Published November 13, 2025 | Version v1
Preprint Open

The Physics of a Discrete Gravitational Ontology: From Relational Networks to Emergent Geometry and Thermodynamics

Authors/Creators

Description

This paper extends the formal framework presented in Discrete Gravitational Ontology (Rev
E) [1]. That version established the theoretical foundation of DGO: a fully discrete, relational
model from which spacetime, quantum phenomena, and general-relativistic curvature emerge
through causal updates at the invariant rate c = ℓ0/τ0. The present work pursues two goals:
1. To formulate the physical and thermodynamic interpretation of DGO in explicit mathe-
matical and plain-language form.
2. To present numerical simulations that demonstrate emergent geometry, quantum-like be-
havior, and the arrow of time as natural consequences of discrete relational dynamics.

Python scripts are included in the ZIP-file with full LaTex source. 

Abstract

This paper presents the first numerical realization of the Discrete Gravitational Ontology
(DGO), a causal and relational framework in which geometry, quantum behavior, and ther-
modynamics emerge from discrete updates of probabilistic links. Building on the theoretical
formulation in Asplind (2025, Rev E), we demonstrate through four complementary simula-
tions that: (1) local probabilistic updates reproduce smooth curvature consistent with the
Einstein–Hilbert (EH) limit; (2) quantum-like propagation and back-reaction arise on the
realized record; (3) relational energy–entropy balances satisfy discrete analogues of the first
and second laws; and (4) a minimal seed universe expands self-consistently, exhibiting an
inflationary phase followed by a causal regime, with fitted exponents obeying the theoretical
constraint β ≈ 2α. Together, these results provide quantitative evidence that, within DGO,
spacetime, quantum statistics, and thermodynamic irreversibility can emerge from a single
discrete causal substrate.

Notes (English)

Acknowledgements
The author thanks colleagues, family, and friends who provided valuable discussion and critique
of the DGO Rev E formulation. The author also acknowledges the use of OpenAI’s ChatGPT
for assistance in scripting and verifying the toy simulations presented in this paper. The author
is actively seeking formal review and open discussion within the established research community
and welcomes collaboration and further investigation in this area.

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Additional details

Dates

Issued
2025-11-13
Rev A

Software

Programming language
Python

References