How Information Evolves

The emergence of complexity and information is a fundamental question spanning disciplines from physics to biology and computation. While traditional approaches describe information as an emergent property, they leave unresolved how it arises dynamically from purely abiotic processes. This paper presents Bayesian Assembly (BA) systems, an abstract framework that models the evolution of patterns through probabilistic interactions and stability-driven selection. By abstracting away specific physical laws, these systems demonstrate a universal mechanism for generating order and information. BA systems evolve patterns over successive generations, with selection pressures favoring stable configurations that persist and interact more frequently. These dynamics have the potential to encode logic, computational rules, and, under certain conditions, self-replicating behaviors, thereby offering a conceptual pathway to better understand the transition from abiotic to biotic evolution. However, substantial work remains to demonstrate these principles in realistic prebiotic scenarios or computational analogs. The framework also highlights the interplay between top-down and bottom-up causality, illustrating how emergent patterns recursively influence their formation while being shaped by local interactions. This study offers a computationally realizable pathway that may help bridge randomness and complexity. Further empirical validation and more detailed modeling are needed to confirm and refine these suggestions.