Prof. Tamer Başar

Tamer Başar (Life Fellow, IEEE) received the B.S.E.E. degree from Robert College, Istanbul, Turkiye, in 1969, and the M.S., M.Phil., and Ph.D. degrees in engineering and applied science from Yale University, New Haven, CT, USA, in 1970, 1971, and 1972, respectively. 

He has been with the University of Illinois Urbana-Champaign since 1981, where he is currently Swanlund Endowed Chair Emeritus and Center for Advanced Study (CAS) Professor Emeritus of Electrical and Computer Engineering, with also affiliations with the Coordinated Science Laboratory, Information Trust Institute, and Mechanical Science and Engineering. At Illinois, he has also served as Director of CAS (2014–2020), Interim Dean of Engineering (2018), and Interim Director of the Beckman Institute (2008–2010). His current research interests include stochastic teams, games, and networks; risk-sensitive estimation and control; mean-field game theory; multiagent systems and learning; data-driven distributed optimization; epidemics modeling and control over networks; strategic information transmission, spread of disinformation, and deception; security and trust; energy systems; and cyber-physical systems. Dr. Başar received the Wilbur Cross Medal in 2021. 

He is a Member of the US National Academy of Engineering and a Fellow of the American Academy of Arts and Sciences, as well as Fellow of IEEE, IFAC, and SIAM. He has served as the President of IEEE Control Systems Society (CSS), International Society of Dynamic Games (ISDG), and American Automatic Control Council (AACC). He has received several awards and recognitions over the years, including the highest awards of IEEE CSS, IFAC, AACC, and ISDG, the IEEE Control Systems Award, and a number of international honorary doctorates and professorships. He has over 1000 publications in systems, control, communications, optimization, networks, and dynamic games, including books on noncooperative dynamic game theory, robust control, network security, wireless and communication networks, and stochastic networked control. He was the Editor-in-Chief of Automatica between 2004 and 2014, and is currently the editor of several book series.

Title: RL and Equilibria for Multi-Agent Dynamical Systems in the High-Population Regime.

Abstract: Decision making in dynamic uncertain environments with multiple agents arises in many disciplines and application domains, including control, communications, distributed optimization, social networks, and economics. Here a natural framework, and a comprehensive one, for modeling, optimization, and analysis is the one provided by stochastic dynamic games (SDGs), which accommodates different solution concepts depending on how the interactions among the agents are modeled, particularly whether they are in a cooperative mode (with the same objective functions, as in teams) or in a noncooperative mode (with different objective functions) or a mix of the two, such as teams of agents interacting noncooperatively across different teams (and of course cooperatively within each team). What also affects (strategic) interactions among the agents is the asymmetric nature of the information different agents acquire (and do not share or only partially share (selectively) with others, even within teams). What makes such problems even more challenging in a dynamic environment with networked agents is the dependence of the information available to one agent at some point in time on the policies or decisions of other agents who have already acted at earlier instants of time. Such decision problems, initially studied in a team framework, are known as those with nonclassical information where optimal policies of team agents must be designed to balance a tradeoff between contribution to optimality of the team objective function and signaling through their actions useful information to other agents in their neighborhood who would be acting after them. Existence of such a tradeoff between signaling and optimization creates even more challenging issues in SDGs with mis-aligned objectives among at least a subset of agents, which however can be addressed effectively for a specially structured subclass of such games, namely mean-field games. 

This talk will provide an overview of the landscape above, first for a general class of stochastic dynamic teams and games, and then for a subclass where the objective functions are quadratic, and the interaction relationships are linear. The talk will also cover reinforcement learning embedded into policy development when agents do not have precise information on the underlying models.

Prof. Dario Bauso

Dario Bauso (Member, IEEE) received the Laurea degree in aeronautical engineering and the Ph.D. degree in automatic control and system theory from the University of Palermo, Palermo, Italy, in 2000 and 2004, respectively. 

Since 2005, he has been with the Dipartimento di Ingegneria, University of Palermo. Since 2018, he has been with the Jan C. Willems Center for Systems and Control, ENTEG, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands, where he is currently a Full Professor and Chair of Operations Research for Engineering Systems. Since 2018, he has also been a Guest Professor with Keio University, Minato, Japan. His research interests include field of optimization, optimal and distributed control, and game theory. 

Dr. Bauso was an Associate Editor for IEEE Transactions on Automatic Control from 2011 to 2016, IFAC Automatica from 2015 to 2021, IEEE Control Systems Letters from 2016 to 2021, Dynamic Games and Applications from 2011 to 2022, and Journal of Dynamics and Games since 2019.

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Prof. Manh Hong Duong

Hong Duong is a Professor of Mathematics. Hong Duong’s research interests span a wide range of topics in the intersections of analysis, applied probability, and computational mathematics including partial differential equations, interacting particle systems, non-equilibrium thermodynamics, and evolutionary game theory. Most of his research are inspired from applications in statistical physics and biological/social/material sciences. His research has been supported from the ITN Fronts and Interfaces in Science and Technology (EU), the NWO (Netherlands), the London Mathematical Society (UK) and the EPSRC (UK).

Hong Duong's research interests span a wide range of topics in the intersections of analysis, applied probability and computational mathematics. Most of his research are inspired from applications in statistical physics and biological/social/material sciences.

Title: Evolutionary Game Theory, Evolution of Cooperation and Institutional Incentives.

Abstract: Darwin's theory of evolution by natural selection, also known as “survival of the fittest”, implies that evolution is based on a fierce competition between individuals and should therefore only reward selfish behavior. However, cooperation occurs at all levels of biological organizations, from cellular clusters to bees to humans. Cooperation is in fact needed for evolution to construct new levels of organization. The problem of promoting cooperative behaviour within populations of self-regarding individuals has been intensively investigated across diverse fields of behavioural, social and computational sciences. Evolutionary Game Theory provides a powerful mathematical framework for understanding this key challenge over the last 50 years. Under this framework, several mechanisms for promoting the evolution of cooperation have been identified, including kin selection, direct reciprocity, indirect reciprocity, network reciprocity, group selection and different forms of incentives. 

In this talk, I will discuss these topics and present our recent research on promoting cooperation via institutional incentives. We will show how this practical problem can be formulated into mathematically constrained, multi-objective optimization problems, where one wishes to minimize the cost of providing incentives while ensuring a minimum level of cooperation, sustained over time.