STIML (Scaling Theory Informed Machine Learning)

STIML — Scaling Theory Informed Machine Learning — is West's late-career methodological synthesis, developed to address the principal limitation of scaling theory: that it predicts ensemble averages but cannot predict individual trajectories. The framework uses scaling laws to capture the 'trend-driven predictability' — the average trajectory that any given company, city, or organism will follow based on its size and network topology — while deploying machine-learning methods to model the 'fluctuation-driven predictability' — the residual deviations that separate individual cases from the average. The framework is an epistemological compromise, acknowledging that scaling laws describe the central tendency but not the specific outcome, and that understanding any individual system requires both the structural prediction from theory and the data-driven modeling of its specific deviations. STIML is also an implicit admission that where individual fate diverges from ensemble average — where specific leadership choices, cultural factors, or accidents of timing matter — the mathematics provides a map but not a route.