Robust Mediators in Large Games
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Abstract (arXiv)
A mediator is a mechanism that can only suggest actions to players, as a function of all agents' reported types, in a given game of incomplete information. We study what is achievable by two kinds of mediators, "strong" and "weak." Players can choose to opt-out of using a strong mediator but cannot misrepresent their type if they opt-in. Such a mediator is "strong" because we can view it as having the ability to verify player types. Weak mediators lack this ability--- players are free to misrepresent their type to a weak mediator. We show a striking result---in a prior-free setting, assuming only that the game is large and players have private types, strong mediators can implement approximate equilibria of the complete-information game. If the game is a congestion game, then the same result holds using only weak mediators. Our result follows from a novel application of differential privacy, in particular, a variant we propose called joint differential privacy.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Dec 2015.
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Citing papers per year: 2023: 3; 2024: 2; 2025: 2.
7 papers.
- 2024Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queriescited by 12
- 2023Private Everlasting Predictioncited by 5
- 2023Mediated Multi-Agent Reinforcement Learningcited by 5
- 2024On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspectivecited by 5
- 2023On Differentially Private Online Predictionscited by 3
- 2025Towards Optimal Differentially Private Regret Bounds in Linear MDPscited by 0
- 2025Learning Recommender Mechanisms for Bayesian Stochastic Gamescited by 0
- 2025Learning Recommender Mechanisms for Bayesian Stochastic Games29 May 2025
- 2025Towards Optimal Differentially Private Regret Bounds in Linear MDPs12 Apr 2025
- 2024Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries28 Feb 2024
- 2024On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective26 Feb 2024
- 2023Mediated Multi-Agent Reinforcement Learning14 Jun 2023
- 2023Private Everlasting Prediction16 May 2023
- 2023On Differentially Private Online Predictions27 Feb 2023