Beyond the Black Box: Nonlinear Regimes and Explainable AI in Global Innovation Systems

This study, which uses the 2025 Global Innovation Index dataset, examines the structural architecture of global innovation systems, using Knowledge and Technology Outputs (KTO) as the dependent variable to measure the level of innovation. It aims to identify the latent dimensions of the global innovation system, the heterogeneity of global innovation regimes, and the determinants of global innovation outputs. The study adopts a holistic approach, including PCA, K-means clustering, machine learning algorithms such as MLP, and explainable artificial intelligence methods such as SHAP and LIME. The study finds that the global innovation system is highly concentrated, with 80.22% of the total variance explained by a single component. In addition, the study identifies five sharply differentiated global innovation regimes with near-perfect separability, achieving up to 100% accuracy. The nonlinear MLP model demonstrates strong predictive performance (R2 = 0.8836), with Business Sophistication as the main factor affecting KTO, followed by Infrastructure and Human Capital. Explainability analysis shows high consistency between SHAP and LIME (ρ=0.999) and a highly centralized interaction structure, in which a few dimensions have a strong impact on innovation performance. The structural paradigm of the global innovation system is hybrid, with a linear backbone and nonlinear interactions coexisting. This study has contributed to methodological development in the field of innovation research and has provided insights into the development of more precise and effective innovation policy.