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Utilizing Surrogate Numbers for Probability Elicitation
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2018 (English)In: Decision Making / [ed] Fausto Pedro García Márquez, Alberto Pliego Marugán, Mayorkinos Papaelias, IntechOpen , 2018, p. 39-56Chapter in book (Other academic)
Abstract [en]

Comparatively few of the vast number of suggested decision-analytical methods have been widely spread in actual practice. The majority of those methods call for exact and accurate numbers as input, which could be one of several reasons for this lack of actual use; people frequently seem to be unfamiliar with, or reluctant to express those, in a sense, “true” values required. Many alternative methods to resolve this complication have been suggested over the years, including procedures for dealing with incomplete information. One way, which has proliferated for a while, is to introduce so-called surrogate numbers in the form of ordinal ranking methods for multi-criteria weights. In this chapter, we show how those can be adapted for use in probability elicitation. Furthermore, when decision-makers possess more information regarding the relative strengths of probabilities, that is, some form of cardinality, the input information to ordinal methods is sometimes too restricted. Therefore, we suggest a testing methodology and analyze the relevance of a set of cardinal ordering methods in addition to the ordinal ones.

Place, publisher, year, edition, pages
IntechOpen , 2018. p. 39-56
Keywords [en]
decision analysis, probability elicitation, cardinal ranking, rank order, imprecise probability
National Category
Other Computer and Information Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-163229DOI: 10.5772/intechopen.76422ISBN: 978-1-78923-795-5 (print)ISBN: 978-1-78923-796-2 (electronic)OAI: oai:DiVA.org:su-163229DiVA, id: diva2:1271980
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2018-12-21Bibliographically approved

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CiteExportLink to record
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