Attribution, Surplus, and Governance: Extending Structural Indeterminacy to the Economic Domain
Description
The Structural Limit Theorem establishes that causal attribution of system-generated output is formally indeterminate under the constrained intervention conditions typical of real-world institutional deployment. Prior work has mapped this result to legal and creative domains. This paper extends the theorem to the economic domain, where its implications have received no formal treatment. When any system — mechanical, algorithmic, or artificial intelligence — generates measurable surplus under conditions of attribution indeterminacy, that surplus is not merely maldistributed. It is structurally ungovernable in the absence of an explicit attribution prior condition.
The paper introduces three concepts: the Surplus Governability Condition, which holds that surplus is governable only where attribution is resolvable; the Non-Conversion Principle, which holds that detected surplus cannot legitimately acquire economic standing through accumulation or metric visibility alone; and the Attribution Prior Condition as applied to the economic domain. AI systems are treated as the current and most urgent instantiation of a general structural problem that includes prior automation waves and any future system generating output under attribution uncertainty.
The paper maps these concepts against the five principal concerns of Magnifica Humanitas (Leo XIV, 2026), demonstrating that the encyclical's moral framework and formal structural analysis arrive at convergent positions through independent derivational paths. The convergence is offered as epistemological evidence that the structural requirements identified are necessary rather than contingent features of the governance problem both frameworks address.
The paper is published with its own provenance record — a Declared-Origin Gate declaration, Conception Event Anchor log, and AI-Assisted Work Admissibility Packet (supplementary files) — demonstrating in practice the Attribution Prior Condition and Non-Conversion Principle it argues for in theory. This is the fourth paper in a series extending the Structural Limit Theorem to specific governance domains.
Notes (English)
This paper is the fourth in a series extending the Structural Limit Theorem (Blanchette, 2026a) to specific governance domains. The series covers: (1) foundational theorem and ABCF-v1 architecture; (2) legal domain extension; (3) creative domain extension; (4) economic domain extension (this paper).
The paper introduces three concepts to the formal AI governance literature for the first time: the Surplus Governability Condition, the Non-Conversion Principle, and the Attribution Prior Condition as applied to the economic domain. These concepts are stated at the level of abstraction required to establish prior art. Full implementation architecture is the subject of ongoing research.
The Declared-Origin Gate (DOG) and AI-Assisted Work Admissibility Packet (AWAP) architectures cited in Section 5 are published under CC BY 4.0 at GitHub (rjblanchette/DOG and rjblanchette/AWAP) and are patent pending.
The supplementary files — AWAP_surplus_paper_v1.0.pdf and CEA_log_surplus_paper_v1.0.pdf — constitute the complete provenance record (genesis chain) for this paper under the ABCF-v1 governance architecture. They are published alongside the paper as a demonstration of the Attribution Prior Condition and Non-Conversion Principle argued in Section 5.
Submitted from a position of institutional independence. No funding, institutional affiliation, or conflicts of interest to declare.