Information systems — from biological evolution to human language and digital communication — exhibit recurring patterns of optimization, innovation, and propagation. This paper proposes three candidate laws that describe these dynamics within a unified mathematical framework. Unlike traditional information theory, which primarily quantifies transmission and entropy, these laws concern the behavior of information as it evolves through networks and competitive environments. Law I formalizes optimization under repeated selection via a non‑negative expected utility derivative. Law II posits infinite creative dimensionality through the non‑saturation of meaningful representational space. Law III models entangled propagation across coupled network platforms using a system of coupled differential equations. The framework generates experimentally testable predictions regarding communication efficiency, creative search spaces, and multi‑platform diffusion. Each law is presented alongside an explicit falsifiability criterion, and the paper closes with a concrete proposal for empirical validation.
Richard Anthony Amaya (Sun,) studied this question.