This work presents Randomness–Selection Dynamics (RSD), a theoretical framework proposing that complexity across physical, chemical, and biological systems emerges from the interaction between stochastic state generation (randomness) and probabilistic stabilization (selection). The framework introduces formal definitions for local randomness, global generative capacity, and a selection operator modeled as a probabilistic transition over a stability landscape. It explains how local randomness can decrease while overall generative capacity increases through combinatorial interactions, enabling sustained complexity growth. A coupled dynamical system is developed to describe the evolution of randomness, selection strength, and total complexity over time. The model is designed to be domain-independent, allowing application across multiple scales, including physical systems, chemical processes, and biological evolution. The framework proposes falsifiable predictions, including the existence of an intermediate randomness regime that maximizes complexity growth and the tendency of systems to transition toward nearest stable states following destabilization. This document represents an initial formalization intended for early dissemination and future development through theoretical refinement, simulation, and empirical validation.
Ambar Shukla Ambar Shukla (Thu,) studied this question.