Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model

Patrice Perny LIP6, Paolo Viappiani Lip6 Paris, abdellah Boukhatem LIP6
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:534-543, 2016.

Abstract

This work concerns decision making under risk with the rank-dependent utility model (RDU), a generalization of expected utility providing enhanced descriptive possibilities. We introduce a new incremental decision procedure, involving monotone regression spline functions to model both components of RDU, namely the probability weighting function and the utility function. First, assuming the utility function is known, we propose an elicitation procedure that incrementally collects preference information in order to progressively specify the probability weighting function until the optimal choice can be identified. Then, we present two elicitation procedures for the construction of a utility function as a monotone spline. Finally, numerical tests are provided to show the practical efficiency of the proposed methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-lip616a, title = {Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model}, author = {LIP6, Patrice Perny and Paris, Paolo Viappiani Lip6 and LIP6, abdellah Boukhatem}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {534--543}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/lip616a/lip616a.pdf}, url = {https://proceedings.mlr.press/r14/lip616a.html}, abstract = {This work concerns decision making under risk with the rank-dependent utility model (RDU), a generalization of expected utility providing enhanced descriptive possibilities. We introduce a new incremental decision procedure, involving monotone regression spline functions to model both components of RDU, namely the probability weighting function and the utility function. First, assuming the utility function is known, we propose an elicitation procedure that incrementally collects preference information in order to progressively specify the probability weighting function until the optimal choice can be identified. Then, we present two elicitation procedures for the construction of a utility function as a monotone spline. Finally, numerical tests are provided to show the practical efficiency of the proposed methods.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model %A Patrice Perny LIP6 %A Paolo Viappiani Lip6 Paris %A abdellah Boukhatem LIP6 %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-lip616a %I PMLR %P 534--543 %U https://proceedings.mlr.press/r14/lip616a.html %V R14 %X This work concerns decision making under risk with the rank-dependent utility model (RDU), a generalization of expected utility providing enhanced descriptive possibilities. We introduce a new incremental decision procedure, involving monotone regression spline functions to model both components of RDU, namely the probability weighting function and the utility function. First, assuming the utility function is known, we propose an elicitation procedure that incrementally collects preference information in order to progressively specify the probability weighting function until the optimal choice can be identified. Then, we present two elicitation procedures for the construction of a utility function as a monotone spline. Finally, numerical tests are provided to show the practical efficiency of the proposed methods. %Z Reissued by PMLR on 04 October 2026.
APA
LIP6, P.P., Paris, P.V.L. & LIP6, a.B.. (2016). Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:534-543 Available from https://proceedings.mlr.press/r14/lip616a.html. Reissued by PMLR on 04 October 2026.

Related Material