Uncertainty, Intelligence, and National Security Decision Making
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
Uncertainty is both inherent in nature and endemic to national security decision-making. Intelligence communities throughout the Western world, however, rely on vague language to communicate uncertainty—both the probability of critical events and the confidence that analysts have in their assessments—to decision-makers. In this article, we review the status-quo approach taken by the intelligence community and, drawing on abundant research findings, we describe fundamental limitations with the approach, including the inherent vagueness, context-dependence, and implicit meanings that attend the use of verbal uncertainty expressions. We then present an alternative approach based on the use of imprecise numeric estimates supplemented by clear written rationales, highlighting the affordances of this alternative. Finally, we describe institutional barriers to reform and address common objections raised by practitioners. While we focus our discussion on the domain of national security intelligence, the case for numeric probabilities is relevant to any organizational field where high-stakes decisions are made under conditions of uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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