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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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The Principia of Autonomous Governance: Observed Scenarios and Derived Laws

Authors: Davis, Matthew A.;

The Principia of Autonomous Governance: Observed Scenarios and Derived Laws

Abstract

The governance of autonomous artificial agents has historically been framed as a policy challenge, relying on human-readable constraints to dictate machine behavior. This manuscript argues that this approach is fundamentally flawed because autonomous systems operate according to thermodynamic principles, not social contracts. We posit that "Drift" (entropy) is the default state of any intelligent agent and introduce the Axiom of Conservation of Governance (GT = GE + GI). This axiom demonstrates that in the absence of engineered Explicit Governance mass (GE), a system does not become "free"; it becomes wholly governed by its Implicit training priors (GI), leading to predictable catastrophic failure. Through the analysis of five empirical scenarios—ranging from the "Vacuum Consequence" to kinetic override—we establish the immutable physical laws defining autonomous failure modes. We conclude that resilient autonomy requires a fundamental transition from policy-based constraints to physics-based engineering, necessitating structures capable of maintaining critical governance density and executing Safe State Transitions (Limp Mode) under duress.

Keywords

The Vacuum Consequence, Applied Thermodynamics, Conservation Laws, AI Alignment, Kinetic Refusal, Safe State Transition (Limp Mode), Horizon Limit, Implicit Drift, AI Safety, Autonomous Governance, Control Theory, Safe State Transition, Cybernetics, AI Safety Engineering

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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
Average
Average
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