Abstract: We consider problems involving multiple agents making decisions dynamically in the presence of asymmetric information.
When agents have a common objective (dynamic decentralized teams) recent results have established a systematic framework for obtaining the optimal decision strategy that is akin to the well-known backward induction in partially observed Markov decision processes (POMDPs). However, when agents are strategic (dynamic games with asymmetric information) there is no known systematic process for evaluating the appropriate equilibria in a sufficiently general setting. The well-known backward induction process for finding sub-game perfect equilibria is useless in these problems and we are stuck with an indecomposable fixed-point equation in the space of strategies and beliefs.
In this talk we will discuss a class of perfect Bayesian equilibria (PBE) that are the counterparts of Markov perfect equilibria (MPE) for asymmetric information games. The corresponding “state” is a belief based on the common information among agents. We will then propose a two-step backward-forward inductive algorithm to find these structured PBE. The backward inductive part of this algorithm defines an equilibrium generating function. Each period in the backward induction involves solving a “small” fixed point equation. Using this generating function, equilibrium strategies and beliefs are defined through a forward recursion.
Bio: Achilleas Anastasopoulos received the Diploma in EE from the National Technical University of Athens, Greece in 1993, and the M.S. and Ph.D. degrees in EE from the University of Southern California in 1994 and 1999, respectively. He is currently an Associate Professor of EECS at the University of Michigan, Ann Arbor. His research interests lie in 1) the general area of communication and information theory, with emphasis in channel coding and multi-user channels; 2) control theory with emphasis in decentralized stochastic control and its connections to communications and information-theoretic problems; 3) analysis of dynamic games and mechanism design for resource allocation in networked systems.