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Vincent Mousseau

Researcher at Université Paris-Saclay

Publications -  159
Citations -  5236

Vincent Mousseau is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Sorting & ELECTRE. The author has an hindex of 36, co-authored 146 publications receiving 4669 citations. Previous affiliations of Vincent Mousseau include Sandia National Laboratories & Université du Québec à Trois-Rivières.

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Ordinal regression revisited: multiple criteria ranking using a set of additive value functions

TL;DR: Distinguishing necessary and possible consequences of preference information on the complete set of actions, UTAGMS answers questions of robustness analysis and can support the decision maker when his/her preference statements cannot be represented in terms of an additive value function.
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Inferring an ELECTRE TRI Model from Assignment Examples

TL;DR: An interactive approach that infers the parameters of an ELECTRE TRI model from assignment examples is proposed that lies in the possibility given to the DM to revise his/her assignment examples and/or to give additional information before the optimization phase restarts.
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A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support

TL;DR: This paper presents a new implementation of an existing method called ELECTRE TRI, which integrates specific functionalities supporting the decision maker (DM) in the preference elicitation process and aims at reducing the cognitive effort required from the DM in the phase of calibration of the preference model.
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An aggregation/disaggregation approach to obtain robust conclusions with ELECTRE TRI

TL;DR: A new interactive approach is proposed, where the insight obtained during robustness analyses guides the DMs during the elicitation phase, by integrating two approaches developed independently to deal with the case where the decision makers are unsure of which values should each parameter take.
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Using assignment examples to infer weights for ELECTRE TRI method: Some experimental results

TL;DR: Results showed that this tool is able to infer weights that restores in a stable way the assignment examples and that it was able to identify “inconsistencies” in the assignmentExamples.