Title :
Multiattribute Choice With Ordinal Information: A Comparison of Different Decision Rules
Author :
Sarabando, Paula ; Dias, Luís Cândido
Author_Institution :
Escola Super. de Tecnol. de Viseu, Viseu
fDate :
5/1/2009 12:00:00 AM
Abstract :
In the context of additive multiattribute aggregation, we address problems with ordinal information, i.e., considering a ranking of the weights (the scaling coefficients). Several rules for ranking alternatives in these situations have been proposed and compared, such as the rank-order-centroid weight, minimum value, central value, and maximum regret rules. This paper compares these rules, together with two rules that had never been studied (quasi-dominance and quasi-optimality) that use a tolerance parameter to extend the concepts of dominance and optimality. Another contribution of this paper is the study of the behavior of these rules in the context of selecting a subset of the most promising alternatives. This study intends to provide guidelines about which rules to choose and how to use them (e.g., how many alternatives to retain and what tolerance to use), considering the contradictory goals of keeping a low number of alternatives yet not excluding the best one. The comparisons are grounded on Monte Carlo simulations.
Keywords :
Monte Carlo methods; optimisation; utility theory; Monte Carlo simulation; additive multiattribute aggregation; decision rules; maximum regret rule; multiattribute choice; ordinal information; rank-order-centroid weight; rule behavior; scaling coefficient; tolerance parameter; utility theory; weight ranking; Imprecise/incomplete/partial information; Multiattribute utility theory (MAUT)/multiattribute value theory (MAVT); multicriteria decision analysis; ordinal information; simulation;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/TSMCA.2009.2014555