A geometric decomposition of finite games: Convergence vs. recurrence under exponential weights - Université Grenoble Alpes
Communication Dans Un Congrès Année : 2024

A geometric decomposition of finite games: Convergence vs. recurrence under exponential weights

Résumé

In view of the complexity of the dynamics of learning in games, we seek to decompose a game into simpler components where the dynamics' long-run behavior is well understood. A natural starting point for this is Helmholtz's theorem, which decomposes a vector field into a potential and an incompressible component. However, the geometry of game dynamics - and, in particular, the dynamics of exponential / multiplicative weights (EW) schemes - is not compatible with the Euclidean underpinnings of Helmholtz's theorem. This leads us to consider a specific Riemannian framework based on the so-called Shahshahani metric, and introduce the class of incompressible games, for which we establish the following results: First, in addition to being volume-preserving, the continuous-time EW dynamics in incompressible games admit a constant of motion and are Poincar\'e recurrent - i.e., almost every trajectory of play comes arbitrarily close to its starting point infinitely often. Second, we establish a deep connection with a well-known decomposition of games into a potential and harmonic component (where the players' objectives are aligned and anti-aligned respectively): a game is incompressible if and only if it is harmonic, implying in turn that the EW dynamics lead to Poincar\'e recurrence in harmonic games.
Fichier principal
Vignette du fichier
Main.pdf (4.29 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04629310 , version 1 (29-06-2024)

Licence

Identifiants

Citer

Davide Legacci, Panayotis Mertikopoulos, Bary Pradelski. A geometric decomposition of finite games: Convergence vs. recurrence under exponential weights. ICML 2024 - 41st International Conference on Machine Learning, Jul 2024, Vienna, Austria. ⟨hal-04629310⟩
157 Consultations
86 Téléchargements

Altmetric

Partager

More