Game on! Seminars

Game theory. Control. Intelligent systems.

Participation is open to everyone with no registration required.
The talks are typically held on Tuesdays at 16:00 CET.

Upcoming seminars

24-Feb-2026, 16:00 CET

Dr. Benita Nortmann
EMPA - Swiss federal laboratories for material science and technology

Exploring feedback Nash equilibria in infinite-horizon LQ dynamic games

Many modern processes involve real-time decisions in dynamic environments influenced by multiple decision makers with team-based and individual, potentially competitive objectives. Dynamic game theory captures interactions between such players and allows us to model and design decision strategies. We focus on the class of infinite-horizon, nonzero-sum, linear quadratic (LQ), discrete-time dynamic games, which are relevant in various engineering and economic applications. Linear feedback Nash equilibrium (FNE) solutions to such LQ games are characterised via the solutions of coupled algebraic matrix equations. While reminiscent of the algebraic Riccati equation arising in LQ optimal control, these coupled equations are generally difficult to solve and may admit multiple solutions with different outcomes.

To effectively employ dynamic games to model and design multi-player decisions, it is important to understand the existence, number, and properties of equilibria and to develop efficient computation methods. In this talk, we first build intuition for the number and properties of FNE solutions by considering games with scalar dynamics and inputs, using geometric arguments. We then discuss iterative methods to compute FNE solutions for general LQ games, and a data-driven approach, which enables players to jointly converge to a FNE solution without knowledge of each other’s control objectives.

03-Mar-2026, 16:00 CET

Dr. Nicolas Lanzetti
Caltech

Strategically Robust Game Theory via Optimal Transport

In many game-theoretic settings, agents are challenged with taking decisions against the uncertain behavior exhibited by others. Often, this uncertainty arises from multiple sources, e.g., incomplete information, limited computation, bounded rationality. While it may be possible to guide the agents' decisions by modeling each source, their joint presence makes this task particularly daunting. Toward this goal, it is natural for agents to seek protection against deviations around the emergent behavior itself, which is ultimately impacted by all the above sources of uncertainty. To do so, we propose that each agent takes decisions in face of the worst-case behavior contained in an ambiguity set of tunable size, centered at the emergent behavior implicitly defined. This gives rise to a novel equilibrium notion, which we call strategically robust equilibrium. Building on its definition we show that, when judiciously operationalized via optimal transport, strategically robust equilibria
(i) interpolate between Nash and security strategies;
(ii) come at no additional computational cost compared to Nash equilibria;
(iii) often lead to better decisions and higher payoffs.
Through a variety of experiments including bi-matrix games, congestion games, and Cournot competition, we show that strategic robustness protects against uncertainty in the opponents' behavior and, surprisingly, results in higher equilibrium payoffs – an effect we refer to as coordination via robustification. Joint work with S. Fricker, S. Bolognani, F. Dörfler, and D. Paccagnan.

10-Mar-2026, 16:00 CET

Prof. Alberto Bemporad
IMT Lucca

Solution methods for generalized Nash equilibrium problems and game-theoretic control

Generalized Nash equilibrium problems (GNEPs) arise in non-cooperative multi-agent decision making with shared constraints. This talk focuses on optimization methods for computing generalized Nash equilibria and for addressing game design and game-theoretic control problems. We first present an active-learning approach that identifies equilibria directly from best-response queries, without requiring explicit knowledge of the agents' objective functions. We then introduce a multiparametric solver for linear–quadratic GNEPs with parametric dependence, which yields explicit piecewise-affine equilibrium mappings over polyhedral regions of the parameter space. The talk concludes with an overview of a software library for solving nonlinear and linear–quadratic GNEPs, with applications to game design and game-theoretic linear–quadratic and model predictive control.

17-Mar-2026, 16:00 CET

Sophie Hall
ETH Zürich

Title TBA

TBA

07-Apr-2026, 16:00 CET

Prof. Chinmay Maheshwari
John Hopkins University

Title TBA

TBA

12-May-2026, 16:00 CET

Prof. Sergio Grammatico
TU Delft

Title TBA

TBA

26-May-2026, 16:00 CET

Dr. Filippo Fabiani
IMT Lucca

Data-based certificates in stochastic Nash games

Many modern systems in smart grids and smart cities rely on the interaction of multiple decision-makers whose choices affect one another. These interactions can be naturally described using game-theoretic models, but in practice they are often influenced by uncertainty (e.g., fluctuating demand or renewable generation) whose statistical properties are unknown. From a mathematical perspective, this complicates enormously the evaluation of the expected cost of each agent. Most existing approaches rely on large amounts of data and guarantee convergence only in the limit of infinite samples, an assumption that is unrealistic in many real-world and safety-critical settings. This talk asks a more practical question: what can be guaranteed when only a finite amount of data is available?

Building on recent advances in stability analysis and stochastic approximation, we will introduce a data-based framework that provides computable certificates measuring how close one can get from a Nash or generalized Nash equilibrium using finite samples. The approach leverages the monotonicity property and variational inequality structure of the stochastic game at hand, together with standard Nash equilibrium seeking schemes based on operator theory, thereby enabling reliable assessment of convergence even when part of the game model is unknown and shall be approximated in a data-driven fashion. Our results thus offer finite-sample certificates that bound equilibrium residuals and stability margins directly from available uncertainty realizations, without knowing the underlying probability distribution. As such, the proposed framework provides a unifying view of learning dynamics and equilibrium verification in stochastic multi-agent systems, with implications for data-driven control, economic modeling, and large-scale learning in games. Numerical illustrations demonstrate how the proposed certificates track equilibrium quality in practice.