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Seminar: Information and Incentives in Learning and Decision Making on Networks

Parinaz Naghizadeh, Purdue University and Princeton University Edge Lab
Wednesday, February 20, 2019, 4:00 pm to 5:00 pm
198 Baker Systems
1971 Neil Avenue
Columbus, OH 43210

Seminar by Parinaz Naghizadeh

Postdoctoral Research Associate

Purdue University and Princeton University Edge Lab

 

Networks play a central role in determining the outcomes of a variety of social and economic interactions. Examples include public good provision, trade, investment decisions, and learning by teams of agents, in network environments. In this talk, I analyze the role of information and incentives in distributed learning and decision making in such problems.  


I will first discuss the role of information sharing in a multi-agent (reinforcement) learning problem. We study learning and decision making by agents who have heterogeneous information about their partially observable environment. We identify two benefits of information sharing between agents: it facilitates coordination among them, and further enhances the learning rate of both informed and less informed agents. We show however that these benefits will depend on the communication  timing, in that delayed information sharing may be preferred in certain scenarios. 


I will then present a framework for characterizing​ the effects of the network topology on outcomes of strategic decision making over networks. Specifically, we establish a connection between the equilibrium outcomes of network games with non-linear (resp. linear) best-response functions, and variational inequality (resp. linear complementarity) problems. Through these connections, we outline conditions for existence, uniqueness, and stability of equilibria in these games, extending several existing results in the literature. 

Parinaz Naghizadeh is a postdoctoral research associate in the Department of Electrical and Computer Engineering at Purdue University and Princeton University Edge Lab. She received her Ph.D. in electrical engineering from the University of Michigan in 2016, M.Sc. degrees in electrical engineering and mathematics, both from the University of Michigan, in 2013 and 2014, respectively, and her B.Sc. in electrical engineering from Sharif University of Technology, Iran, in 2010. Her research interests are in network economics, learning theory, game theory, reinforcement learning, and data analytics. She was a recipient of the Barbour Scholarship in 2014, a finalist for the ProQuest Dissertation Award in 2016, and a Rising Stars in EECS in 2017.