MR-Sort with Partial Information to Decide Whether to Invest in Innovation Projects

[en] More often than not, writing a funding application for an innovation project requires information that is not yet available. To avoid rejecting incomplete applications a priori, we propose here a variant of the MR-Sort method for dealing with partial information. It consists of identifying the criteria whose assessment is available and those whose assessment is missing. Depending on the mindset of the decision-maker, a bipolar hierarchy of ordered classes is run through to identify the class to be recommended on the basis of the state of information. This new proposal keeps the simplicity of use and expressiveness of the original MR-Sort method. It can be applied in contexts where the information is not always initially complete and can be acquired progressively.

Disciplines :

Computer science

Author, co-author :

Fortemps, Philippe ; Université de Mons - UMONS

Pirlot, Marc ; Université de Mons - UMONS

Language :

English

Title :

MR-Sort with Partial Information to Decide Whether to Invest in Innovation Projects

Publication date :

2022

Main work title :

Multiple Criteria Decision Making

Publisher :

Springer Science and Business Media Deutschland GmbH

ISBN/EAN :

978-3-03-096318-7
978-3-03-096317-0

Peer reviewed :

Peer reviewed

Development Goals :

9. Industry, innovation and infrastructure

Additional URL :

https://link.springer.com/content/pdf/10.1007/978-3-030-96318-7_12

Research unit :

F113 - Management de l'Innovation Technologique
F151 - Mathématique et Recherche opérationnelle

Research institute :

Infortech

Available on ORBi UMONS :

since 09 January 2023

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