Stochastic-Dissipative Least-Action framework for self-organizing biological systems, Part II: Empirical estimation,...

Quantifying how biological systems maintain organization far from thermodynamic equilibrium remains a fundamental challenge. Part I of this study introduced a stochastic-dissipative variational principle predicting that systems with defined source and sink endpoints evolve toward minimum average action per productive event. Here, this prediction is translated into an empirical framework centered on Average Action Efficiency (AAE), a dimensionless metric quantifying the number of productive events accomplished per unit physical action. A practical workflow is developed for extracting AAE directly from aggregate energetic and kinetic measurements-event counts, dissipated energy, and elapsed time-without reconstructing microscopic trajectories. The framework yields three testable predictions: (P1) AAE is operationally measurable from aggregate data; (P2) stronger feedback or tighter coupling reduces average action per event and raises AAE; and (P3) tightly coupled systems approach a system-specific minimum action, producing near-invariant event-level action across varying conditions. Applying the AAE framework to Nath's oxidative phosphorylation measurements reveals an AAE near unity, consistent with an average action per elementary step of order Planck's constant as derived in his analysis. This confirms operation at a system-specific lower bound set by the enzyme's discrete chemical and rotational degrees of freedom, and demonstrates that the product of energy per step and step duration remains nearly invariant, as predicted for tightly coupled molecular machines. These results establish AAE as a practical, dimensionless diagnostic of self-organization that links energy, time, and feedback into a single measurable quantity. Such optimization may represent a physical organizing tendency that complements random variation and natural selection by shaping the conditions under which evolutionary adaptation unfolds.