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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

17-Feb-2026, 15:00 CET

df_keil

Prof. David Fridovich-Keil
University of Texas at Austin

Variations on a Theme: Information Structure, Equilibria, and Dynamic Games

This talk will review a fundamental building block of dynamic game theory—the linear-quadratic game—and discuss how Nash equilibrium solutions differ as a consequence of the information players have access to at different times. In this context, we will examine several recent results, aligned to the following questions:

  1. How can we find feedback strategies which closely approximate Nash solutions, but minimize inter-agent communication/sensing?
  2. If agents’ access to information changes during an interaction, are there scenarios in which we can still find equilibria efficiently?
  3. In two-player, zero-sum games, there are classical results about the equivalence of solutions under different information structures for linear-quadratic games. In what sense do these extend beyond the linear-quadratic setting?

24-Feb-2026, 16:00 CET

b_nortmann

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

Title TBA

TBA


03-Mar-2026, 16:00 CET

n_lanzetti

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 so 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) are guaranteed to exist under the same assumptions required for Nash equilibria; (ii) interpolate between Nash and security strategies; (iii) come at no additional computational cost compared to Nash equilibria. 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, often results in higher equilibrium payoffs - an effect we refer to as coordination via robustification.


10-Mar-2026, 16:00 CET

a_bemporad

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

s_hall

Sophie Hall
ETH Zürich

Title TBA

TBA


17-Mar-2026, 16:00 CET

c_maheshwari

Prof. Chinmay Maheshwari
John Hopkins University

Title TBA

TBA


12-May-2026, 16:00 CET

s_grammatico

Prof. Sergio Grammatico
TU Delft

Title TBA

TBA


26-May-2026, 16:00 CET

f_fabiani

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.


02-June-2026, 16:00 CET

j_shamma

Prof. Jeff Shamma
University of Illinois at Urbana-Champaign

Title TBA

TBA


09-June-2026, 16:00 CET

e_elokda

Dr. Ezzat Elokda
KTH Stockholm

Title TBA

TBA


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