About this Research Topic
This Research Topic focuses on metrics, models, and visualizations designed to quantify and communicate the structure and dynamics of complex innovation systems. Conventional measures that are often used as indicators of activity and performance are not able to accurately capture self-organizing and frequently scale-invariant emergent properties.
An innovation system is conceptualized as a network of organizations that creates, diffuses, and exploits scientific and technological knowledge in order to develop, adapt, imitate, promote, or adopt knowledge and technologies that are novel within a given context.
Complex systems exhibit emergent properties, that is, they are not properties of any component of the system but are features of the system as a whole. The constituents of complex systems are interdependent and interact in nonlinear ways giving rise to novel and emergent dynamics frequently leading to scale invariant emergent properties. The structure of, and characteristics of scale-invariant emergent properties are recursive and similar over a broad range of spatial and temporal scales.
Complex adaptive systems, such as innovation systems, survive and evolve using the interplay between competition and co-operation at different scales. Co-operation in these systems can lead to self-organization, that is, pattern formation through interactions within the system, without intervention by external directing influences. Self-organization, closely related to scale-invariance, may in a natural way be among the most important mechanisms leading to scale invariance in complex systems.
Over the past few decades researchers of complex physical, biological, social, and economic systems have developed novel tools and techniques for quantifying, characterizing, modelling, and visualizing emergent properties. These range from principles adopted from statistical mechanics and agent-based modelling to the use of AI and dynamic visualization software.
Researchers are invited to submit articles on metrics, models and visualizations that quantify and characterize emergent and scale-invariant properties of complex innovation systems using tools and techniques ranging from, but not limited to, informetrics, statistical mechanics to modelling, social science, and data visualization. A non-extensive list of relevant topics includes the following:
• Global, national, and regional complex innovation systems
• Economics of innovation and technological change
• Performance and evaluation indicators
• Knowledge, collaborative, and citation networks
• Patents and publishing
• Diffusion of knowledge and technology
• Growth dynamics of sectors, firms, and HEI institutes
• Cities and urban engines of innovation and wealth
• Personnel mobility
• Process, product, and service innovation
• Open source innovation/crowding sourcing
• Institutional and firm linkages
• Innovation in work & education
• Pandemics/covid-19 and complex innovation systems
Keywords: knowledge dissemination, technological knowledge, scientific knowledge, complex system, innovation system, indicator
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