Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Learning in nonatomic games, Part I: Finite action spaces and population games

Abstract : We examine the long-run behavior of a wide range of dynamics for learning in nonatomic games, in both discrete and continuous time. The class of dynamics under consideration includes fictitious play and its regularized variants, the best reply dynamics (again, possibly regularized), as well as the dynamics of dual averaging / "follow the regularized leader" (which themselves include as special cases the replicator dynamics and Friedman's projection dynamics). Our analysis concerns both the actual trajectory of play and its time-average, and we cover potential and monotone games, as well as games with an evolutionarily stable state (global or otherwise). We focus exclusively on games with finite action spaces; nonatomic games with continuous action spaces are treated in detail in Part II.
Document type :
Preprints, Working Papers, ...
Complete list of metadata
Contributor : Panayotis Mertikopoulos Connect in order to contact the contributor
Submitted on : Monday, September 13, 2021 - 5:09:09 PM
Last modification on : Friday, August 5, 2022 - 11:59:58 AM


Files produced by the author(s)


  • HAL Id : hal-03342992, version 1


Saeed Hadikhanloo, Rida Laraki, Panayotis Mertikopoulos, Sylvain Sorin. Learning in nonatomic games, Part I: Finite action spaces and population games. 2021. ⟨hal-03342992⟩



Record views


Files downloads