Abstract: Many real-world dynamic decision-making problems consist of multiple decision-makers with asymmetric information. Some examples include Markets, social learning, traffic management, autonomous vehicles, cyber-physical systems, internet of things and many more. In these systems, there are multiple decision-makers (DMs) who make some common and private observations of the 'state' of the systems with the goal to minimize their own cost (dynamic games) or total cost incurred by everybody (dynamic teams).
In this talk, I will present a general sequential decomposition framework to study such problems. This framework extends currently known results in decentralized stochastic control for team problems. For strategic users, it presents a novel methodology to compute (Markovian) Perfect Bayesian equilibria (PBE), which was an open problem in the theory of dynamic games. I present a running public-goods example to study its PBE.
In general, our results extend the ideas of dynamic programming to general multi-agent dynamic optimization problems to study 'signaling' behavior i.e. how players' actions reveal their private information in the system which affects other users' utilities.
Bio: Deepanshu received the BTech degree in electronics and communication engineering from the Indian Institute of Technology, Guwahati, India in 2009, and the M.S. degrees in Mathematics, and M.S. and Ph.D. degrees in electrical engineering and computer science and from University of Michigan, Ann Arbor in 2012, 2010 and 2016, respectively. He is currently a postdoctoral research associate at Northwestern University. His research interests include dynamic games, stochastic control, information theory, applied probability.