Why Do Competitive Markets Converge to First-Price Auctions?
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Abstract (arXiv)
We consider a setting in which bidders participate in multiple auctions run by different sellers, and optimize their bids for the aggregate auction. We analyze this setting by formulating a game between sellers, where a seller's strategy is to pick an auction to run. Our analysis aims to shed light on the recent change in the Display Ads market landscape: here, ad exchanges (sellers) were mostly running second-price auctions earlier and over time they switched to variants of the first-price auction, culminating in Google's Ad Exchange moving to a first-price auction in 2019. Our model and results offer an explanation for why the first-price auction occurs as a natural equilibrium in such competitive markets.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 3.
5 papers.
- 2021Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctionscited by 8
- 2021Learning to Price Against a Moving Targetcited by 5
- 2025BAT: Benchmark for Auto-bidding Taskcited by 2
- 2025Learning to Coordinate Bidders in Non-Truthful Auctionscited by 0
- 2025HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environmentscited by 0
- 2025HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments17 Oct 2025
- 2025Learning to Coordinate Bidders in Non-Truthful Auctions3 Jul 2025
- 2025BAT: Benchmark for Auto-bidding Task13 May 2025
- 2021Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions8 Oct 2021
- 2021Learning to Price Against a Moving Target8 Jun 2021