Information fusion measures of effectiveness (MOE) for decision support
Bibliographic record
Abstract
For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.
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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 | 0.052 | 0.193 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".