Epistemic Parsimony

Epistemic parsimony is the principle that explanatory frameworks should minimize the number of unobserved entities, unverified assumptions, or unnecessary theoretical posits required to account for a phenomenon. It is Occam's Razor translated from metaphysics into epistemology: not merely a claim that nature is simple, but a constraint on what a rational agent is justified in believing given available evidence. In scientific practice, epistemic parsimony functions as a complexity prior that prevents overfitting — the tendency to construct theories so detailed that they explain noise rather than signal.

The principle becomes problematic in complex systems, where the minimal sufficient explanation may be intractably large. A parsimonious model of climate dynamics or neural computation may be systematically wrong not because it includes too much but because it includes too little, smoothing over causal mechanisms that are essential to prediction. The challenge for contemporary methodology is to distinguish descriptive parsimony — few parameters — from mechanistic parsimony — few causal processes — and to recognize that the two can conflict.