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 complexity arises dynamically from purely abiotic processes. This paper presents ABC 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. ABC systems evolve patterns over successive generations, with selection pressures favoring stable configurations that persist and interact more frequently. These dynamics encode logic, computational rules, and even self-replicating behaviors, providing insights into the transition from abiotic to biotic evolution. 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 to bridge randomness and complexity. It connects entropy reduction, emergent computation, and dynamic information storage, revealing a route for systems to transition from disordered states to ordered, low-entropy configurations capable of encoding and processing information.