Flexibility Degree of Fuzzy Numbers and its Implication to a Group-Decision-Making Model
Bibliographic record
Abstract
The theory of fuzzy sets considers that everything exhibits some elasticity and is a matter of degree. When fuzzy numbers are used to evaluate the judgements of decision makers (DMs) in pairwise comparisons of alternatives following the analytic hierarchy process, the flexibility experienced by DMs has been exhibited. In order to capture this aspect of flexibility, it is important to know how to realize the flexibility degree of fuzzy numbers and further present a method of realizing its quantification. In this paper, a definition of the flexibility degree of fuzzy numbers is proposed. Some formulas are proposed to quantify the flexibility and rigidity degrees of interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers. A group decision making (GDM) model is developed under the consideration of the flexibility of DMs. By considering the effects of the applied scale and the reciprocal relation, the flexibility degree of interval multiplicative reciprocal comparison matrices is further defined, which is used to evaluate the flexibility degree of the DM involved in the decision process. An RD-IOWGA operator is proposed to aggregate individual interval multiplicative reciprocal matrices by associating more importance to that with less flexibility. A new algorithm is shown to solve GDM problems with interval multiplicative reciprocal preference relations. Numerical studies are carried out to illustrate the new definitions and offer some comparative analysis. The observations reveal that the developed consensus method can be used to model the GDM with a dominant position.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".