An Automated Explanation Approach for a Decision Support System based on MCDA.
Bibliographic record
Abstract
In a military context, the process of planning operations involves the assessment of the situation, the generation of Courses of Action (COAs), and their evaluation according to significant points of view, in order to select the course of action that represents the best possible compromise. Since several conflicting and quite incommensurable criteria need to be considered and balanced to make wise decisions, MultiCriterion Decision Aid (MCDA) has been used to develop decision support systems. Defence Research and Development Canada – Valcartier (DRDC Valcartier) has developed an advisor tool to assist the Air Operation Centre staff in managing events and their related COAs, as well as prioritizing these COAs according to different evaluation criteria by means of a MCDA procedure. Following this development, an investigation has been conducted to provide this decision support system with explanation facilities. This paper describes the suggested approach for the automated generation of explanations of a ranking proposed by a decision support system based on a MultiCriterion Aggregation Procedure (MCAP).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".