Illustration of a Methodology to Characterize Some Preference Uncertainties in Multicriteria Decision Analysis
Bibliographic record
Abstract
Abstract Any multicriteria decision analysis (MCDA) method should make sense to its users and be practical in helping make decisions. Using selected civil and environmental engineering examples in life cycle sustainability assessment and in prioritizing environmental assessment and remediation, the presented methodology strives to meet this objective by emphasizing the importance of visualizing some of the preference uncertainties associated with choice and ranking decision analyses. The emphasis on visualization should compel stakeholders to discuss MCDA results and deliberate conclusions, which is a need in any MCDA practice. The methodology employs simple yet well-founded equations derived from partial-order theory for uncertainty visualization. These equations can be easily programmed using a spreadsheet software or used via a publicly available software highlighted in the article. In addition to its simplicity, the methodology avoids the requirement to quantify stakeholder preferences or to aggregate attribute scores in all situations. As shown through the examples, reasonable decisions can often be made by visualizing the information as it is.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".