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 has received the Laurea degree in Aeronautical Engineering in 2000 and the Ph.D. degree in Automatic Control and System Theory in 2004 from the University of Palermo, Italy. Since 2018 he has been with the Jan C. Willems Center for Systems and Control, ENTEG, Faculty of Science and Engineering, University of Groningen (The Netherlands), where he is currently Full Professor and Chair of Operations Research for Engineering Systems. Since 2005 he has also been with the Dipartimento di Ingegneria, University of Palermo (Italy). Since 2018 he has been a guest professor at Keio University, Japan. His research interests are in the field of Optimization, Optimal and Distributed Control, and Game Theory. Bauso was an Associate Editor of IEEE Transactions on Automatic Control from 2011 to 2016, of  IFAC Automatica from 2015 to 2021, of IEEE Control Systems Letters from 2016 to 2021, of Dynamic Games and Applications from 2011 to 2022, and is Associate Editor of Journal of Dynamics and Games since 2019.

Title: Gradient-Based Learning Dynamics in Coalitional Games with Transferable Utility.

Abstract: We study a group of players making joint decisions under uncertainty and learning via gradient dynamics how to reach the maximal profit and the revenue gets allocated to the players while the game is running. In the first scenario, uncertainty is modeled by a known distribution, allowing players to update decisions via deterministic gradient ascent dynamics. We show that the learning dynamics and value function converge linearly to the optimum. In the second scenario, only samples are available, and players use stochastic gradient ascent dynamics. We prove that the stochastic objective, evaluated at the average of the stochastic gradient ascent dynamics, converges sublinearly to the optimum. We further propose a fair profit allocation mechanism, based on the Shapley value, and show that this learning-allocation mechanism converges to its optimum in both the deterministic and stochastic cases. Numerical results support our theoretical findings.

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.

Dr. Galit Ashkenazi-Golan

Galit Ashkenazi-Golan's research focus is in Game Theory, the mathematical modelling of strategic interactions. In particular, she is interested in dynamic games (such as repeated games, stochastic games, or Borel Games), and often in the effect that information has on the equilibrium of a game. She is also interested in related topics such as social learning, Markov Decision Processes and Computational Game Theory. She joined LSE in 2021 as an Assistant Professor. Before this, she was a research affiliate in the School of Mathematical Sciences of Tel-Aviv University. She completed her PhD under the supervision of Ehud Lehrer from Tel-Aviv University, though she spent several years of her PhD as a visiting scholar at the École Polytechnique in Paris.

Title: TBD.

Abstract: TBD.