[2603.21639] Engineering Distributed Governance for Regional Prosperity: A Socio-Technical Framework for Mitigating Under-Vibrancy via Human Data Engines

[2603.21639] Engineering Distributed Governance for Regional Prosperity: A Socio-Technical Framework for Mitigating Under-Vibrancy via Human Data Engines

arXiv - Machine Learning 4 min read

About this article

Abstract page for arXiv paper 2603.21639: Engineering Distributed Governance for Regional Prosperity: A Socio-Technical Framework for Mitigating Under-Vibrancy via Human Data Engines

Computer Science > Computers and Society arXiv:2603.21639 (cs) [Submitted on 23 Mar 2026] Title:Engineering Distributed Governance for Regional Prosperity: A Socio-Technical Framework for Mitigating Under-Vibrancy via Human Data Engines Authors:Amil Khanzada, Takuji Takemoto View a PDF of the paper titled Engineering Distributed Governance for Regional Prosperity: A Socio-Technical Framework for Mitigating Under-Vibrancy via Human Data Engines, by Amil Khanzada and 1 other authors View PDF Abstract:Most research in urban informatics and tourism focuses on mitigating overtourism in dense global cities. However, for regions experiencing demographic decline and structural stagnation, the primary risk is "under-vibrancy", a condition where low visitor density suppresses economic activity and diminishes satisfaction. This paper introduces the Distributed Human Data Engine (DHDE), a socio-technical framework previously validated in biological crisis management, and adapts it for regional economic flow optimization. Using high-granularity data from Japan's least-visited prefecture (Fukui), we utilize an AI-driven decision support system (DSS) to analyze two datasets: a raw Fukui spending database (90,350 records) and a regional standardized sentiment database (97,719 responses). The system achieves in-sample explanatory power of 81% (R^2 = 0.810) and out-of-sample predictive performance of 68% (R^2 = 0.683). We quantify an annual opportunity gap of 865,917 unrealized visits, equi...

Originally published on March 24, 2026. Curated by AI News.

Related Articles

Ai Safety

Bias in AI: Examples and 6 Ways to Fix it in 2026

AI bias is an anomaly in the output of ML algorithms due to prejudiced assumptions. Explore types of AI bias, examples, how to reduce bia...

AI Events · 36 min ·
Llms

[R] I built a benchmark that catches LLMs breaking physics laws

I got tired of LLMs confidently giving wrong physics answers, so I built a benchmark that generates adversarial physics questions and gra...

Reddit - Machine Learning · 1 min ·
Machine Learning

We need to teach AI the essence of being human to reduce the risk of misalignment

One part of the alignment problem is that AI does not genuinely understand what it's like to live in the world, even though it can descri...

Reddit - Artificial Intelligence · 1 min ·
California’s New AI Regulations Take Effect Oct. 1: Here’s Your Compliance Checklist
Ai Safety

California’s New AI Regulations Take Effect Oct. 1: Here’s Your Compliance Checklist

California's new regulations on automated decision systems take effect on October 1, affecting all employers and requiring compliance wit...

AI Events · 4 min ·
More in Ai Safety: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime