Prof. Boleslaw Szymanski presented a lecture "Failures, Dynamics, Evolution and Control of the Global Risk Network" at the Network Science Institute in Boston.

Prof. Boleslaw Szymanski presented a talk in the 3rd Annual Network Science Distinguished Speaker Lecture Series hosted by Network Science Institute at Northeastern University in Boston MA. The talk entitled "Failures, Dynamics, Evolution and Control of the Global Risk Network" was based on research that involved several Center's scientists including Xin Lin, Xiang Niu, Noemi Derzsy, Alaa Moussawi, Jianxi Gao and G. Korniss. Risks that threaten modern societies by form an intricately interconnected networks. The largest of the, the global risks network is defined by World Economic Forum experts. We model dynamics of this network using Cascading Alternating Renewal Processes (CARP) with variable intensities driven by hidden values of exogenous and endogenous failure probabilities. The maximum likelihood evaluation is used to find the optimal model parameters based on the expert assessments and historical status of each risk. This approach enables analysts to analyze risks that are particularly difficult to quantify, such as geo-political or social risks in addition to more quantitative risks such as economic, technological and natural. In the talk, we describe model dynamics and discuss how to use the model to provide quantitative means for measuring interdependence and materialization of risks in the network. We also talk about limits of the predictability of the system parameters from historical data and model ability to recover hidden variable. Then, we describe how the network evolved recently by comparing steady state which would be reached if the risks were left unabated at different time points. Finally, we also analyze the model resilience and optimal control. Our findings elucidate the identity of risks most detrimental to system stability at various points in time. The model provides quantitative means for measuring the adverse effects of risk interdependence and the materialization of risks in the global risk network.