Many adaptive systems compress high–dimensional environmental information into tractableinternal representations in order to support prediction and action under informational con-straints. While information–theoretic approaches have studied how individual agents performsuch compression, most models implicitly assume that interacting agents share a common rep-resentational space. In practice, different agents may compress the same environment throughdistinct representational architectures.This paper develops the concept of reality compression signatures as a formal framework forcharacterizing how agents encode, preserve, and reconstruct environmental information underinformation–theoretic constraints. Each agent is modeled as possessing a compression signaturethat defines its encoding, decoding, and distortion tolerance. Distances between signaturesinduce a geometric structure in representation space that governs semantic interoperabilitybetween agents.We formalize signature distance, decoding elasticity, and interoperability, and show howthese mechanisms induce weighted communication networks whose connectivity depends onrepresentational compatibility. As signature divergence increases relative to decoding elastic-ity, communication networks undergo fragmentation into clusters of representation-compatibleagents.This work establishes a formal basis for analyzing communication breakdown, intellectualfragmentation, and interoperability among heterogeneous cognitive and artificial systems.
Riaan de Beer (Sun,) studied this question.