May 15, 2026, Webb Hall 1100

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: 
(i) How can we find feedback strategies which closely approximate Nash solutions, but minimize inter-agent communication/sensing? (ii) If agents’ access to information changes during an interaction, are there scenarios in which we can still find equilibria efficiently? (iii) 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?

David Fridovich-Keil is an assistant professor of aerospace engineering at the University of Texas at Austin. Fridovich-Keil's research investigates a wide variety of multi-agent strategic decision-making problems, and focuses on establishing game-theoretic models of these interactions, inverting those models to infer agents' intentions from data, and leveraging that information to guide future interactions. A key aim of Fridovich-Keil's recent work has been to integrate these capabilities with generative machine learning by leveraging fundamental connections with optimization theory and differentiable programming. Fridovich-Keil is the recipient of an NSF Graduate Research Fellowship and an NSF CAREER award.