Widely Used Multi-Criteria Decision Analysis Methods—A Comprehensive Ranking

Danielson, M. (2026). Widely Used Multi-Criteria Decision Analysis Methods—A Comprehensive Ranking. Applied Sciences 16 (14) e7269. 10.3390/app16147269.

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Abstract

This article presents a comprehensive ranking of popular Multi-Criteria Decision Analysis (MCDA) methods, assessed across six dimensions of visibility and usage: general web presence, academic mentions, number of academic publications, citation impact, reported academic applications, and real-world (industrial and public sector) use cases. Rather than evaluating theoretical performance or methodological robustness, this study focuses on visibility and adoption, the extent to which an MCDA method has achieved prominence through name recognition, dissemination, academic use, and documented practical application. The observed ranking patterns are consistent with the possibility that method popularity is partly shaped by branding, dissemination dynamics, and cumulative visibility, rather than by demonstrated methodological superiority alone. AHP emerges as the most dominant method across all six criteria, followed by TOPSIS, PROMÉTHÉE, and VIKOR. Newer or more recently visible methods such as BWM demonstrate rapid academic growth, while software-supported methods such as PAPRIKA illustrate how dedicated tools can strengthen practical visibility. By contrast, established methods like ÉLECTRE and MACBETH, while academically respected, exhibit more niche or regionally concentrated use. The study also touches on the meta-cognitive irony that MCDA methods, designed to reduce bias and promote rational deliberation, may themselves be adopted in ways consistent with mechanisms such as availability, familiarity, and brand recognition. This raises concerns about the criteria by which methodological choices are made, particularly in academic modelling and industrial decision-support contexts. Ultimately, the findings suggest that while branding plays a legitimate role in method dissemination, it should not overshadow considerations of methodological transparency, theoretical coherence, and decision-context fit. By distinguishing between methodological performance and visibility/adoption, this article contributes to a more reflective discourse on the selection, teaching and development of MCDA methods and techniques. The ranking should therefore be interpreted as a comparative map of visibility and adoption, not as an assessment of methodological quality, theoretical soundness, or decision-analytic performance.

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