Socio-technological Networks and Decision Making

Abstract
We will overview the role of DeGroot’s opinion dynamic model for learning and decision making in a system of agents communicating over a network. We start with the classical DeGroot model and, then, use it as the main mechanism for solving optimization problems and games on networks.  We then consider DeGroot’s model in presence of malicious agents and consider a trust-based approach for reaching an agreement among legitimate agents via statistical learning. 

Bio
Angelia Nedich has a Ph.D. from Moscow State University, Moscow, Russia, in Computational Mathematics and Mathematical Physics (1994), and a Ph.D. from Massachusetts Institute of Technology, Cambridge, USA, in Electrical and Computer Science Engineering (2002). She has worked as a senior engineer in BAE Systems North America, Advanced Information Technology Division at Burlington, MA. Currently, she is a faculty member of the school of Electrical, Computer, and Energy Engineering at Arizona State University at Tempe. Prior to joining Arizona State University, she has been a Willard Scholar faculty member at the University of Illinois at Urbana-Champaign. She is a recipient (jointly with her co-authors) of the Best Paper Award at the Winter Simulation Conference 2013 and the Best Paper Award at the International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) 2015.  Her general research interest is in optimization, large-scale complex systems dynamics, variational inequalities, and games.