Models are widely used across science, economics, climate research, and AI. Yet the first step in understanding their function is to recognise what they are and what they are not.
A model is a theory of possible instances. It is a structured abstraction that represents potential configurations, behaviours, or dynamics of a system. It does not exist as the system itself; it cannot instantiate phenomena independently. The relational cut — the distinction between model and world — is essential: the model is a tool for exploring possibilities, not a literal depiction of reality.
Classical examples illustrate clarity: a Newtonian planetary model represents the possible trajectories of celestial bodies; an ideal gas law models ensembles of particles under specific assumptions. In both cases, the model predicts or describes behaviour, but it does so as a theory of possibilities, not as the phenomena themselves.
Confusion arises when this cut is ignored or vanishes. When a model is treated as the world itself, outputs are interpreted as literal reality, uncertainties are underappreciated, and the theory’s relational status is obscured. The model ceases to be a diagnostic tool and becomes a surrogate for the world — a structural mutation we will explore across multiple domains.
Establishing this foundational clarity allows us to see the consequences of vanishing cuts, and why recognising the distinction between model and world is critical for accurate interpretation, evaluation, and responsible application of models.
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