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A framework for converting MCDM methods for model comparison
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PRE
AI
DOI：10.1080/12460125.2026.2653695.png)
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Abstract
En 中文
Selecting the right MCDM method is a critical challenge for decision makers. In complex scenarios like employee selection, the choice of method can fundamentally alter the final ranking. This paper introduces a novel framework for model translation, comparing the qualitative DEX baseline against four prominent quantitative methods: AHP, TOPSIS, PROMETHEE, and PAPRIKA. To ensure rigorous validation, we utilised a controlled environment with an exhaustive dataset of nearly 2 million alternatives. Our preliminary findings reveal that while PROMETHEE and PAPRIKA show near-perfect alignment, the low correlations between DEX and the quantitative methods highlight a critical risk: the choice of method alone can fundamentally shift which employee is hired. This research underscores that no single MCDM method is flawless. By presenting a systematic translation and comparison, we provide a robust framework that helps managers navigate this complexity by identifying the most suitable method for their specific decision goals.
Keywords:
Model conversion
MCDM
framework
decision support systems
employee selection
DEX
Journal
J
IF:
4.3
Papers:
98
Citations:
0
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Cited Papers
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