Minimax Regret Approaches for Preference Elicitation with Rank-Dependent Aggregators - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EURO Journal on Decision Processes Année : 2015

Minimax Regret Approaches for Preference Elicitation with Rank-Dependent Aggregators

Nawal Benabbou
Christophe Gonzales
Patrice Perny
Paolo Viappiani

Résumé

Recently there has been a growing interest in non-linear aggregation models to represent the preferences of a decision maker in a multicriteria decision problem. Such models are expressive as they are able to represent synergies (positive and negative) between attributes or criteria, thus modeling different decision behaviors. They also make it possible to generate Pareto-optimal solutions that cannot be obtained by optimizing a linear combination of criteria. This is the case of rank-dependent aggregation functions such as Ordered Weighted Averages and their weighted extensions, but more generally of Choquet integrals. A key question is how to assess the parameters of such models to best fit decision maker’s behaviors or preferences. In this work, adopting a principled decision-theoretic approach, we consider the optimization problem induced by adaptive elicitation using the minimax regret criterion.
Fichier principal
Vignette du fichier
choquet.pdf (406.38 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01170030 , version 1 (11-07-2016)

Identifiants

Citer

Nawal Benabbou, Christophe Gonzales, Patrice Perny, Paolo Viappiani. Minimax Regret Approaches for Preference Elicitation with Rank-Dependent Aggregators. EURO Journal on Decision Processes, 2015, 3 (1-2), pp.29-64. ⟨10.1007/s40070-015-0040-6⟩. ⟨hal-01170030⟩
177 Consultations
338 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More