Improving Probability Judgment in Intelligence Analysis: From Structured Analysis to Statistical Aggregation
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
As in other areas of expert judgment, intelligence analysis often requires judging the probability that hypotheses are true. Intelligence organizations promote the use of structured methods such as “Analysis of Competing Hypotheses” (ACH) to improve judgment accuracy and analytic rigor, but these methods have received little empirical testing. In this experiment, we pitted ACHagainst a factorized Bayes theorem (FBT) method, and we examined the value of recalibration (coherentization) and aggregation methods for improving the accuracy of probability judgment. Analytic techniques such as ACH and FBT were ineffective in improving accuracy and handling correlated evidence, and ACH in fact decreased the coherence of probability judgments. In contrast, statistical post-analytic methods (i.e., coherentization and aggregation) yielded large accuracy gains. A wide range of methods for instantiating these techniques were tested. The interactions among the factors considered suggest that prescriptive theorists and interventionists should examine the value of ensembles of judgment-support methods.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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