A generalized approach for multi-criteria decision aid methods
Bibliographic record
Abstract
Abstract Outranking methods were developed for solving Multi-Criteria Decision-Making (MCDM) problems with a finite set of alternatives. In this paper, we propose a comprehensive framework that extends the application of outranking methods for solving MCDM problems with continuous sets of alternatives. This is achieved through the integration of multi-objective optimization methods and outranking methods. The derived Three-phases integration procedure is illustrated by the integration of the Weighted Sum method for continuous MCDM problems and the outranking Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) method. Moreover, the proposed framework incorporates the use of the Principal Component Analysis (CPA) to address the problem of selecting a Pareto optimal solution when the phase of application of the outranking method generates a large set of ‘best’ Pareto optimal solutions. The application potential of the procedure is illustrated by a multi-dimensional financial portfolio selection problem.
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How this classification was reachedexpand
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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".