Optimal Auctions with Restricted Allocations
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Abstract (arXiv)
We study the problem of designing optimal auctions under restrictions on the set of permissible allocations. In addition to allowing us to restrict to deterministic mechanisms, we can also indirectly model non-additive valuations. We prove a strong duality result, extending a result due to Daskalakis et al. [2015], that guarantees the existence of a certificate of optimality for optimal restricted mechanisms. As a corollary of our result, we provide a new characterization of the set of allocations that the optimal mechanism may actually use. To illustrate our result we find and certify optimal mechanisms for four settings where previous frameworks do not apply, and provide new economic intuition about some of the tools that have previously been used to find optimal mechanisms.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jun 2016.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2.
7 papers.
- 2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learningcited by 22
- 2022Differentiable Economics for Randomized Affine Maximizer Auctionscited by 22
- 2021Learning Revenue-Maximizing Auctions With Differentiable Matchingcited by 14
- 2021Recommender Systems meet Mechanism Designcited by 2
- 2023Neural Auctions Compromise Bidder Informationcited by 1
- 2024Optimal Automated Market Makers: Differentiable Economics and Strong Dualitycited by 1
- 2024Statistically Truthful Auctions via Acceptance Rulecited by 0
- 2024Statistically Truthful Auctions via Acceptance Rule20 May 2024
- 2024Optimal Automated Market Makers: Differentiable Economics and Strong Duality14 Feb 2024
- 2023Neural Auctions Compromise Bidder Information28 Feb 2023
- 2022Differentiable Economics for Randomized Affine Maximizer Auctions6 Feb 2022
- 2021Recommender Systems meet Mechanism Design25 Oct 2021
- 2021Learning Revenue-Maximizing Auctions With Differentiable Matching15 Jun 2021
- 2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning6 Jun 2021