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

12-May-2026, 16:00 CET

Prof. Sergio Grammatico
TU Delft

Stackelberg equilibrium seeking

We discuss Stackelberg equilibrium seeking in hierarchical multi-agent systems with limited information and possibly multiple follower equilibria. We first address the ill-posedness induced by multiple equilibria by introducing an optimal selection mechanism based on Tikhonov regularization. Such a mechanism yields a well-defined induced Stackelberg equilibrium and enables follower-agnostic zeroth-order seeking. Still in the setting of unknown follower models, in the second part of the talk, we present a learning-based approach that integrates zeroth-order optimization with online estimation of the followers response, hereby significantly improving efficiency and reducing interaction requirements.

19-May-2026, 16:00 CET

Prof. Sarah H. Q. Li
Georgia Institute of Technology

From Centralized Game-theoretic Coordination to Commitment Breaking in Multi-Agent Aerial Traffic Systems

This talk investigates multi-agent aerial traffic across a spectrum of coordination paradigms, from centralized air traffic planning to decentralized interaction with breakable commitments. I first discuss structured coordination through MDP congestion games and reach-avoid potential games, where agents reason strategically within a shared mathematical framework to improve traffic efficiency or enforce stronger safety objectives. These approaches illustrate how game-theoretic structure can support scalable coordination, while also revealing tradeoffs between computational tractability and guarantee strength. I then consider decentralized airspace settings such as uncontrolled airspaces in which centralized planning is unavailable and agents coordinate only through announced intentions or commitments. In these lower-information environments, coordination depends not only what agents announce, but also on their credibility. This leads to game-theoretic questions of belief mismatch, strategic deviation, and commitment breaking. The talk uses these examples to highlight a broader challenge in aerial autonomy: how to design coordination mechanisms that remain safe, tractable, and robust as centralized structure weakens and strategic behavior becomes more prominent.

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.