Imprecise Swing Weighting for Multi-Attribute Utility Elicitation Based on Partial Preferences

Matthias C. M. Troffaes, Ullrika Sahlin
Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 62:333-345, 2017.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v62-troffaes17b, title = {Imprecise Swing Weighting for Multi-Attribute Utility Elicitation Based on Partial Preferences}, author = {Troffaes, Matthias C. M. and Sahlin, Ullrika}, booktitle = {Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications}, pages = {333--345}, year = {2017}, editor = {Antonucci, Alessandro and Corani, Giorgio and Couso, Inés and Destercke, Sébastien}, volume = {62}, series = {Proceedings of Machine Learning Research}, month = {10--14 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v62/troffaes17b/troffaes17b.pdf}, url = {https://proceedings.mlr.press/v62/troffaes17b.html}, 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.} }
Endnote
%0 Conference Paper %T Imprecise Swing Weighting for Multi-Attribute Utility Elicitation Based on Partial Preferences %A Matthias C. M. Troffaes %A Ullrika Sahlin %B Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2017 %E Alessandro Antonucci %E Giorgio Corani %E Inés Couso %E Sébastien Destercke %F pmlr-v62-troffaes17b %I PMLR %P 333--345 %U https://proceedings.mlr.press/v62/troffaes17b.html %V 62 %X 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.
APA
Troffaes, M.C.M. & Sahlin, U.. (2017). Imprecise Swing Weighting for Multi-Attribute Utility Elicitation Based on Partial Preferences. Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 62:333-345 Available from https://proceedings.mlr.press/v62/troffaes17b.html.

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