Finite Cognition and Model Selection: A Maximal Stabilization Framework

Model selection frameworks such as Bayesian inference and Minimum Description Length (MDL) assume that candidate models can be stably represented and manipulated. Real cognitive and computational systems, however, are finite, constrained in memory, computation, and representational fidelity. This paper operationalizes stabilization, the principle that a model must persist, transform, and integrate reliably to remain viable. We formalize stabilization as a dynamic, multi-objective function, define measurable proxies for each component, propose simulation protocols to evaluate model viability under realistic constraints, and examine cross-domain manifestations of stabilization. This framework bridges conceptual insights from finite cognition with practical model assessment, moving beyond classical error- or complexity-based selection toward a system-constrained notion of model survival and practical usability.