Decision Making Under Uncertainty in Networks of Strategic Agents

Many complex systems involve large-scale interconnection of agents with heterogeneous information who interact strategically. Such systems are prevalent in diverse domains ranging from cyber-physical systems, transportation networks, financial markets, consumer networks, and more broadly, complex social and economic networks. The strategic interactions of the agents affect both the direct and indirect (inferred) flow of information, hence introducing new challenges to modeling, analysis, and control. In my talk, I discuss several applications of this nature, ranging from endogenous spreading processes in large networks, to dynamic information aggregation and learning, to stochastic optimal control with non-classical information structure (e.g., Witsenhausen's counterexample). In all such cases, I present a collection of ideas and methods from game theory, networks, microeconomics, and applied probability theory to address the challenges resulting from the interplay between the information flow and actions.
Amir Ajorlou is a postdoctoral research fellow at the Institute for Data, Systems and Society at MIT. He received his BS from Sharif University of Technology in Tehran, Iran, and his PhD in electrical and computer engineering from Concordia University in Montreal, Canada, in 2013. He has been the recipient of several prestigious awards, including two gold medals in the International Mathematical Olympiad (IMO), Concordia University Doctoral Prize in Engineering and Computer Science, Governor General of Canada Academic Gold Medal, and NSERC Postdoctoral Fellowship. His current research lies at the intersection of decision theory, network economics, and information economics, where he applies tools and techniques from optimization, game theory, and applied probability theory to decision making under uncertainty in networks.