A Generic Framework to Include Belief Functions in Preference Handling for Multi-criteria Decision

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)


Modelling the preferences of a decision maker about multi-criteria alternatives usually starts by collecting preference information, then used to fit a model issued from a set of hypothesis (weighted average, CP-net). This can lead to inconsistencies, due to inaccurate information provided by the decision maker or to a poor choice of hypothesis set. We propose to quantify and resolve such inconsistencies, by allowing the decision maker to express her/his certainty about the provided preferential information in the form of belief functions.


Decision Maker Partial Order Maximal Element Preference Model Information Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Sorbonne Université, UMR CNRS 7253 Heudiasyc, Université de Technologie de Compiègne CS 60319Compiègne CedexFrance

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