A spectrum of compromise aggregation operators for multi-attribute decision making.
OA Location
Author(s)
Luo, X
Jennings, NR
Type
Journal Article
Abstract
In many decision making problems, a number of independent attributes or criteria are often used to individually rate an alternative from an agent?s local perspective and then these individual ratings are combined to produce an overall assessment. Now, in cases where these individual ratings are not in complete agreement, the overall rating should be somewhere in between the extremes that have been suggested. However, there are many possibilities for the aggregated value. Given this, this paper systematically explores the space of possible compromise operators for such multi-attribute decision making problems. Specifically, we axiomatically identify the complete spectrum of such operators in terms of the properties they should satisfy, and show the main ones that are widely used–namely averaging operators, uninorms and nullnorms–represent only three of the nine types we identify. For each type, we then go onto analyse their properties and discuss how specific instances can actually be developed. Finally, to illustrate the richness of our framework, we show how a wide range of operators are needed to model the various attitudes that a user may have for aggregation in a given scenario (bidding in multi-attribute auctions).
Date Issued
2007
Citation
Artificial Intelligence Journal, 2007, 171, pp.161-184
Start Page
161
End Page
184
Journal / Book Title
Artificial Intelligence Journal
Volume
171
Identifier
http://eprints.soton.ac.uk/264222/
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
aggregation operator
uninorm
nullnorm risk
multi-attribute decision making
multi-attribute auction
COMPENSATORY OPERATORS
NEGOTIATING AGENTS
TRIANGULAR NORMS
FUZZY-SET
SYSTEMS
INFORMATION
UNINORMS
MODEL
CONNECTIVES
PREFERENCES
Artificial Intelligence & Image Processing
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Article Number
2-3