Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system

Marco Cinelli, Miłosz Kadziński, Grzegorz Miebs, Michael Gonzalez and Roman Słowiński

European Journal of Operational Research, 2022, vol. 302, issue 2, 633-651

Abstract: We present a new methodology to lead the selection of Multiple Criteria Decision Analysis (MCDA) methods. It is implemented in the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS), a decision support system that helps analysts answer a recurring question in decision science: “Which is the most suitable Multiple Criteria Decision Analysis method (or a subset of MCDA methods) that should be used for a given Decision-Making Problem (DMP)?”. The MCDA-MSS provides guidance to lead decision-making processes and choose among an extensive collection (>200) of MCDA methods. These are assessed according to an original comprehensive set of problem characteristics. The accounted features concern problem formulation, preference elicitation and types of preference information, desired features of a preference model, and construction of the decision recommendation. The applicability of the MCDA-MSS has been tested on several case studies. The MCDA-MSS includes the capabilities of (i) covering from very simple to very complex DMPs, (ii) offering recommendations for DMPs that do not match any method from the collection, (iii) helping analysts prioritize efforts for reducing gaps in the description of the DMPs, and (iv) unveiling methodological mistakes that occur in the selection of the methods. A community-wide initiative involving experts in MCDA methodology, analysts using these methods, and decision-makers receiving decision recommendations will contribute to the expansion of the MCDA-MSS.

Keywords: Decision analysis; Multiple criteria; Taxonomy; Decision support system; Method recommendation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:302:y:2022:i:2:p:633-651

DOI: 10.1016/j.ejor.2022.01.011

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