Prof. Anna Scaglione of Arizona State University

Opinion dynamics models aim is capturing the phenomenon of social learning through public discourse. While a functioning society should converge towards common answers, the reality often is characterized by divisions and polarization. This talk is structured in two parts. The first part reviews the key models that capture social learning and its vulnerabilities. In particular, we review models that explain the effect of bounded confidence and social pressure from zealots (i.e. fake new sources) and show how very simple models can explain the trends observed when social learning is subject to these phenomena. We also introduce mechanisms to achieve robust consensus, or detect the presence of sociopaths, and how their influence exposes trust different agents place on each other. The second part of the talk is concerned with network inference and describes how to formulate the problem of inferring the underlying influence of various social agents from observations of their opinions on various topics, posing it as a system identification problem. The lecture will provide theoretical limits based on the opinion dynamics models explored in the first part of the talk, as well as numerical results using the roll call data from the US Senate.

Biography:

Anna Scaglione (M.Sc.'95, Ph.D. '99) is currently a professor in electrical and computer engineering at Arizona State University.
Her research is rooted in statistical signal processing and spans a broad number of disciplines that relate to network science, from communication, control and energy delivery systems. Her most recent work in signal processing has focused on distributed learning and data analytics for signals that are driven by network processes. Dr. Scaglione was elected an IEEE fellow in 2011. She served in many capacities, primarily the IEEE Signal Processing Society. At present she is deputy EiC for the IEEE Transactions on Control Over Networked Systems. She received the 2000 IEEE Signal Processing Transactions Best Paper, the 2013, IEEE Donald G. Fink Prize Paper Award for the best review paper in that year in the IEEE publications. Her work with her student earned several conference papers awards, and also the 2013 IEEE Signal Processing Society Young Author Best Paper Award (Lin Li).