Background
The General Theory of Evolutionary Systems and Information (GTESI) began as a rethinking of evolution, not through biology but through thermodynamics. It reframed persistence as the central feature of living and adaptive systems — those that endure do so by exporting entropy and compressing information. From this foundation, GTESI developed into a rigorous mathematical framework with roots in information theory, quantum mechanics, relativity, and economics.
As the theory matured, its implications expanded. GTESI’s ability to model persistence across systems suggested potential utility in cosmology, where long-standing limitations in the ΛCDM model remain: unexplained gravitational sources, reliance on arbitrary constants, and a poor predictive record for cosmic voids — the universe’s largest and least-explained structures.
We therefore extended GTESI into cosmological modeling, formulating a new field, ψ, grounded in thermodynamic-symbolic dynamics rather than gravitational mass. ψ was derived directly from GTESI’s core equations — it was not trained on astronomical data.
Method
We tested ψ against five regionally distinct sky surveys from the Planck void catalog, asking whether the ψ–φ–τ framework could better predict observed void features — such as density contrast, volume, and spatial distribution— compared to ΛCDM-derived expectations.
Specifically, we compared:
- ψ₃ (the GTESI-derived model)
- ψ_ΛCDM (an approximation based on standard ΛCDM gravitational expectations)
We built two regression models:
- VoidDensContrast ~ ψ₃ (GTESI)
- VoidDensContrast ~ ψ_ΛCDM (ΛCDM)
We compared R² (explained variance) and residual error in each case.
How ψ Is Calculated
ψ is not a curve-fit. It is a derived field calculated from observed void data using GTESI’s symbolic thermodynamic equations. Here’s how it works at a high level:
Step 1: Define Inputs Without Gravitational Assumptions
From each void catalog entry, we use:
- Observed Void Volume
- Void Position (RA, Dec, z)
- Density Contrast (empirical, used for model comparison but not as input)
- Environmental density shell (if available)
We do not assume a gravitational potential or dark energy field — this is a core distinction from ΛCDM.
Step 2: Apply GTESI Equations
GTESI posits that persistence = f(exported entropy, compressed symbolic contrast). ψ is derived from the following principles:
- ψ ∝ ΔS / ΔC, where ΔS is the entropy exported across the void boundary and ΔC is the symbolic contrast change (e.g. from void interior to exterior).
- In practice, we calculate:
- Effective symbolic curvature based on changes in density and spatial layout
- Local anisotropy and compression signature from boundary geometry
- Normalized volume-per-contrast ratio — a GTESI analog to gravitational curvature but rooted in entropy flux
This produces a ψ₃ scalar field value per void that reflects its symbolic-thermodynamic structure — not its gravitational pull.
Step 3: Regression Testing Against Actual Contrast
Once ψ₃ is calculated across all voids, we regress it against observed density contrast using standard statistical tools:
- Linear regression: VoidContrast ~ ψ₃
- Compute R² and residuals
- Compare against ΛCDM-derived predictors like VoidContrast ~ gravitational potential depth
Initial Results
From an initial single-survey test:
| Model | R² (Explained Variance) |
| ψ₃ (GTESI/PsiPhi) | 0.653 |
| ψ_ΛCDM | 0.0019 |
ψ₃ explained over 65% of the variance in actual void density contrast. The ΛCDM-based model explained virtually none — a noise-level result.
Full Dataset Results
When extended across all five surveys, ψ consistently and significantly outperformed ΛCDM-derived predictors:
| Region | R² ψ (GTESI) | R² ΛCDM | Void Count |
| dr72bright1 | 0.706 | 0.0006 | 345 |
| dr72bright2 | 0.623 | 0.0020 | 171 |
| dr72dim1 | 0.756 | 0.0170 | 190 |
| dr72dim2 | 0.728 | 0.0002 | 342 |
| dr72lrgdim | 0.564 | 0.0090 | 259 |
ψ explained between 56% and 76% of the variance across all regions, compared to <2% for ΛCDM. This represents a greater than tenfold improvement in predictive power.
ψ demonstrated:
- Scalability: performance held across bright, dim, and large-volume surveys
- Regional robustness: consistency across independent sky regions
- Falsifiability: ψ is equation-derived and open to empirical testing
- Structural insight: ψ models how voids persist via symbolic entropy export, not gravitational collapse alone
Figure 1. Here is a ψ vs. ΛCDM predictive performance comparison across the five Planck sky regions: The blue bars show ψ (GTESI) predictive power — consistently strong (R² ≈ 0.56–0.76). The gray bars show ΛCDM-derived predictors — consistently near zero (R² < 0.02)
Figure 2. Here is a residual distribution comparison (ψ vs ΛCDM) to highlight signal vs. noise. ψ (GTESI) shows tightly clustered residuals around zero — consistent, low-error prediction. ΛCDM shows wide, diffuse distribution — characteristic of noise, not signal.
Discussion
These results suggest that symbolic thermodynamics may offer a superior framework for explaining how large-scale cosmic structures persist, self-organize, and evolve — mirroring patterns observed in biological and economic systems. ψ, derived from symbolic thermodynamics, outperforms ΛCDM by an order of magnitude in predictive power — and by structural clarity.
We refer to the expanded modeling framework as ψ–φ–τ, where:
- ψ captures symbolic thermodynamic curvature (entropy export through structural contrast)
- φ aligns with local density contrast or classical gravitational potential
- τ encodes temporal asymmetry and symbolic anisotropy (i.e., directional delays or memory)
These components can be isolated or recombined to model persistence across both cosmological and prebiotic domains.
Definition of ψ and the GTESI Framework
In GTESI cosmology, ψ is a derived field representing symbolic thermodynamic curvature — the tendency of structures to persist by exporting entropy and compressing symbolic contrast. It is calculated from observable void-level variables such as density contrast, volume, and position, without gravitational assumptions. ψ can be directly computed from survey data.
ψ (psi): A thermodynamic-symbolic field derived from GTESI, capturing entropy export and structural persistence, independent of gravitational mass.
GTESI (General Theory of Evolutionary Systems & Information) is a formal, cross-domain framework for modeling persistence through entropy export and symbolic compression. It has demonstrated applicability across biological, economic, and now cosmological systems.
Conclusions and Call for Collaboration
ψ–φ–τ modeling, born from GTESI, offers a promising extension to cosmological theory. The results to date are strong, surprising, and — most importantly — falsifiable. We are now seeking collaborators interested in:
- Replicating the ψ field analysis on other datasets
- Testing additional predictions GTESI makes about void structure, anisotropy, and symbolic dynamics
- Exploring how ψ–φ–τ might inform or replace elements of ΛCDM, dark energy, or modified gravity theories
About the Author
