We define a novel quantitative strategy inspired by the ecological notion of nestedness to single out the scale at which innovation complexity emerges from the aggregation of specialized building blocks. Our analysis not only suggests that the innovation space can be interpreted as a natural system in which advantageous capabilities are selected by evolutionary pressure, but also that the emerging structure of capabilities is not independent of the scale of observation at which they are observed. Expanding on this insight allows us to understand whether the capabilities characterizing the innovation space at a given scale are compatible with a complex evolutionary dynamics or, rather, a set of essentially independent activities allowing to reduce the system at that scale to a set of disjoint non interacting sub-systems. This yields a measure of the innovation complexity of the system, i.e. of the degree of interdependence between the sets of capabilities underlying the system's building blocks.
Owning the Intelligence: Global AI Patents Landscape and Europe's Quest for Technological Sovereignty
Laboratory for the Analysis of Complex Economic Systems (AXES)（复杂经济系统分析实验室）；IMT School for Advanced Studies Lucca（拉瓜迪亚高级研究学院）；Institute for Human-Centered AI（以人为本的人工智能研究所）；Stanford University（斯坦福大学）
A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
Arizona State University（亚利桑那州立大学）；University of Wisconsin（威斯康星大学）；University of Chicago（芝加哥大学）；University of California Santa Barbara（加州大学圣巴巴拉分校）；University of Pennsylvania（宾夕法尼亚大学）
The Explore of Knowledge Management Dynamic Capabilities, AI-Driven Knowledge Sharing, Knowledge-Based Organizational Support, and Organizational Learning on Job Performance: Evidence from Chinese Technological Companies
