斯坦福大学发布《全球人工智能实力排行榜》（英）-2024.11-47页

continues to evolve rapidly,the need for robust tools to measure and compare the AI capabilities ofdifferent countries has become increasingly evident.The OECD et al.[27]have laid down significant foundational work in the study of composite indexcreation.Their guidance stresses the importance of a coherent theoretical structure,meticulous dataselection and transformation,and robustness checks to ensure the reliability of these indicators.Similarly,Nardo et al.[26]discuss the potential pitfalls of selecting,normalizing,weighting,andaggregating indicators.They emphasize the importance of transparency and consistency in themethodology,which are crucial for enhancing the interpretability and comparability of compositeindices.More recent advances in the methodological framework for composite indicators are discussed inthe comprehensive review by Greco et al.[21].This review highlights the evolution in the adoptionand methodological refinement of composite indicators due to their increased popularity in variousresearch fields.Greco et al.[21]specifically focused on important aspects,such as weighting andaggregation,areas that attract substantial criticism and suggest avenues for future research.Theirwork explores the robustness analysis that follows the construction of these indicators,a lessexplored but significant phase,highlighting the need for robust methodologies that can withstandscrutiny and provide reliable and interpretable results.In addition to foundational knowledge,the European Commission's COIN Tool User Guide [32]provides practical guidance for building composite indicators that are specifically designed forpolicy analysis.This guide serves as a useful resource for researchers and policymakers who aimto apply these metrics to evaluate and compare policy impacts across various regions or countries.Drawing on these lessons,the Global AI Vibrancy Tool (GVT)applies best practices from theliterature,which ensures a solid conceptual framework,transparent data handling,and thoroughrobustness checks.More details are provided in the Methodology Section.There is also a well-established tradition of creating indices to track the technological progress ofdifferent nations.For example,the Technology Achievement Index(TAD,developed by Desai et al.[14],is a foundational framework for measuring cross-country technological advancement.TheTAI evaluates countries according to several dimensions,including technology creation,diffusionand human skill development.This index has set a foundation for more specialized tools designedto assess AI capabilities.Incekara et al.[23]developed TAI-16 from the original TAI,categorizing countries by their techadoption and innovation.This index stresses how the dynamic pace of technological developmentrequires frequently updated criteria.TAI-16 also shows how countries adapt to technologicalchanges and measures AI readiness.Archibugi et al.10 offer an extensive analysis of synthetic indicators for measuring the tech-nologi