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

TL;DR

This paper introduces MCDA-MSS, a decision support system that guides analysts in selecting suitable multiple criteria decision analysis methods based on a comprehensive set of problem features, tested on multiple case studies.

Contribution

The paper presents a novel taxonomy-based software tool for selecting MCDA methods, incorporating extensive problem characteristics and supporting complex decision-making scenarios.

Findings

Successfully tested on multiple case studies

Provides recommendations even for unmatched decision problems

Helps identify methodological mistakes in method selection

Abstract

We present the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS). This decision support system helps analysts answering 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 includes guidance to lead decision-making processes and choose among an extensive collection (over 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 expansion of the MCDA-MSS.