Dynamic games with asymmetric information appear in many social-economic contexts. In these games, multiple agents/decision makers, interact repeatedly in a changing environment. Agents have different information and seek to optimize their respective long-term payoffs. Examples include market competition, cyber-security, and transportation networks. In some settings, agents can form groups, or teams. The agents in the same group share a common goal but may have different information available to them. In this talk, I focus on a class of stochastic dynamic games among teams with asymmetric information, where members of a team share their observations internally with a delay of d>0. I will describe a general approach to characterize a subset of Nash Equilibria where the agents can use a compressed version of their information, instead of the full information, to choose their actions. Our results highlight the tension among compression of information, existence of (compression based) equilibria, and backward inductive sequential computation of such equilibria in stochastic dynamic games with asymmetric information. This is joint work with Hamidreza Tavafoghi, Vijay Subramanian, Ashutosh Nayyar, and Demosthenis Teneketzis.
Bio: Dengwang Tang is a Ph.D. student at Electrical and Computer Engineering Department of University of Michigan. He works with Prof. Vijay Subramanian. He received the B.S.E. degree in Computer Engineering from University of Michigan, Ann Arbor, USA in 2016. He also received the B.S.E. degree in Electrical and Computer Engineering from Shanghai Jiao-Tong University, Shanghai, China in 2016. He received the M.S. degree in Electrical and Computer Engineering from University of Michigan, Ann Arbor, USA in 2018. His research interests include multi-agent dynamic systems, dynamic games, mechanism design, and information design.