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March 31, 20260 citationsOpen Access

Ontological Commitments in Formal Models of Artificial General Intelligence

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SSSamuel G. da Silva

Key Points

  • This research investigates the implicit ontological commitments in the AIXI and Gödel Machine frameworks of AGI.
  • Utilized Quine's criterion of ontological commitment to analyze AGI frameworks.
  • Characterized each framework with an ontological signature Ω(F) = ⟨α, ω, μ, ι⟩.
  • Established the Dichotomy of Universality theorem (Theorem 4.10) to illustrate incompatibilities.
  • Identified that normative universality (D1) is incompatible with formal self-foundation (D2) and computational realizability (D3).
  • Demonstrated that AIXI meets D1 but suffers from incomputability issues.
  • Showed that the Gödel Machine meets D2 and D3 but faces incompleteness challenges.

Abstract

This paper examines the ontological commitments implicit in the two canonical formal frameworks of Artificial General Intelligence — AIXI and the Gödel Machine — using Quine's criterion of ontological commitment. We propose an ontological signature Ω(F) = ⟨α, ω, μ, ι⟩ characterizing each framework's presuppositions about agency, domain structure, internal states, and the formal criterion of intelligence. The central result is the Dichotomy of Universality (Theorem 4.10): normative universality (D1) is formally incompatible with both formal self-foundation (D2) and computational realizability (D3), partitioning the space of AGI frameworks into two irreconcilable blocks. AIXI satisfies D1 at the cost of incomputability (Turing barrier); the Gödel Machine satisfies D2∧D3 at the cost of incompleteness (Gödel barrier). We argue that the structural limits of each framework are not contingent technical deficiencies but necessary logical consequences of their ontological commitments — a thesis we term ontological determination. The dispute between AGI programs is therefore not merely technical, but grounded in inescapable ontological choices.

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Cite This Study

Samuel G. da Silva (2026) studied this question.

synapsesocial.com/papers/69cb64f0e6a8c024954b900fhttps://doi.org/10.5281/zenodo.19320203
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