Techniques to model uncertain input data of multi‐criteria decision‐making problems: a literature review
Bibliographic record
Abstract
Abstract There are a few studies in the literature regarding possible types of uncertainty in input data of multi‐criteria decision making (MCDM) or multi‐criteria decision analysis (MCDA) problems and the techniques employed to deal with each of them. Therefore, the aim of this study is to identify the different types of uncertainty that occur in input data of MCDM/MCDA problems and the most appropriate techniques to deal with each one of these uncertainty types. In this paper, a comprehensive literature review is presented in order to meet this objective. We selected and summarized 134 international journal articles. They were analyzed based on the type of data with uncertainty, the type of uncertainty, and the technique used to model it. We identified three distinct types of uncertainty in input data of MCDM/MCDA problems, namely (i) uncertainty due to ambiguity, (ii) uncertainty due to randomness, and (iii) uncertainty due to partial information. We identified a new generation of fuzzy approaches including Type‐2, intuitionistic, and hesitant fuzzy sets (FSs), which are used to model these types of uncertainty alongside other approaches such as traditional FSs theory, probability theory, evidential reasoning theory, rough set theory, and grey numbers. Finally, a framework that indicates techniques used in different decision‐making contexts under uncertainty is proposed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".