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March 30, 20260 citationsOpen Access

Structural Differentiation Quantum Theory (SDQ) v1.1: Time and Interaction as Emergent Structural Ordering

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KOKoji Okino

Key Points

  • This research aims to establish a unified framework in which time and interaction are emergent properties related to structural differentiation.
  • Introduces the Structural Differentiation Quantum Theory (SDQ) v1.1 framework.
  • Defines time as the ordering of irreversible structural change and interaction as overlapping structural gradients.
  • Presents the SDQ-Correlation Test (SDQ-CT) to provide a quantitative observational criterion.
  • Demonstrates that correlation strength depends on structural connectivity, differing from standard quantum predictions.
  • Introduces the formula S(L_s) = 2√2 e^{-βL_s} for correlation decay, providing a falsifiable criterion for structural emergence.

Abstract

This work presents Structural Differentiation Quantum Theory (SDQ) v1. 1, a minimal, unified, and falsifiable framework in which time and interaction are not fundamental, but emerge from structural differentiation. In SDQ, time is defined as the ordering of irreversible structural change, while interaction arises from the overlap of structural gradients. Quantum states are interpreted as relational configurations within unresolved structural networks rather than intrinsic probabilistic entities. The theory replaces fundamental assumptions with structural relations, proposing that observables such as time, force, and probability are projections of an underlying structural process. A single decisive observational test is introduced: the SDQ-Correlation Test (SDQ-CT). While standard quantum theory predicts constant maximal correlation, SDQ predicts an intrinsic decay of correlation with structural connectivity: S (Lₛ) = 2√2 e^-βLₛ A statistically significant dependence of correlation strength on structural connectivity at fixed physical separation provides a direct and falsifiable criterion distinguishing structural emergence from intrinsic randomness. All figures are reproducible via the included Python script. This framework provides a direct path toward experimental validation.

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Cite This Study

Koji Okino (2026) studied this question.

synapsesocial.com/papers/69c9c57ff8fdd13afe0bd536https://doi.org/10.5281/zenodo.19286335
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