"A theory has only the alternative of being right or wrong. A model has a third possibility: it may be right, but irrelevant"

When considering scientific progress, it's vital to distinguish between theories and models, both indispensable tools, yet serving different purposes. Theories are comprehensive frameworks designed to explain a broad range of phenomena, underpinned by principles and laws that embody our fundamental understanding. Their validity can be tested directly: either they accurately describe reality, or they do not. There is a palpable clarity in a theory’s fate – it stands or falls based on its accuracy with respect to observed evidence.

Models, on the other hand, are representations, sometimes simplifying or abstracting reality to make sense of complexity. They might employ analogies, mathematical structures, or assumptions that help conceptualize specific systems or processes. Crucially, a model may contain correct mathematics and valid logic, yielding predictions that follow from its premises. However, a model’s correctness is not the sole measure of its usefulness. Even when technically sound, a model can be rendered irrelevant if it fails to capture the aspects of reality most pertinent to the problem at hand.

Relevance hinges on contextual alignment, does the model illuminate the phenomena currently of interest? A model of planetary motion using Newtonian mechanics is both right and relevant for everyday applications; for relativistic speeds or immense gravitational fields, its predictions are correct within a limited scope but irrelevant for phenomena it fails to describe. Similarly, economic models may employ rational agents and equilibrium thinking, offering internally logical conclusions. Yet, in times of crisis, irrational behaviour dominates, making these models irrelevant despite their correctness within assumed parameters.

Recognizing this third possibility, correct but irrelevant, highlights the subtlety required to advance knowledge. Progress in science and other disciplines relies on updating models, ensuring that they not only work logically but also address pertinent, real-world conditions. The ultimate value of any model, then, is determined not only by its internal correctness but by its resonance with the realities to which it is applied.