Clustering Decision Makers with respect to similarity of views

Edward Abel; Ludmil Mikhailov; John Keane

doi:10.1109/MCDM.2014.7007186

Clustering Decision Makers with respect to similarity of views

Edward Abel, Ludmil Mikhailov, John Keane

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - MCDM 2014: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Proceedings
Publisher	IEEE
Pages	40-47
Number of pages	8
ISBN (Electronic)	9781479944682
DOIs	https://doi.org/10.1109/MCDM.2014.7007186
Publication status	Published - 12 Jan 2015
Event	2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014

Conference

Conference	2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM 2014
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14

Keywords

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

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10.1109/MCDM.2014.7007186

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Abel, E., Mikhailov, L., & Keane, J. (2015). Clustering Decision Makers with respect to similarity of views. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - MCDM 2014: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Proceedings (pp. 40-47). Article 7007186 IEEE. https://doi.org/10.1109/MCDM.2014.7007186

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Abel, E, Mikhailov, L & Keane, J 2015, Clustering Decision Makers with respect to similarity of views. in IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - MCDM 2014: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Proceedings., 7007186, IEEE, pp. 40-47, 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM 2014, Orlando, United States, 9/12/14. https://doi.org/10.1109/MCDM.2014.7007186

Clustering Decision Makers with respect to similarity of views. / Abel, Edward; Mikhailov, Ludmil; Keane, John.
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - MCDM 2014: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Proceedings. IEEE, 2015. p. 40-47 7007186.

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Clustering Decision Makers with respect to similarity of views

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