Dissertation NETWORK THEORY AND ITS APPLICATIONS IN ECONOMIC SYSTEMS

This dissertation covers the two major parts of my Ph.D. research: i) developing a theoretical framework of complex networks; and ii) applying statistical physics concepts and methods to quantitatively analyze complex systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted attack and find that when the highly-connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable, with pc significantly larger than zero. 2) We analytically demonstrate that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply concepts and methods developed in statistical physics to study economic systems, which also includes two chapters: 1) Centrality measures are widely studied in statistical physics to describe the importance of certain nodes in complex systems. We apply centrality measure concepts to study the influence of directors in the US corporate governance network, in which nodes represent directors and links between two directors represent their service on common company boards. 2) Using concepts of percolation and vi cascading failure, we study the robustness of the interconnected banking system as a complex network. With empirical banks’ balance sheet data in 2007 as input to the model, we find that our bipartite network model efficiently identifies a significant portion of the actual failed banks during the financial crisis between 2008 and 2011. The results suggest that complex network models could be useful for systemic risk stress testing of banking systems. We also find first-order-like phase transition in the banking system, which means that the banking system can experience an abrupt collapse under certain circumstances.