Informational State Dynamics: Compression, Selectivity, and Observer-Dependent Structure

Across physics, computation, and information science, observation is increasingly recognized as a non-neutral operation. However, a unified formulation describing how observer-dependent structure emerges from information processing remains lacking. In this work, we introduce Informational State Dynamics (ISD), a general framework in which observers are formally modeled as compression operators acting on an underlying informational substrate. Informational states are defined as observer-relative compressions shaped by intrinsic informational loss and structural selectivity. We show that no observer can access a complete or unbiased representation of the informational substrate, and that non-equivalence between observers arises as a necessary consequence of compression asymmetries. ISD is substrate-independent, compatible with classical information theory, and provides a minimal, falsifiable foundation for analyzing observer-dependent structure across physical systems, artificial agents, and abstract information processors.