An Automated Explanation Approach for a Decision Support System based on MCDA.
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
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).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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