2021arXiv (Cornell University)Open access

Recommending Multiple Criteria Decision Analysis Methods with A New\n Taxonomy-based Decision Support System

Marco Cinelli, Miłosz Kadziński, Grzegorz Miebs, Michael J. González, Roman Słowiński

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Abstract

We present the Multiple Criteria Decision Analysis Methods Selection Software\n(MCDA-MSS). This decision support system helps analysts answering a recurring\nquestion in decision science: Which is the most suitable Multiple Criteria\nDecision Analysis method (or a subset of MCDA methods) that should be used for\na given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead\ndecision-making processes and choose among an extensive collection (over 200)\nof MCDA methods. These are assessed according to an original comprehensive set\nof problem characteristics. The accounted features concern problem formulation,\npreference elicitation and types of preference information, desired features of\na preference model, and construction of the decision recommendation. The\napplicability of the MCDA-MSS has been tested on several case studies. The\nMCDA-MSS includes the capabilities of (i) covering from very simple to very\ncomplex DMPs, (ii) offering recommendations for DMPs that do not match any\nmethod from the collection, (iii) helping analysts prioritize efforts for\nreducing gaps in the description of the DMPs, and (iv) unveiling methodological\nmistakes that occur in the selection of the methods. A community-wide\ninitiative involving experts in MCDA methodology, analysts using these methods,\nand decision-makers receiving decision recommendations will contribute to\nexpansion of the MCDA-MSS.\n

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We present the Multiple Criteria Decision Analysis Methods Selection Software\n(MCDA-MSS). This decision support system helps analysts answering a recurring\nquestion in decision science: Which is the most suitable Multiple Criteria\nDecision Analysis method (or a subset of MCDA methods) that should be used for\na given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead\ndecision-making processes and choose among an extensive collection (over 200)\nof MCDA methods. These are assessed according to an original comprehensive set\nof problem characteristics. The accounted features concern problem formulation,\npreference elicitation and types of preference information, desired features of\na preference model, and construction of the decision recommendation. The\napplicability of the MCDA-MSS has been tested on several case studies. The\nMCDA-MSS includes the capabilities of (i) covering from very simple to very\ncomplex DMPs, (ii) offering recommendations for DMPs that do not match any\nmethod from the collection, (iii) helping analysts prioritize efforts for\nreducing gaps in the description of the DMPs, and (iv) unveiling methodological\nmistakes that occur in the selection of the methods. A community-wide\ninitiative involving experts in MCDA methodology, analysts using these methods,\nand decision-makers receiving decision recommendations will contribute to\nexpansion of the MCDA-MSS.\n

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Available abstract

We present the Multiple Criteria Decision Analysis Methods Selection Software\n(MCDA-MSS). This decision support system helps analysts answering a recurring\nquestion in decision science: Which is the most suitable Multiple Criteria\nDecision Analysis method (or a subset of MCDA methods) that should be used for\na given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead\ndecision-making processes and choose among an extensive collection (over 200)\nof MCDA methods. These are assessed according to an original comprehensive set\nof problem characteristics. The accounted features concern problem formulation,\npreference elicitation and types of preference information, desired features of\na preference model, and construction of the decision recommendation. The\napplicability of the MCDA-MSS has been tested on several case studies. The\nMCDA-MSS includes the capabilities of (i) covering from very simple to very\ncomplex DMPs, (ii) offering recommendations for DMPs that do not match any\nmethod from the collection, (iii) helping analysts prioritize efforts for\nreducing gaps in the description of the DMPs, and (iv) unveiling methodological\nmistakes that occur in the selection of the methods. A community-wide\ninitiative involving experts in MCDA methodology, analysts using these methods,\nand decision-makers receiving decision recommendations will contribute to\nexpansion of the MCDA-MSS.\n

Key concepts: Multiple-criteria decision analysis, Decision analysis, Computer science, Preference, Selection (genetic algorithm), Decision support system, Preference elicitation, Management science

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