Enhancing the Efficiency of National R&D Programs Using Machine Learning-Based Anomaly Detection

Sang-Kyu Lee
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Sang-Kyu Lee: Korea Institute for Industrial Economics and Trade

Industrial Economic Review from Korea Institute for Industrial Economics and Trade

Abstract: This study is grounded on the premise that, given the transformative advances in artificial intelligence (AI) technologies occurring across the industrial landscape, AI tools should be actively implemented into the design and implementation of industrial policy. We argue that this is especially true for R&D policy, which is central to national competitiveness in science and technology, and which must consider multiple diverse variables, including the global economy, the overall industrial environment, corporate management, and technological capabilities.

For this study, I apply machine learning (ML)-based anomaly detection (AD) to analyze high-performing national R&D projects, and specifically assess ML-based AD that considers both input and output variables and analyzes structural patterns. Building on these analytical results, I propose firm-size-specific differentiated policy measures designed to enhance R&D performance.

The goal of this study is to establish a policy-decision framework that improves timeliness and precision in the operation and management of national R&D programs and, in the longer term, contributes to the realization of AI-based policy planning and operational management.

Keywords: machine learning; artificial intelligence; AI; anomaly detection; DEA; SHAP; research and development; R&D; government R&D; industrial policy; South Korea (search for similar items in EconPapers)
JEL-codes: I23 I28 O32 O38 (search for similar items in EconPapers)
Pages: 9
Date: 2025-10-31
New Economics Papers: this item is included in nep-ppm and nep-tid
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Published in KIET Industrial Economic Review Vol. 30, No. 5

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Persistent link: https://EconPapers.repec.org/RePEc:ris:kieter:021804

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