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Aggregation and consensus for preference relations based on fuzzy partial orders

Beliakov, Gleb, James, Simon and Wilkin, Tim 2016, Aggregation and consensus for preference relations based on fuzzy partial orders, Fuzzy optimization and decision making, In Press, pp. 1-20, doi: 10.1007/s10700-016-9258-4.

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Title Aggregation and consensus for preference relations based on fuzzy partial orders
Author(s) Beliakov, GlebORCID iD for Beliakov, Gleb orcid.org/0000-0002-9841-5292
James, SimonORCID iD for James, Simon orcid.org/0000-0003-1150-0628
Wilkin, TimORCID iD for Wilkin, Tim orcid.org/0000-0003-4059-1354
Journal name Fuzzy optimization and decision making
Season In Press
Start page 1
End page 20
Total pages 20
Publisher Springer
Place of publication Berlin, Germany
Publication date 2016-11-23
ISSN 1568-4539
1573-2908
Keyword(s) pairwise preference relations
aggregation functions
Kemeny distance
fuzzy partial order
group decision making
linear programming
Summary We propose a framework for eliciting and aggregating pairwise preference relations based on the assumption of an underlying fuzzy partial order. We also propose some linear programming optimization methods for ensuring consistency either as part of the aggregation phase or as a pre- or post-processing task. We contend that this framework of pairwise-preference relations, based on the Kemeny distance, can be less sensitive to extreme or biased opinions and is also less complex to elicit from experts. We provide some examples and outline their relevant properties and associated concepts.
Language eng
DOI 10.1007/s10700-016-9258-4
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
080202 Applied Discrete Mathematics
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090141

Document type: Journal Article
Collection: School of Information Technology
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