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Imprecise swing weighting for multi-attribute utility elicitation based on partial preferences

Troffaes, Matthias C.M.; Sahlin, Ullrika

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Authors

Ullrika Sahlin



Contributors

Alessandro Antonucci
Editor

Giorgio Corani
Editor

Inés Couso
Editor

Sébastien Destercke
Editor

Abstract

We describe a novel approach to multi-attribute utility elicitation which is both general enough to cover a wide range of problems, whilst at the same time simple enough to admit reasonably straightforward calculations. We allow both utilities and probabilities to be only partially specified, through bounding. We still assume marginal utilities to be precise. We derive necessary and sufficient conditions under which our elicitation procedure is consistent. As a special case, we obtain an imprecise generalization of the well known swing weighting method for eliciting multi-attribute utility functions. An example from ecological risk assessment demonstrates our method.

Citation

Troffaes, M. C., & Sahlin, U. (2017). Imprecise swing weighting for multi-attribute utility elicitation based on partial preferences. In A. Antonucci, G. Corani, I. Couso, & S. Destercke (Eds.), Proceedings of the Tenth International Symposium on Imprecise Probability : Theories and Applications, 10-14 July 2017, Lugano (Switzerland) (333-345)

Conference Name The Tenth International Symposium on Imprecise Probability: Theories and Applications (ISIPTA ’17)
Conference Location Lugano, Switzerland
Start Date Jul 10, 2017
End Date Jul 14, 2017
Acceptance Date Apr 17, 2017
Online Publication Date Jun 20, 2017
Publication Date Jun 20, 2017
Deposit Date Mar 9, 2017
Publicly Available Date Mar 29, 2024
Publisher PMLR
Pages 333-345
Series Title Proceedings of Machine Learning Research
Series Number 62
Series ISSN 1938-7228
Book Title Proceedings of the Tenth International Symposium on Imprecise Probability : Theories and Applications, 10-14 July 2017, Lugano (Switzerland).
Public URL https://durham-repository.worktribe.com/output/1149037
Publisher URL http://proceedings.mlr.press/v62/troffaes17b.html

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