A framework for converting MCDM methods for model comparison

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.