Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond

Gabriele Farina, Tuomas Sandholm

Abstract

Unlike normal-form games, where correlated equilibria have been studied for more than 45 years, extensive-form correlation is still generally not well understood. Part of the reason for this gap is that the sequential nature of extensive-form games allows for a richness of behaviors and incentives that are not possible in normal-form settings. This richness translates to a significantly different complexity landscape surrounding extensive-form correlated equilibria. As of today, it is known that finding an optimal extensive-form correlated equilibrium (EFCE), extensive-form coarse correlated equilibrium (EFCCE), or normal-form coarse correlated equilibrium (NFCCE) in a two-player extensive-form game is computationally tractable when the game does not include chance moves, and intractable when the game involves chance moves. In this paper we significantly refine this complexity threshold by showing that, in two-player games, an optimal correlated equilibrium can be computed in polynomial time, provided that a certain condition is satisfied. We show that the condition holds, for example, when all chance moves are public, that is, both players observe all chance moves. This implies that an optimal EFCE, EFCCE and NFCCE can be computed in polynomial time in the game size in two-player games with public chance moves.

Bibtex entry

@inproceedings{Farina20:Polynomial, title={Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond}, author={Farina, Gabriele and Sandholm, Tuomas}, booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, year={2020} }

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Bibtex entry

@inproceedings{Farina20:Polynomial, title={Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond}, author={Farina, Gabriele and Sandholm, Tuomas}, booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, year={2020} }

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Metadata

Venue: NeurIPS 2020
Topic: Decision Making, Optimization, and Computational Game Theory