106. Aggregation of pairwise comparison matrices: A clustering approach
Invited abstract in session FB-2: Decision theory, stream Decision theory.
Friday, 10:15 - 11:45Room: C 103
Authors (first author is the speaker)
| 1. | Kolos Ágoston
|
| Operations Research and Actuarial Sciences, Corvinus University of Budapest | |
| 2. | Sándor Bozóki
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| Research Group of Operations Research and Decision Systems, Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI) | |
| 3. | Laszlo Csato
|
| Institute for Computer Science and Control (SZTAKI) and Corvinus University of Budapest |
Abstract
We consider clustering in group decision making where the opinions are given by pairwise comparison matrices. In particular, the k-medoids model is suggested to classify the matrices as it has a linear programming problem formulation. Its objective function depends on the measure of dissimilarity between the matrices but not on the weights derived from them. With one cluster, our methodology provides an alternative to the conventional aggregation procedures. It can also be used to quantify the reliability of the aggregation. The proposed theoretical framework is applied to a large-scale experimental dataset, on which it is able to automatically detect some mistakes made by the decision-makers.
Keywords
- Optimization for learning and data analysis
Status: accepted
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