Research Summary: Unsnarling the Red Tape: Computational Infrastructure for Regulatory Systems

Regulatory complexity is identified as a significant impediment to innovation, institutional responsiveness, and economic growth, potentially reducing the U.S. GDP growth rate by almost one percentage point annually. This research proposes that computational approaches, including knowledge representation, artificial intelligence, natural language processing, and cryptography, can mitigate 'red tape' by enhancing efficiency, transparency, and responsiveness within regulatory systems. It outlines a framework to understand and address regulatory friction, aiming to support compliance, analysis, and reform through modernization.

Why it matters

The accumulation of regulatory complexity presents a systemic drag on economic growth and innovation, impacting national competitiveness and societal well-being. Adopting computational infrastructure for regulatory systems offers a strategic pathway to enhance efficiency, transparency, and responsiveness, thereby fostering a more agile and innovative environment for all sectors.

Key insights

• Regulatory complexity negatively impacts innovation, institutional responsiveness, and long-term economic growth.
• Regulatory accumulation is estimated to reduce the U.S. GDP growth rate by nearly a full percentage point annually.
• Computational approaches (knowledge representation, AI, NLP, cryptography) can reduce regulatory 'red tape'.
• These computational methods can improve efficiency, transparency, and institutional responsiveness in regulatory systems.
• A framework is needed to understand sources of regulatory friction and how to mitigate them.
• Modernization of regulatory systems requires a computational infrastructure to support compliance, analysis, and reform.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.28482