Too soon to tell if the US intelligence community prediction market is more accurate than intelligence reports: Commentary on
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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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 | 0.064 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.044 | 0.070 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".