Entropy Attractor Intelligence: A Philosophical Foundations Note Authors/Creators De Villa, Agerico (Other)

This paper advances Entropy Attractor Intelligence (EAIP) as an alternative to the classicaltruth-seeking paradigm that has dominated epistemology and scientific method since antiquity.Building on the Bridge360 Metatheory Model, EAIP reconceives intelligence as a navigationprocess rather than a representational or correspondence-based achievement. It holds thatintelligent systems—biological, social, or artificial—optimize survival not through accuratemapping of a presumed external reality but through the minimization of entropic blowoutunder finite cognitive and operational budgets.The account integrates three formally compatible components: (1) Rule-of-InferenceMemetics, which treats inference rules (valid or invalid) as physical, tokenizable replicators thatpropagate across neural, cultural, and computational substrates; (2) Entropy-Driven Altruism,derived from combining Kropotkin’s Mutual Aid thesis with Shannon entropy, explaining whycooperative aggrupations outperform purely individual competition; and (3) Attractor-basedNavigation, which replaces truth-correspondence with trajectory stability governed by budget(B), tolerance (ε), and fragility (F) constraints.EAIP provides a unified explanatory framework for phenomena ranging from political memeticcontagion to ecological cascades and technological risk amplification. More importantly, itreframes the problem of Artificial General and Superintelligence: LLMs already function asentropy-minimizing, attractor-sensitive systems, making them the first safe laboratory forentropy-bounded engagement rather than control or alignment.The paradigm is truth-neutral, substrate-agnostic, and consistent with a post-correspondencelinguistic space in which “true,” “false,” and “reality” have no operational role. Instead,coherence, survivability, and entropic governance become the primary epistemic criteria. EAIPtherefore offers a metatheoretic foundation for understanding intelligence—human orartificial—as a thermodynamically constrained, memetically structured, attractor-navigatingprocess.Mathematical expressions are in marked down format.

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Publication Details

Journal
Figshare
Published
2026-10-29
DOI
https://doi.org/10.6084/m9.figshare.34846659
Primary Topic
Complex Systems and Dynamics
Type
preprint
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Entropy Attractor Intelligence: A Philosophical Foundations Note Authors/Creators De Villa, Agerico (Other)

Agerico De Villa
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Figshare
Complex Systems and Dynamics
preprint

Entropy Attractor Intelligence: A Philosophical Foundations Note Authors/Creators De Villa, Agerico (Other)

Agerico De Villa
preprint en

Abstract

This paper advances Entropy Attractor Intelligence (EAIP) as an alternative to the classicaltruth-seeking paradigm that has dominated epistemology and scientific method since antiquity.Building on the Bridge360 Metatheory Model, EAIP reconceives intelligence as a navigationprocess rather than a representational or correspondence-based achievement. It holds thatintelligent systems—biological, social, or artificial—optimize survival not through accuratemapping of a presumed external reality but through the minimization of entropic blowoutunder finite cognitive and operational budgets.The account integrates three formally compatible components: (1) Rule-of-InferenceMemetics, which treats inference rules (valid or invalid) as physical, tokenizable replicators thatpropagate across neural, cultural, and computational substrates; (2) Entropy-Driven Altruism,derived from combining Kropotkin’s Mutual Aid thesis with Shannon entropy, explaining whycooperative aggrupations outperform purely individual competition; and (3) Attractor-basedNavigation, which replaces truth-correspondence with trajectory stability governed by budget(B), tolerance (ε), and fragility (F) constraints.EAIP provides a unified explanatory framework for phenomena ranging from political memeticcontagion to ecological cascades and technological risk amplification. More importantly, itreframes the problem of Artificial General and Superintelligence: LLMs already function asentropy-minimizing, attractor-sensitive systems, making them the first safe laboratory forentropy-bounded engagement rather than control or alignment.The paradigm is truth-neutral, substrate-agnostic, and consistent with a post-correspondencelinguistic space in which “true,” “false,” and “reality” have no operational role. Instead,coherence, survivability, and entropic governance become the primary epistemic criteria. EAIPtherefore offers a metatheoretic foundation for understanding intelligence—human orartificial—as a thermodynamically constrained, memetically structured, attractor-navigatingprocess.Mathematical expressions are in marked down format.

Figshare
Complex Systems and Dynamics
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