Complexity and Optimality of the Best Response Algorithm in Random Potential Games - Archive ouverte HAL
Communication Dans Un Congrès Année : 2016

Complexity and Optimality of the Best Response Algorithm in Random Potential Games

Résumé

In this paper we compute the worst-case and average execution time of the Best Response Algorithm (BRA) to compute a pure Nash equilibrium in finite potential games. Our approach is based on a Markov chain model of BRA and a coupling technique that transform the average execution time of this discrete algorithm into the solution of an ordinary differential equation. In a potential game with N players and A strategies per player, we show that the worst case complexity of BRA (number of moves) is exactly N A N −1 , while its average complexity over random potential games is equal to e γ N + O(N), where γ is the Euler constant. We also show that the effective number of states visited by BRA is equal to log N + c + O(1/N) (with c e γ), on average. Finally , we show that BRA computes a pure Nash Equilibrium faster (in the strong stochastic order sense) than any local search algorithm over random potential games.
Fichier principal
Vignette du fichier
sagtFinalVersion2.pdf (404.44 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01404643 , version 1 (29-11-2016)

Identifiants

Citer

Stéphane Durand, Bruno Gaujal. Complexity and Optimality of the Best Response Algorithm in Random Potential Games. SAGT 2016 - Symposium on Algorithmic Game Theory (SAGT) 2016, Sep 2016, Liverpool, United Kingdom. pp.40-51, ⟨10.1007/978-3-662-53354-3_4⟩. ⟨hal-01404643⟩
368 Consultations
966 Téléchargements

Altmetric

Partager

More