Digital Twins and World Models: A Systematic Taxonomic Disambiguation

Although often conflated, Digital Twins and World Models represent distinct paradigms of system reasoning. A Digital Twin is defined by a persistent, bidirectional data coupling with a specific physical asset. Conversely, a World Model captures environmental dynamics to support prediction and planning, without necessitating a direct link to a singular physical counterpart. This paper establishes a rigorous disambiguation for researchers and practitioners in the Physical AI realm. We define minimal criteria for each paradigm, compare them via a multidimensional taxonomy, and validate the framework using concrete real–world systems. Our analysis demonstrates that neither paradigm subsumes the other. Rather, they intersect within a bounded convergence zone where specific architectures integrate both. While this taxonomy is descriptive and integrative, we note that formal ontological grounding remains an avenue for future work.