Correlated and Coarse equilibria of Single-item auctions
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Abstract (arXiv)
We study correlated equilibria and coarse equilibria of simple first-price single-item auctions in the simplest auction model of full information. Nash equilibria are known to always yield full efficiency and a revenue that is at least the second-highest value. We prove that the same is true for all correlated equilibria, even those in which agents overbid -- i.e., bid above their values. Coarse equilibria, in contrast, may yield lower efficiency and revenue. We show that the revenue can be as low as 26% of the second-highest value in a coarse equilibrium, even if agents are assumed not to overbid, and this is tight. We also show that when players do not overbid, the worst-case bound on social welfare at coarse equilibrium improves from 63% of the highest value to 81%, and this bound is tight as well.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 2; 2026: 1.
8 papers.
- 2021Auctions Between Regret-Minimizing Agentscited by 22
- 2024Contracting with a Learning Agentcited by 22
- 2023The Role of Transparency in Repeated First-Price Auctions with Unknown Valuationscited by 15
- 2023Learning in Repeated Multi-Unit Pay-As-Bid Auctionscited by 3
- 2024Games with Payments between Learning Agentscited by 3
- 2025Proximal Regret and Proximal Correlated Equilibria: A New Tractable Solution Concept for Online Learning and Gamescited by 1
- 2025Breaking Algorithmic Collusion in Human-AI Ecosystemscited by 1
- 2026Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learnerscited by 0
- 2026Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners25 Feb 2026
- 2025Breaking Algorithmic Collusion in Human-AI Ecosystems26 Nov 2025
- 2025Proximal Regret and Proximal Correlated Equilibria: A New Tractable Solution Concept for Online Learning and Games3 Nov 2025
- 2024Games with Payments between Learning Agents31 May 2024
- 2024Contracting with a Learning Agent29 Jan 2024
- 2023Learning in Repeated Multi-Unit Pay-As-Bid Auctions27 Jul 2023
- 2023The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations14 Jul 2023
- 2021Auctions Between Regret-Minimizing Agents22 Oct 2021