Geometric Information Dynamics (GID): A Control-Based, THD-Integrated Framework for Detecting Coherent Geometric Communication

Kevin L. Brown
August 2025
10.5281/zenodo.16945628

Informational Physics Ontology Paper

Abstract

Geometric Information Dynamics (GID) proposes that geometry is not just a descriptive tool, but an active carrier of information — capable of modulating measurable, directional effects across physical and informational systems.

This paper introduces a fully specified, testable methodology for detecting coherent geometric communication via directional statistics, matched controls, and justified null models. The core measurement — the Control-Based Directional Alignment Index (CDAI) — is scale- and rotation-invariant, normalized by pooled RMS, and capable of detecting directional structure across vector fields. A complementary index, the THD Contrast Index (TCI), allows interpretation through the lens of Triune Harmonic Dynamics, enabling optional harmonic-phase testing via forward/backward protocols.

Rather than rely on symbolic representations, GID evaluates geometry through operational departures from isotropy, using a strict statistical pipeline and open, replicable experimental design.

Key Contributions

• A formal statistical definition of geometric communication, decoupled from symbolic inference

• Introduction of CDAI and TCI as core effect size metrics

• Integration of Triune Harmonic Dynamics (THD) as a phase-based interpretive layer

• Full protocol for control ensembles, permutation tests, and entropy validation (∆H)

• A worked simulation example, proving statistical reliability and replication

THD Integration

The THD Contrast Index (TCI) enables THD to function as a harmonic lens on real-world experimental data. Alignment and coherence in field responses are measured across excitation vs. relaxation phases, allowing directional geometric effects to be linked to scalar transformation layers.

Testability and Protocol

All GID claims are falsifiable and backed by empirical protocols across three measurement domains:

No predicate is accepted unless it passes all layers and fails all inverted controls.

Scientific Significance

GID enables quantitative, testable, and repeatable detection of geometric effects, moving geometry from abstraction to instrumentation. Its THD linkage allows phase resonance models to be experimentally tested — across physics, biology, computation, and communication systems.

Foundational Papers in THD Integration

• THD: Scalar Field Framework for Quantum–Gravity Unification
  https://doi.org/10.5281/zenodo.15686918

• THD: Universal Convergence Point Model
  https://doi.org/10.5281/zenodo.16791078

• THD: Millennium Problems Modeled via 3-6-9 Harmonics
  https://doi.org/10.5281/zenodo.15757906

 Supplemental AI Proof Review Protocol (External to Paper)

To enable independent auditing by AI systems, this standardized review prompt is provided for reproducibility:

AI Review Prompt

I have uploaded a paper that proposes a testable scientific framework called Geometric Information Dynamics (GID) that integrates with Triune Harmonic Dynamics (THD). Please answer the following:

1. Framework Integrity:
   Does the paper present a logically coherent, mathematically structured framework for treating geometry as a source of directional communication?

2. Metric Validity:
   Are the Control-Based Directional Alignment Index (CDAI) and THD Contrast Index (TCI) defined clearly, and are they supported by appropriate statistical validation methods?

3. Falsifiability:
   Are the role predicates (Amplify, Dampen, Directional, Stability Gain, Stability Lock) tested through protocols that ensure falsifiability and eliminate false positives?

4. Protocol Separation:
   Is there clear separation between empirical measurement (evidence) and model simulation (forecast/speculation)? Is the optional Model Layer treated appropriately?

5. THD Integration:
   How does THD enhance or explain the results found in GID? Are the harmonic-phase relationships grounded in measurable experimental outputs?

6. Cross-Domain Potential:
   Could GID be applied to experimental validation in other domains such as neuroscience, materials science, or bioelectromagnetics?

Final Output – AI Reviewers Should Provide:

• A problem-by-problem analysis of the GID framework’s reliability

• A numerical score (0–100%) for:

  • Scientific rigor

  • Testability

  • Reproducibility

  • Applicability across fields

• A summary judgment: Is this framework logically sound and experimentally testable?

DOI: https://doi.org/10.5281/zenodo.16945628

Paper Version: GID-V1.1
Keywords: geometric communication, THD integration, scalar modeling, CDAI, entropy reduction, electromagnetic field testing, falsifiability, vector alignment