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Copyright © 2012 Mona Riabacke et al. Mona Riabacke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Comparatively few of the vast amounts of decision analytical methods suggested have been widely spread in actual practice. Some approaches have nevertheless been more successful in this respect than others. Quantitative decision making has moved from the study of decision theory founded on a single criterion towards decision support for more realistic decision-making situations with multiple, often conflicting, criteria. Furthermore, the identified gap between normative and descriptive theories seems to suggest a shift to more prescriptive approaches. However, when decision analysis applications are used to aid prescriptive decision-making processes, additional demands are put on these applications to adapt to the users and the context. In particular, the issue of weight elicitation is crucial. There are several techniques for deriving criteria weights from preference statements. This is a cognitively demanding task, subject to different biases, and the elicited values can be heavily dependent on the method of assessment. There have been a number of methods suggested for assessing criteria weights, but these methods have properties which impact their applicability in practice. This paper provides a survey of state-of-the-art weight elicitation methods in a prescriptive setting.

Details

Title
State-of-the-Art Prescriptive Criteria Weight Elicitation
Author
Riabacke, Mona; Danielson, Mats; Love Ekenberg
Publication year
2012
Publication date
2012
Publisher
Asia University, Taiwan
ISSN
20903359
e-ISSN
20903367
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1272300886
Copyright
Copyright © 2012 Mona Riabacke et al. Mona Riabacke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.