Thu, 05 Dec 2024
16:00
L4

Mean Field Games in a Stackelberg problem with an informed major player

Dr Philippe Bergault
(Université Paris Dauphine-PSL)
Further Information

Please join us for refreshments outside the lecture room from 15:30.

Abstract

We investigate a stochastic differential game in which a major player has a private information (the knowledge of a random variable), which she discloses through her control to a population of small players playing in a Nash Mean Field Game equilibrium. The major player’s cost depends on the distribution of the population, while the cost of the population depends on the random variable known by the major player. We show that the game has a relaxed solution and that the optimal control of the major player is approximatively optimal in games with a large but finite number of small players. Joint work with Pierre Cardaliaguet and Catherine Rainer.

Wed, 04 Dec 2024
11:00
L4

Effective Mass of the Polaron and the Landau-Pekar-Spohn Conjecture

Chiranjib Mukherjee
(University of Münster)
Abstract

According to a conjecture by Landau-Pekar (1948) and by Spohn (1986), the effective mass of the Fröhlich Polaron should diverge in the strong coupling limit like a quartic power of the coupling constant. In a recent joint with R. Bazaes, M. Sellke and S.R.S. Varadhan, we prove this conjecture.

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Tue, 05 Nov 2024
14:00
L5

María Reboredo Prado: Webs in the Wind: A Network Exploration of the Polar Vortex

María Reboredo Prado
(Mathematical Institute)
Abstract

All atmospheric phenomena, from daily weather patterns to the global climate system, are invariably influenced by atmospheric flow. Despite its importance, its complex behaviour makes extracting informative features from its dynamics challenging. In this talk, I will present a network-based approach to explore relationships between different flow structures. Using three phenomenon- and model-independent methods, we will investigate coherence patterns, vortical interactions, and Lagrangian coherent structures in an idealised model of the Northern Hemisphere stratospheric polar vortex. I will argue that networks built from fluid data retain essential information about the system's dynamics, allowing us to reveal the underlying interaction patterns straightforwardly and offering a fresh perspective on atmospheric behaviour.

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