Query Complexity of Correlated Equilibrium
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Abstract (arXiv)
We study lower bounds on the query complexity of determining correlated equilibrium. In particular, we consider a query model in which an n-player game is specified via a black box that returns players' utilities at pure action profiles. In this model we establish that in order to compute a correlated equilibrium any deterministic algorithm must query the black box an exponential (in n) number of times.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2013.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2.
4 papers.
- 2021When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?cited by 63
- 2023Fast swap regret minimization and applications to approximate correlated equilibriacited by 30
- 2024Barriers to Welfare Maximization with No-Regret Learningcited by 0
- 2024Computational Lower Bounds for Regret Minimization in Normal-Form Gamescited by 0
- 2024Computational Lower Bounds for Regret Minimization in Normal-Form Games4 Nov 2024
- 2024Barriers to Welfare Maximization with No-Regret Learning4 Nov 2024
- 2023Fast swap regret minimization and applications to approximate correlated equilibria30 Oct 2023
- 2021When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?8 Oct 2021