Abstract: Team theory is a mathematical formalism for decentralized stochastic control problems in which a “team,” consisting of a number of members, cooperates to achieve a common objective. It was developed to provide a rigorous mathematical framework of cooperating members in which all members have the same objective yet different information. In static team problems, the information received by the team members is not affected by the decisions of other team members, while in dynamic team problems, the information of at least one team member is affected by the decisions of other team members. If there is a prescribed order in which team members make decisions, then such a problem is called a sequential team problem. The information structures in sequential team decision problems designate who knows what about the status of the team and are classified as classical, partially nested, and non-classical. In classical information structures, all team members receive the same information and have perfect recall. In partially nested information structures, some team members have a nonempty intersection of their information structures while they have perfect recall. Any information structure that is not classical or partially nested is called nonclassical. In this talk, I consider sequential dynamic team decision problems with nonclassical information structures. First, I will address the problem from the point of view of a “manager” who seeks to derive the optimal strategy for the team in a centralized process. I provide structural results that yield an information state for the team, which does not depend on the control strategy, and thus, it can lead to a dynamic programming decomposition where the optimization problem is over the space of the team’s decisions. I will then provide structural results for each team member that yield an information state that does not depend on their control strategy, and thus, it can lead to a dynamic programming decomposition where the optimization problem for each team member is over the space of their decisions. Finally, I will show that the solution of each team member is the same as the one derived by the manager. Therefore, each team member can derive their optimal strategy, which is also optimal for the team, without the manager’s intervention.

Bio: Andreas Malikopoulos is a Professor in the School of Civil & Environmental Engineering and the Director of the Information and Decision Science Lab at Cornell University. Prior to these appointments, he was the Terri Connor Kelly and John Kelly Career Development Professor in the Department of Mechanical Engineering (2017-2023) and the founding Director of the Sociotechnical Systems Center (2019-2023) at the University of Delaware (UD). Before he joined UD, he was the Alvin M. Weinberg Fellow (2010-2017) in the Energy & Transportation Science Division at Oak Ridge National Laboratory (ORNL), the Deputy Director of the Urban Dynamics Institute (2014-2017) at ORNL, and a Senior Researcher in General Motors Global Research & Development (2008-2010). He received a Diploma from the National Technical University of Athens, Greece, and his M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2004 and 2008, respectively, all in Mechanical Engineering. His research interests span several fields, including analysis, optimization, and control of cyber-physical systems; decentralized stochastic systems; stochastic scheduling and resource allocation; and learning in complex systems. Dr. Malikopoulos is the recipient of several prizes and awards, including the 2007 Dare to Dream Opportunity Grant from the University of Michigan Ross School of Business, the 2007 University of Michigan Teaching Fellow, the 2010 Alvin M. Weinberg Fellowship, the 2019 IEEE Intelligent Transportation Systems Young Researcher Award, and the 2020 UD’s College of Engineering Outstanding Junior Faculty Award. He has been selected by the National Academy of Engineering to participate in the 2010 German-American Frontiers of Engineering (FOE) Symposium and organize a session on transportation at the 2016 European-American FOE Symposium. He has also been selected as a 2012 Kavli Frontiers of Science Scholar by the National Academy of Sciences. Dr. Malikopoulos has been an Associate Editor of the IEEE Transactions on Intelligent Vehicles and IEEE Transactions on Intelligent Transportation Systems from 2017 through 2020. He is an Associate Editor of Automatica and IEEE Transactions on Automatic Control, and a Senior Editor of IEEE Transactions on Intelligent Transportation Systems. He is a Senior Member of the IEEE, a Fellow of the ASME, and a member of the Board of Governors of the IEEE Intelligent Transportation Systems Society.