Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Normative Stress Dynamics: Empirical Evidence for a Cross-Domain Dynamical Universality Class

Authors: Paajanen, Juho;

Normative Stress Dynamics: Empirical Evidence for a Cross-Domain Dynamical Universality Class

Abstract

Normative Stress Dynamics (NSD) posits that corporate burnout cascades, financial market contagion, and neurological seizure activity belong to a single dynamical universality class governed by leaky stress conservation, adaptive capacity degradation, and homeostatic plasticity. We test this claim empirically across two physical substrates using an identical two-state Kalman filter. In Domain A (cryptocurrency microstructure), we progress through three filtering architectures — SVGD particle filter, Rao-Blackwellized Particle Filter, and Kalman filter — coupled with an agent-based liquidation cascade engine, applied to 300 million Binance BTCUSDT book updates. In Domain B (neurological), we apply the same Kalman filter to scalp EEG recordings from the CHB-MIT Seizure Database. The transition matrix $A$ and control matrix $B$, encoding the structural NSD physics, are held constant across both domains. Only the process noise ($Q$) and measurement noise ($R$) matrices are recalibrated via Maximum Likelihood Estimation to match each domain's signal-to-noise ratio. Under an 11-test causal identification battery, the Kalman pressure state achieves LIKELY CAUSAL in crypto (7/11, Tier 2: 2/4) and 8–10/11 in EEG (Tier 2: 4/4 across all 24 patients tested). The EEG domain provides *stronger* structural causal evidence — do-intervention and time-reversal tests that fail in crypto pass in EEG, for physically interpretable reasons. Cross-patient generalization is confirmed across the entire CHB-MIT database: all 24 patients achieve LIKELY CAUSAL or better (median 10/11, mean 9.5/11, Tier 2: 4/4 for all 24). In a cross-domain transfer learning experiment, the stress persistence parameter $A_{11}$ is empirically extracted from BTCUSDT data via MLE, yielding $A_{11}^{\text{BTC}} = 0.843$ (vs. theoretical prior 0.95). Injecting this Bitcoin-derived physics into the EEG engine *improves* chb01 from 8/11 to 9/11, providing direct evidence that the structural dynamics are transferable across physical substrates. These results constitute the first cross-domain empirical evidence that the NSD state-space model captures a genuine universality class operative in both financial and neurobiological systems.

Keywords

Econophysics, Computational Neuroscience, Limit Order Books, Epilepsy, Kalman Filter, Complex Systems.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green