Unsnarling the Red Tape: Computational Infrastructure for Regulatory Systems

arXiv:2609.28482v1 Announce Type: new Abstract: Regulatory complexity is increasingly recognized as an impediment to innovation, institutional responsiveness, and long-run economic growth, with regulatory accumulation estimated to reduce the U.S. GDP growth rate by nearly a full percentage point annually. This paper argues that computational approaches--including knowledge representation, artificial intelligence, natural-language processing, and cryptography--can help reduce forms of regulatory "red tape" by improving efficiency, transparency, and institutional responsiveness. We develop a framework for understanding the sources of regulatory friction and how they can be mitigated to support compliance, analysis, and reform. We further argue that effective modernization requires treating regulation not merely as legal text, but as a complex institutional and informational system that can be partially represented, analyzed, coordinated, and improved computationally while preserving legal legitimacy and procedural accountability.

The paper suggests AI can improve regulatory systems, but evidence is speculative.

The text claims computational methods can reduce regulatory friction, improving efficiency and economic growth. This is a materially optimistic implication as it could help address economic drag and institutional delays. Evidence is from an academic framework rather than deployment, so confidence is moderate.

This is a conceptual framework paper, not an empirical demonstration, proposing AI could ease regulatory friction. The GDP claim is cited from elsewhere and the paper's own contribution is speculative and forward-looking, warranting modest positive score with low confidence.

The paper proposes computational approaches, including AI, to reduce regulatory 'red tape' and improve efficiency and transparency. It claims this will boost economic growth and innovation. The implications are positive but the paper is a theoretical framework, not a report on implemented results.

The paper argues that AI and related technologies can significantly reduce regulatory burdens. This implies substantial economic benefits and increased institutional efficiency. The claims are supported by specific mechanisms and a clear framework.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   2184c0bc-b9f1-40b5-a11a-1c44712159c2
           https://arxiv.org/abs/2609.28482
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      +16  (Mixed or uncertain)
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CONFIDENCE 72%