Applying Information Theory to Validate Commanders’ Critical Information Requirements
Bibliographic record
Abstract
The primary aim of this chapter is to introduce a novel approach to strengthen contemporary intelligence community practices for establishing intelligence collection priorities based on expected information value. We propose the integration of quantitative measures of information utility that have been discussed in the literature on information theory (Lindley, 1956; Nelson, 2005; Crupi & Tentori, 2014) as a method for optimizing intelligence collection planning. We argue that enhancing the effectiveness through which command information requirements are established can improve consequent intelligence collection priorities. We contrast this approach with the structured analytic technique (SAT) approach that is currently described as a method for prioritizing information requirements in intelligence collection. Specifically, we proceed with a review of the Indicators Validator™ (IV) SAT (Heuer & Pherson, 2008) for establishing information value, illustrating how it works, and where it falls short as an analytic method. Next, we introduce a quantitative information-theoretic measure of information utility called information gain (Lindley, 1956). We illustrate the contrast between these approaches using a practical example featuring a hypothetical North Atlantic Treaty Organization (NATO) dilemma. This analysis shows how information gain overcomes many limitations of the IV technique, along with how it might be applied to modern NATO operational practice.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".