The “analysis of competing hypotheses” in intelligence analysis
Bibliographic record
Abstract
Summary The intelligence community uses “structured analytic techniques” to help analysts think critically and avoid cognitive bias. However, little evidence exists of how techniques are applied and whether they are effective. We examined the use of the analysis of competing hypotheses (ACH)—a technique designed to reduce “confirmation bias.” Fifty intelligence analysts were randomly assigned to use ACH or not when completing a hypothesis testing task that had probabilistic ground truth. Data on analysts' judgement processes and conclusions were collected using written protocols that were then coded for statistical analyses. We found that ACH‐trained analysts did not follow all of the steps of ACH. There was mixed evidence for ACH's ability to reduce confirmation bias, and we observed that ACH may increase judgement inconsistency and error. It may be prudent for the intelligence community to consider the conditions under which ACH would prove useful and to explore alternatives.
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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 | 0.449 | 0.698 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".