Finite Cognition and Stabilization: Fragility, Biological Constraints, and an Operational Framework

This paper extends Finite Cognition and Model Selection: An Operational Framework for Stabilization by explicitly incorporating cognitive fragility and biological processing lag (T_bio) into a generalized stabilization framework. Stabilization is formalized as a balance between Negentropy (structural coherence, redundancy, knowledge integration) and Entropy (information load, cognitive strain, perturbations, information flux), with operational proxies provided for simulation and minimal testing. While the formulation is abstract and partly symbolic, all limitations—including linearity assumptions, weight calibration, and simplifications—are explicitly acknowledged, providing a transparent, reproducible, and conceptually rigorous foundation for studying finite cognition across domains such as AI, scientific modeling, and cognitive systems. This framework allows the reader to evaluate representational viability under bounded cognition, even in the absence of full empirical calibration.