NDLI: Aggregation of group members' opinions based on two principles

Content Provider	IEEE Xplore Digital Library
Author	Entani, T. Inuiguchi, M.
Copyright Year	2010
Description	Author affiliation: Department of Economics and Social Sciences, Kochi University, 2-5-1 Akebono, Kochi, 780-0915 Japan (Entani, T.) \|\| Department of Systems Innovation, Osaka University, 1-3 Machikaneyama Toyonaka Osaka 560-8531, Japan (Inuiguchi, M.)
Abstract	In this paper, group decision making is discussed from the view of aggregating the members' opinions. At first, the individual preferences are obtained by Interval Analytic Hierarchy Process (Interval AHP), which can handle subjective judgments and reflect uncertainty of information. The interval priority weights of alternatives are induced from the pairwise comparison matrix given by a decision maker easily. They help decision makers to reveal the differences and similarities between their own opinions and others concretely, and encourage them to reduce their communication barriers. Then, the individually obtained preferences are aggregated so as to reach the final group decision. The aggregation models from two viewpoints, such as decision makers and alternatives, are proposed. The former model emphasizes the members' consensus by reducing their dissatisfaction for the aggregation. The latter model emphasizes estimating the plausible values of alternatives by taking the essential part of many decision makers' opinions on each alternative.
Starting Page	1
Ending Page	6
File Size	141872
Page Count	6
File Format	PDF
ISBN	9781424469192
ISSN	10987584
e-ISBN	9781424469215
DOI	10.1109/FUZZY.2010.5584710
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Decision making Uncertainty Focusing Aggregates Numerical models Biological system modeling
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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