intelligence community forecasting methods are discussed.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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