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| Format: | Recurso digital |
| Language: | English |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20394642 |
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| _version_ | 1866902144052887552 |
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| author | Blanchette, Robert J. |
| author_facet | Blanchette, Robert J. |
| contents | <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20394642 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Attribution, Surplus, and Governance: Extending Structural Indeterminacy to the Economic Domain Blanchette, Robert J. structural indeterminacy AI governance surplus governability attribution prior condition non-conversion principle economic domain Magnifica Humanitas Catholic social teaching Rerum Novarum accountability causal attribution Structural Limit Theorem ABCF-v1 labor displacement provenance governance <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> |
| title | Attribution, Surplus, and Governance: Extending Structural Indeterminacy to the Economic Domain |
| topic | structural indeterminacy AI governance surplus governability attribution prior condition non-conversion principle economic domain Magnifica Humanitas Catholic social teaching Rerum Novarum accountability causal attribution Structural Limit Theorem ABCF-v1 labor displacement provenance governance |
| url | https://doi.org/10.5281/zenodo.20394642 |