Complexity, innovation and economic growth: The competitive network of innovation and organizational size and growth in innovation

The agglomeration, industrial concentration and mobility of the population between different geographic locations is tightly connected to organizational activity and economic growth. A full understanding of organizational and geographic activity involves understanding associated economic driving forces (e.g., innovation and technology). This dissertation examines the innovation process, technological advancement and resultant economic growth from the perspective of complexity, complex systems and complex networks.;First, the complex nature and structure of the network of innovation as characterized by patent citations, invention collaboration and patent agglomeration is examined. All three networks display significant signs of complexity, self-organization, and scaling behavior, specifically small world and approximate scale free behavior. The competitive network of innovation is driven by the goal of achieving a competitive advantage, which is characterized by preferential and assortative behavior by inventors and invention projects as they seek to optimize performance, overcome knowledge resource constraints and assess risks and rewards. Innovation displays a hierarchical organizational structure where star performers and technology projects drive innovation and technological advancement.;Next, the size distribution and growth dynamics of economic activity is investigated using the number and size of a firm's and city's constituent 'innovation' components (patents, citations and inventors). The size distribution of firms and cities is a Zipf distribution, with size inversely proportional to rank. Growth rates follow Gibrat's 'weak law' of mean proportionate growth, where expected growth is independent of size, though survival increases with size. Growth rate variance differs from Gibrat's 'strong law' indicating a negative scaling relationship, dependent on size, and consistent with a hierarchical model of the internal structure of the organization. The distribution of growth rates follows a Laplace distribution with power law tails dominated by increasingly smaller firms and cities, and center composed of relatively larger entities. Serial correlation reveals persistent asymmetries in growth dynamics. Smaller firms and cities are more likely to experience negative correlation, while larger entities display positive correlation. Those that experience average growth rates are more likely to not experience any serial correlation, while high growth rate entities experience significant negative correlation, particularly fast growth small firms and cities whose growth patterns are erratic.