Skipped Mass Correction Model

The Skipped Mass Correction Model demonstrates strong accuracy across a diverse range of systems—without relying on any fitted parameters. It was derived purely from the structural observation that exponential models skip over meaningful intermediate values. When tested across six distinct domains—epidemics, hardware scaling, population growth, energy efficiency, startup valuation, and internet adoption—the model consistently showed low mean absolute and root mean square errors in systems with measurable structure. In structured, physical systems (like hardware and epidemic spread), the model performs exceptionally well, capturing real-world growth with minimal deviation. In human-influenced systems (like population and startups), it still tracks the overall behavior while exposing domains where external factors—policy, sentiment, or hype—introduce volatility. The model's success, despite its simplicity and lack of data fitting, suggests that structural skipping is a real and measurable phenomenon in exponential growth. It offers both a corrective tool and an interpretive lens for forecasting and system modeling.