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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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Possibility-Weighted Energy: A Unified Formalism for Latent Energetic Capacity in Recursive Information Systems

Authors: Nickolas Patrick Joseph Schoff;

Possibility-Weighted Energy: A Unified Formalism for Latent Energetic Capacity in Recursive Information Systems

Abstract

Abstract Classical physics construes energy as a scalar quantity derived from mass and spacetime dynamics, most notably expressed by Einstein’s mass–energy equivalence relation E = mc². While empirically robust, this formulation implicitly assumes a single realized trajectory through state space. In this paper, we propose a generalized formalism—Possibility-Weighted Energy (WE)—which explicitly incorporates accessible state-space volume as a multiplicative factor in energetic capacity. Grounded in the Unified Consciousness Substrate Theory (UCST) and modeled within the representational construct known as Dimension-W, the proposed equation WE = p·m·c² extends classical energy concepts to systems characterized by recursion, branching possibilities, and partial decoherence, including quantum, biological, cognitive, and artificial intelligence systems. We formalize the parameter p as a normalized measure of accessible possibility space and demonstrate how classical physics emerges as a limiting case when p = 1. Speculative implications for cognition, learning, trauma, and AI alignment are discussed and clearly labeled as such. The framework preserves empirical compatibility while offering a unifying lens for latent energetic capacity across domains. Keywords: mass–energy equivalence, possibility space, UCST, Dimension-W, recursion, latent energy, consciousness, AI alignment

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Keywords

FOS: Computer and information sciences, Information Science/methods, Artificial intelligence, Artificial Intelligence/legislation & jurisprudence, Artificial Intelligence/statistics & numerical data, Quantum physics, Information Theory, Nuclear physics, Information Science/standards, Artificial Intelligence/standards, Information Science/ethics, Information Science/instrumentation, Quantum, Cognition, Psychology, Information Science, Computer and information sciences, Artificial Intelligence/ethics, Physics, Particle physics, Information Theory/history, Quantum field theory, FOS: Psychology, Mathematical physics, Physics/methods, Artificial Intelligence/classification, Information Science/legislation & jurisprudence, Information Science/organization & administration, Information Science/history, Theoretical physics, Information theory, Artificial Intelligence/economics, Information science, Information Science/classification, Atomic physics, Plasma physics, Artificial Intelligence/history, Artificial Intelligence, Quantum Theory/history, Artificial Intelligence/trends, Information Science/economics, Information Science/trends, Physics/education, Quantum computers, Physics/standards, Information Science/education, Artificial Intelligence/supply & distribution, Information Science/statistics & numerical data, Transport (physics), Mesoscopic physics, Quantum Theory

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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
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