A generalized data-driven MCDM model: Integrating diverse utility functions and complex criteria interactions

In multi-criteria decision-making (MCDM) scenarios, the adaptive extraction of interpretable decision analysis processes from data is critical for applications such as recommendation systems, marketing, and medical diagnosis. Compromised is the adaptability of existing data-driven MCDM methods, as they usually predetermine a single problem type, a specific utility function, and prescribed interaction patterns of criteria, on the basis of which they develop the corresponding algorithm. To address this, we present a generalized data-driven MCDM (G-MCDM) model and a corresponding end-to-end algorithm, Differentiable Utility Network (DUN), which jointly handles sorting and ranking problems, diverse utility functions, and the interactions among any input criteria by learning optimal functional forms directly from data. Experimental results across 16 real-world datasets demonstrate that DUN surpasses MCDM benchmarks by an average of 9.21% on sorting tasks and reduces ranking errors by 38.1%, while achieving accuracy competitive with machine learning baselines. Notably, DUN liberates MCDM from expert-dependent assumptions by adaptively deriving optimal utility functions and interaction patterns from data—and even potentially uncovering previously unknown utility functions that domain knowledge fails to prescribe.