Professor Vikram Krishnamurty,
Canada Research Chair in Statistical Signal Processing,
Department of Electrical and Computer Engineering,
University of British Columbia
Social Learning models for Interactive Social Sensing
Social Sensing has received wide attention in economics, social sciences and engineering. This talk describes Bayesian social learning models and stochastic control results or interactive social sensing. Individual agents perform social learning to estimate an underlying state of nature and thereby make local decisions. How can a global decision maker use these local decisions to optimize a utility function? Two examples are considered: The first example deals with the quickest detection/estimation problem when individual agents perform social learning. The second example deals with a global decision maker that optimizes a social utility function to delay herding amongst agents. In both examples, the optimal strategy of the global decision maker is unusual in that the stopping set is non convex. In the context of controlled sensing, these results show that global decision making based on local decisions of sensors (rather than Bayesian posteriors) can result in unusual behaviour.
Vikram Krishnamurthy received his from the Australian National University, Canberra, Australia, 1992. He is currently a Toer 1 Canada Research Chair holder at UBC. Prior to joining UBC he was Professor at the University of Melbourne, Australia. In 2009 and 2010, Dr Krishnamurthy served as distinguished lecturer for the IEEE signal processing society. From 2010-2012, he served as Editor in Chief of IEEE Journal Selected Topics in Signal Processing. He has served as associate editor for several journals including IEEE Transactions Automatic Control and IEEE Transactions on Signal Processing, He also currently serves on the editorial board of IEEE Signal Processing Magazine. Dr Krishnamurthy is a Fellow of the IEEE. Dr.Krishnamurthys current research interests include: statistical signal processing for wireless multi-media and sensor networks, computational game theory, stochastic optimization and scheduling, in radar and surveillance systems, stochastic dynamical systems for modeling of proteins and biosensors, and social learning and applications in math finance.
Invited by Professor Ravi Mazumdar