Comparing Machine Learning and MCDM Weighting Paradigms in Smart City Benchmarking

Résumé fourni par la source

Smart city indices embedded in digital benchmarking platforms increasingly shape urban governance decisions, yet criteria weighting, a critical algorithmic design choice, remains underexamined. This study compares two paradigms for deriving criteria weights: objective multi-criteria decision-making (MCDM) methods and machine learning (ML) feature-importance approaches, applied to the IMD Smart City Index 2024 dataset covering 45 Asia-Pacific cities and 39 indicators. TOPSIS rankings are generated under weight configurations from four MCDM and four ML methods. Results reveal systematically different weight structures: Gini coefficients of 0.10–0.37 for MCDM versus 0.42–0.50 for tree-based ML methods, with ML approaches concentrating importance on digital service indicators and allocating 67–75% of weight to the Technology pillar. Despite these divergences, city rankings remain highly correlated, suggesting the disagreement is one of interpretability rather than decision outcomes. The study contributes a comparison framework for designing transparent, configurable IS artifacts for urban governance.

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