Clustering Decision Makers with respect to similarity of views

Edward Abel, Ludmil Mikhailov, John Keane

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Within a large group of decision makers, varying amounts of both conflicting and harmonious views will be prevalent within the group, but obscured due to group size. When the number of Decision Makers is large, utilizing clustering during the process of aggregation of their views should aid both knowledge discovery - about the group's conflict and consensus - as well as helping to streamline the aggregation process to reach a group consensus. We conjecture that this can be realized by using the similarity of views of a large group of decision makers to define clusters of analogous opinions. From each cluster of decision makers, a representation of the views of its members can then be sought. This set of representations can then be utilized for aggregation to help reach a final whole group consensus.

Original languageEnglish
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - MCDM 2014: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Proceedings
PublisherIEEE
Pages40-47
Number of pages8
ISBN (Electronic)9781479944682
DOIs
Publication statusPublished - 12 Jan 2015
Event2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM 2014 - Orlando, United States
Duration: 9 Dec 201412 Dec 2014

Conference

Conference2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM 2014
Country/TerritoryUnited States
CityOrlando
Period9/12/1412/12/14

Keywords

  • Clustering
  • Genetic algorithms
  • Inconsistency
  • Multi-criteria decision making
  • Multi-objective optimization
  • Pairwise comparison

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