Contemporary economic systems, including global supply chains and corporate financial structures, are characterised by substantial uncertainty that complicates reliability modelling. Data on component reliability, interdependencies, and failure modes are often incomplete, vague, or derived from expert judgment, which challenges traditional probabilistic risk models. This paper introduces a novel methodology for data-driven construction of mathematical models of complex systems. We frame the problem as one of classifying a system’s overall state (e.g., “Stable,” “Vulnerable,” “Distressed”) based on the uncertain states of its constituent economic components. The core innovation lies in employing a decision-tree-based classifier capable of handling uncertain and categorical data, and algorithmically transforming it into a Multi-Valued Decision Diagram (MDD). The method remains compatible with fuzzy extensions, but the present implementation uses a crisp decision tree. The MDD serves as a formal, analyzable Multi-State System (MSS) model. The efficacy of the method is demonstrated through a case study on modelling the operational resilience of a multimodal logistics network, showcasing its utility for proactive economic risk management and decision support under uncertainty.