Strong Duality for a Multiple-Good Monopolist
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Abstract (arXiv)
We characterize optimal mechanisms for the multiple-good monopoly problem and provide a framework to find them. We show that a mechanism is optimal if and only if a measure μ derived from the buyer's type distribution satisfies certain stochastic dominance conditions. This measure expresses the marginal change in the seller's revenue under marginal changes in the rent paid to subsets of buyer types. As a corollary, we characterize the optimality of grand-bundling mechanisms, strengthening several results in the literature, where only sufficient optimality conditions have been derived. As an application, we show that the optimal mechanism for n independent uniform items each supported on [c,c+1] is a grand-bundling mechanism, as long as c is sufficiently large, extending Pavlov's result for 2 items [Pavlov'11]. At the same time, our characterization also implies that, for all c and for all sufficiently large n, the optimal mechanism for n independent uniform items supported on [c,c+1] is not a grand bundling mechanism.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2014.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 4; 2024: 3; 2025: 2; 2026: 1.
17 papers.
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- 2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learningcited by 22
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- 2022A Context-Integrated Transformer-Based Neural Network for Auction Designcited by 19
- 2021Learning Revenue-Maximizing Auctions With Differentiable Matchingcited by 14
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- 2022Benefits of Permutation-Equivariance in Auction Mechanismscited by 7
- 2023Mode Connectivity in Auction Designcited by 3
- 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
5 more
- 2025Learning Truthful Mechanisms without Discretizationcited by 1
- 2023Refined Mechanism Design for Approximately Structured Priors via Active Regressioncited by 0
- 2024Statistically Truthful Auctions via Acceptance Rulecited by 0
- 2025Deep Learning for Double Auctioncited by 0
- 2026Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learningcited by 0
- 2026Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning8 Jun 2026
- 2025Learning Truthful Mechanisms without Discretization28 Jun 2025
- 2025Deep Learning for Double Auction7 Apr 2025
- 2024GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning11 Jun 2024
- 2024Statistically Truthful Auctions via Acceptance Rule20 May 2024
- 2024Optimal Automated Market Makers: Differentiable Economics and Strong Duality14 Feb 2024
- 2023Refined Mechanism Design for Approximately Structured Priors via Active Regression11 Oct 2023
- 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Design20 May 2023
- 2023Mode Connectivity in Auction Design18 May 2023
- 2023Neural Auctions Compromise Bidder Information28 Feb 2023
- 2022Benefits of Permutation-Equivariance in Auction Mechanisms11 Oct 2022
- 2022Optimal-er Auctions through Attention26 Feb 2022
5 more
- 2022Differentiable Economics for Randomized Affine Maximizer Auctions6 Feb 2022
- 2022A Context-Integrated Transformer-Based Neural Network for Auction Design29 Jan 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
- 2022Optimal-er Auctions through Attentioncited by 26
- 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Designcited by 23
- 2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learningcited by 22
- 2022Differentiable Economics for Randomized Affine Maximizer Auctionscited by 21
- 2022A Context-Integrated Transformer-Based Neural Network for Auction Designcited by 19
- 2021Learning Revenue-Maximizing Auctions With Differentiable Matchingcited by 14
- 2024GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learningcited by 14
- 2022Benefits of Permutation-Equivariance in Auction Mechanismscited by 7
- 2023Mode Connectivity in Auction Designcited by 3
- 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
12 most cited of 17 All 17 (JSON)
- 2026Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning8 Jun 2026
- 2025Learning Truthful Mechanisms without Discretization28 Jun 2025
- 2025Deep Learning for Double Auction7 Apr 2025
- 2024GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning11 Jun 2024
- 2024Statistically Truthful Auctions via Acceptance Rule20 May 2024
- 2024Optimal Automated Market Makers: Differentiable Economics and Strong Duality14 Feb 2024
- 2023Refined Mechanism Design for Approximately Structured Priors via Active Regression11 Oct 2023
- 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Design20 May 2023
- 2023Mode Connectivity in Auction Design18 May 2023
- 2023Neural Auctions Compromise Bidder Information28 Feb 2023
- 2022Benefits of Permutation-Equivariance in Auction Mechanisms11 Oct 2022
- 2022Optimal-er Auctions through Attention26 Feb 2022
12 newest of 17 All 17 (JSON)