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Unifying approaches to consensus across different preference representations

Beliakov, Gleb and James, Simon 2015, Unifying approaches to consensus across different preference representations, Applied soft computing journal, vol. 35, pp. 888-897.

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Title Unifying approaches to consensus across different preference representations
Author(s) Beliakov, Gleb
James, Simon
Journal name Applied soft computing journal
Volume number 35
Start page 888
End page 897
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-10-01
ISSN 1568-4946
Keyword(s) Consensus measures
Decision making
Distance metric
Kemeny distance
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Summary © 2015 Elsevier B.V. All rights reserved. Consensus measures can be useful in group decision making problems both to guide users toward more reasonable judgments and to give an overall indication of the support for the final decision. The level of consensus between decision makers can be measured in contexts where preferences over alternatives are expressed either as evaluations or scores, pairwise preferences, and weak orders, however these different representations often call for different approaches to consensus measurements. In this paper, we look at the distance metrics used to construct consensus measures in each of these settings and how consistent these are for preference profiles when they are converted from one representation to another. We develop some methods for consistent approaches across decision making settings and provide an example to help investigate differences between some of the commonly used distances.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077921

Document type: Journal Article
Collection: School of Information Technology
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