On the Complexity of Equilibrium Computation in First-Price Auctions
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Abstract (arXiv)
We consider the problem of computing a (pure) Bayes-Nash equilibrium in the first-price auction with continuous value distributions and discrete bidding space. We prove that when bidders have independent subjective prior beliefs about the value distributions of the other bidders, computing an varepsilon-equilibrium of the auction is PPAD-complete, and computing an exact equilibrium is FIXP-complete. We also provide an efficient algorithm for solving a special case of the problem, for a fixed number of bidders and available bids.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 3; 2026: 1.
6 papers.
- 2021Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctionscited by 8
- 2025Joint Value Estimation and Bidding in Repeated First-Price Auctionscited by 6
- 2025Learning to Bid in Non-Stationary Repeated First-Price Auctionscited by 5
- 2024The complexity of approximate (coarse) correlated equilibrium for incomplete information gamescited by 4
- 2026Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctionscited by 1
- 2025Learning to Coordinate Bidders in Non-Truthful Auctionscited by 0
- 2026Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions10 May 2026
- 2025Learning to Coordinate Bidders in Non-Truthful Auctions3 Jul 2025
- 2025Joint Value Estimation and Bidding in Repeated First-Price Auctions24 Feb 2025
- 2025Learning to Bid in Non-Stationary Repeated First-Price Auctions23 Jan 2025
- 2024The complexity of approximate (coarse) correlated equilibrium for incomplete information games4 Jun 2024
- 2021Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions8 Oct 2021