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

arXiv: https://arxiv.org/abs/2106.07378 · HTML: https://mlsanity.com/paper/2106.07378/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 273 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria](https://mlsanity.com/paper/1905.08506/) (2019)
- [A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples](https://mlsanity.com/paper/1910.05485/) (2019)
- [Stochastic Multiobjective Acceptability Analysis for the Choquet integral preference model and the scale construction problem](https://mlsanity.com/paper/1302.6192/) (2013)
- [UTA-poly and UTA-splines: additive value functions with polynomial marginals](https://mlsanity.com/paper/1603.02626/) (2016)
- [A Fuzzy AHP Approach for Supplier Selection Problem: A Case Study in a Gear Motor Company](https://mlsanity.com/paper/1311.2886/) (2013)
- [Generalised framework for multi-criteria method selection](https://mlsanity.com/paper/1810.11078/) (2018)
- [Robust stochastic sorting with interacting criteria hierarchically structured](https://mlsanity.com/paper/2003.04844/) (2020)
- [Robust sustainable development assessment with composite indices aggregating interacting dimensions: the hierarchical-SMAA-Choquet integral approach](https://mlsanity.com/paper/1801.05265/) (2018)

## Built on by (most cited first, 3 of 3)

- [Towards Explainable TOPSIS: Visual Insights into the Effects of Weights and Aggregations on Rankings](https://mlsanity.com/paper/2306.07706/) (2023)
- [Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria](https://mlsanity.com/paper/2409.01612/) (2024)
- [Utility Inspired Generalizations of TOPSIS](https://mlsanity.com/paper/2504.08014/) (2025)

## Abstract (arXiv)

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.