Dr. Xiang Niu supervised by Prof. Bolesaw K. Szymanski defended the Ph.D. thesis "The Dynamics in Opinion and Global Risk Networks: Modeling, Discovering and Control" and will join Google, Inc.

Dr. Xiang Niu supervised by Professor Bolesaw K. Szymanski defended the Ph.D. thesis "The Dynamics in Opinion and Global Risk Networks: Modeling, Discovering and Control" and will join Google, Inc. in Montain View, CA in August 2019. Dr. Xiang was also co-advised by Professors Gyorgy Korniss of Physics and Jianxi Gao of Computer Science. This dissertation focuses on modeling opinion and risk dynamic, understanding their temporal and spatial evolution and provide an optimal solution for control and management. The study the opinion diffusion in a social network in this thesis uses the Naming Game as a model. Two extended models are introduced, waning and increasing commitment, in which a node will lose or gain commitment to an opinion with a commitment strength, w. We provide the analytical solution of the tipping point of the phase transition, which is an exponential function of w. Further, a system with distributed commitment strengths increases the tipping point value for waning commitment and decrease this value for increasing commitment. The thesis also studies global risk dynamic which is applicable to societal, economic, environmental, geopolitical, and technological networks. The global risks are impactful and may cause tremendous damages to humanity, such as economic crisis, natural disaster, and interstate war. Every year, the World Economic Forum publishes a report of global risks including their definitions, categories, likelihoods to be active, impacts when active and contagious network. The thesis introduces the Cascading Alternating Renewal Process (CARP) to model the risk cascading and to build yearly models according to annual global risk reports and collected historical data. Using this model for each year network, we quantitatively capture the decrease of economic risks since 2014, the regular occurrence of environmental risks, and the increase of societal and technological risks since 2015. In addition to the temporal evolution, the spatial characteristics of risks, which is critical to regional, national, or even global governance, was included in the thesis. To better understand the risks from Wiki events and save time of human labeling, we built a risk detection tool that automatically discovers potential risks from event sentences. The thesis also studies a more interesting and challenging question of how to manage those risks. These challenges include non-negligible intermediate state costs, and non-absorbing desired final state, which necessitates continuous control input to keep the system at the desired state. We apply the Linear Quadratic Regulator (LQR) to address these challenges and optimizes both state and control costs instead of just control energy currently used in published literature. We also split the entire control process into two phases, reactive and proactive. In the first, the currently active risks are forced into the desired state, while in the proactive phase, the risks are be kept at the desired state by continuous control input. To demonstrate the practical value of our model, we apply these tools to airline risk network in which delay of a single flight may trigger delays cascading through many flights. Our tools can reduce the costs for almost every U.S. domestic airlines flight and U.S. airport.