Clustering Decision Makers with respect to similarity of views

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 kno...

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Bibliographic Details
Published in:2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) pp. 40 - 47
Main Authors: Abel, Edward, Mikhailov, Ludmil, Keane, John
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2014
Subjects:
Aggregates
Clustering
Clustering algorithms
Educational institutions
Genetic algorithms
Inconsistency
Multi-criteria decision making
Multi-objective optimization
Optimization
Pairwise comparison
Phase change materials
Vectors
Weight measurement
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Summary: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.
DOI:10.1109/MCDM.2014.7007186