A combined fuzzy aggregation and consensus process for Multi-Criteria Group Decision Making problems
Bibliographic record
Abstract
In Multi-Criteria Group Decision Making (MCGDM) problems, aggregation and consensus methods are two key elements in reaching an overall collective value representing the group of experts making the decision. In many instances, the assessment process is based on linguistic terms rather than numerical values. Therefore, fuzzy aggregation and fuzzy consensus methods are more suitable for dealing with the linguistic terms used to reach the final decision. First, we present fuzzy set theory, fuzzy aggregation, and fuzzy consensus. Then, we describe a process for integrating fuzzy aggregation and fuzzy consensus in group decision-making problems. This process considers the aggregation of multiple criteria used for evaluation as well as the degree of consensus between the experts. Finally, we present a hypothetical case study to implement the developed process in MCGDM related to contractor selection. This paper contributes to the body of knowledge by developing a process that applies fuzzy aggregation and fuzzy consensus in solving MCGDM problems in construction. Furthermore, through the application of fuzzy set theory in aggregation and consensus, the developed process assists decision makers in problems that encompass subjectivity and uncertainty in their assessment.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".