Skip to main content

Write a PREreview

Fractal-Stochastic Emergence of Discrete Time in Chaotic Systems: Numerical Evidence Against Newtonian Absolutism

Posted
Server
Preprints.org
DOI
10.20944/preprints202512.2010.v1

Absolute Newtonian time—as a continuous, universal parameter external to physical reality—contradicts the emergent, discrete temporal structure observed in chaotic systems. This paper provides numerical validation for the hypothesis that objective time emerges discretely from ordinal patterns rather than being imposed a priori. The Discrete Extramental Clock Law, defined by tn+1 = tn +∆t·g(τs) with universal gating g(τs) rooted in Kendall’s τ variance thresholds and Feigenbaum scaling, is tested across classical and non-classical chaotic attractors. Extensive simulations reveal empirical support for three core predictions: fractal inheritance in emergent time tn (Dtn ≈ 1.98 from D ≈ 2.06), trimodal stochastic dynamics in g(τs) with high variance (σ2 ≈ 0.85) and autocorrelation (ρ1 ≈ 0.85), and ∼ 50% variance reduction in weakly coupled networks, yielding smoother collective temporality. These results demonstrate time as a fractal-stochastic emergent phenomenon, providing quantitative evidence against Newtonian absolutism and supporting Polo’s transcendental view of extramental persistence. The findings bridge physics and metaphysics, offering empirical tools for modeling synchronization in biological collectives and human agency in critical regimes, where local retrocausality enables kairos—opportune moments—from chaotic physis.

You can write a PREreview of Fractal-Stochastic Emergence of Discrete Time in Chaotic Systems: Numerical Evidence Against Newtonian Absolutism. A PREreview is a review of a preprint and can vary from a few sentences to a lengthy report, similar to a journal-organized peer-review report.

Before you start

We will ask you to log in with your ORCID iD. If you don’t have an iD, you can create one.

What is an ORCID iD?

An ORCID iD is a unique identifier that distinguishes you from everyone with the same or similar name.

Start now