Comparing Naval Decision Support Technologies Using Decision Models, Process Tracing and Error Analysis
Bibliographic record
Abstract
Providing decision support to operators in command and control contexts requires careful assessment of its impacts on task performance. Here we describe a human-in-the-loop experiment using a naval air defense testbed to compare three conditions: 1) a baseline interface (DSSBASE; 2) one that displays the temporal proximity of radar aircraft (DSSTEMP); and 3) one which adds a change history panel (DSSCHEX). Threatevaluation accuracy and cognitive models of participants’ judgments did not significantly differ across conditions. Eye-tracking data showed that DSSTEMP lead to a reduced verification of track attributes. Confusion matrices also differed across conditions: DSSTEMP lead to more errors that are “two-categoriesaway” when erroneously classifying hostile aircraft as non-hostile or uncertain. We conclude that in addition to looking at standard metrics of task accuracy and response times, critical insights can be obtained by assessing how different designs for decision support may alter strategies and cognitive processes.
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".