An application of fuzzy set theory and interval-based distances for expert judgment aggregation

Judgment aggregation is essential whenever multiple experts provide subjective evaluations that must be combined into a single coherent decision. This work adopts the perspective of a meta- expert who forms its final judgment after consulting a group of experts, giving greater weight to opinions closer to its own. Expert assessments are modelled using trapezoidal fuzzy numbers for both criteria weights and ratings, while the aggregation process relies on t-norms and t-conorms to combine fuzzy information coherently. The influence of each expert is determined through a recently introduced distance measure for trapezoidal fuzzy numbers, suitably rescaled to quantify the divergence between the meta-expert’s confidence index and those of the others. The proposed methodology is applied to a classical benchmark concerning the prediction of pressure increase in the containment building of the Sequoyah nuclear power plant, and the results are compared with those obtained through existing approaches.