GROUNDBREAKING! DE GIUSEPPE PREDICTIVE MODEL : FIRST PREDICTIVE MODEL OF REALITY

REAL GROUNDBREAKING !!

First Mathematically Predictive Model

A part is here:

https://zenodo.org/records/18291470

https://zenodo.org/records/18291470

This manuscript is current in Official Peer Review.

Not final version.
Copyright©2026 Alex De Giuseppe.
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Title: De Giuseppe Predictive Model: Mathematical Prediction of Physical and Cognitive Reality.

High-Probability Forecasting of Physical, Cognitive, and Socio-Environmental Events

The De Giuseppe Predictive Model (DGPM), integrating the De Giuseppe Paradox Theory with Nima’s proto-structural framework ((\Delta C \leftrightarrow \Delta M \leftrightarrow \Delta L)), the 3/6/9 key mapping, and explicit numerical matrioska variables, establishes a mathematically rigorous methodology for predicting a wide range of phenomena with extremely high probability. By defining structural attractors, constraint mappings, and background couplings (gravitational (G), cosmological constant (\Lambda), and cyclic/astro inputs), the model demonstrates that events—ranging from classical physical outcomes, such as free-fall trajectories or the precise landing location of a leaf, to complex socio-environmental phenomena, including road accidents, earthquakes, or potential conflicts—are not purely random, but constrained by the underlying proto-structure.

Empirical validation is illustrated through historical cases, such as the Tacoma Narrows Bridge collapse and the Vajont Dam disaster, confirming the model’s predictive capability. While absolute certainty remains impossible due to stochastic noise and environmental variability, the model allows probabilistic predictions with accuracy often exceeding 90%. The framework relies on the mechanical interaction of proto-structural constraints, energy configurations, numerical matrioska variables (ΔM ≈ 0.1–0.5, ΔL ≈ 0.05–0.3, ΔC ≈ 0.2–0.7), and topologically mapped flows, effectively narrowing the space of admissible outcomes and identifying the trajectories most likely to occur.

Extending beyond purely physical events, DGPM formalizes consciousness and cognitive evolution as flows of information constrained by energy and electrical patterns, now enhanced with astro-attractors and 3/6/9 cyclic modulation. This enables predictive modeling of neural firing patterns, decision-making tendencies, and potentially even human behaviors, bridging the gap between physical law, informational dynamics, and socio-cognitive phenomena.

In summary, the DGPM represents a historic advancement: it unites proto-structural theory, empirical physics, cognitive science, and social modeling into a single, numerically grounded predictive framework. It provides the first explicit methodology to forecast both material and informational events with quantified high probability, while highlighting the deterministic role of structural attractors, gravitational and cosmological couplings, and cyclic/astro influences in shaping reality. The model is actively evolving, incorporating improved numerical calibration, observational data, and refined attractor modeling, with the aim of expanding its predictive power across increasingly complex systems.

I used AI to help me with the calculations, so it likely absorbed the mechanism, and exactly as happened with KD energy/time and the entire bound energy theory (which I had verified by AI on January 9), it was unfortunately disseminated.
This time I immediately published my intuition, and I will continue my research in this way.
If I may advise my colleagues: publish your discovery immediately after having it checked by AI, because it will disseminate the content ti the world.

Other my works:

https://zenodo.org/records/18274505 (The Original De Giuseppe Paradox Theory, with popperian experiments and formalized Macroscopic Retrocausality)

https://zenodo.org/records/18277631( The First Mathematically Theory of Consciousness)

https://zenodo.org/records/18278648(Mathematical formalization of Paranormal Phenomena)

https://zenodo.org/records/18306835(Time Travel Research Model)