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March 3, 20260 citationsOpen Access

Relational Structural Framework

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HMHess Malin

Key Points

  • The aim is to establish a unified framework explaining the emergence of physical law, complexity, and consciousness from a primitive concept.
  • Developed a relational-structural framework based on minimal distinction and unified relational totality.
  • Analyzed causal structure as an intrinsic parameterization resulting from ordered dependency.
  • Defined entropy within statistical mechanics related to globally consistent configuration spaces.
  • Investigated persistent entropy gradients and finite information capacity's role in adaptive system formation.
  • Proposed a relationship between consciousness and high integrated information within bounded subsystems.
  • Established a connection between minimal distinction and the emergence of complex systems and consciousness.
  • Demonstrated that entropy gradients influence complexity and system adaptation.
  • Defined typicality as a measure-theoretic result of symmetry invariance, offering insights into physical and biological phenomena.

Abstract

This white paper develops a relational-structural framework that derives the emergence of physical law, complex systems, and consciousness from a single primitive: minimal distinction. Beginning with the axiom that existence requires differentiation within a unified relational totality (U), we demonstrate how logical consistency necessitates ordered dependency, from which causal structure (time) emerges as an intrinsic parameterization. The framework then extends to statistical mechanics, defining entropy over globally consistent configuration spaces and establishing typicality as a measure-theoretic consequence of symmetry invariance. Within this landscape, metastable complexity scales with persistent entropy gradients and finite information capacity, providing a structural account of adaptive system formation. Finally, we propose that consciousness corresponds to regimes of high integrated information (), characterized by recursive self-modeling within bounded subsystems. Meaning arises locally as valuation dynamics within such systems navigating entropy gradients—an emergent phenomenon requiring no external teleology. The framework unifies physical, biological, and phenomenal domains under a common structural ontology, offering testable principles for investigating the interface between information theory, complexity science, and consciousness studies.

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Cite This Study

Hess Malin (2026) studied this question.

synapsesocial.com/papers/69a67f06f353c071a6f0ac41https://doi.org/10.5281/zenodo.18824467
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