A Simple and Approximately Optimal Mechanism for an Additive Buyer
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Abstract (arXiv)
We consider a monopolist seller with n heterogeneous items, facing a single buyer. The buyer has a value for each item drawn independently according to (non-identical) distributions, and her value for a set of items is additive. The seller aims to maximize his revenue. We suggest using the a-priori better of two simple pricing methods: selling the items separately, each at its optimal price, and bundling together, in which the entire set of items is sold as one bundle at its optimal price. We show that for any distribution, this mechanism achieves a constant-factor approximation to the optimal revenue. Beyond its simplicity, this is the first computationally tractable mechanism to obtain a constant-factor approximation for this multi-parameter problem. We additionally discuss extensions to multiple buyers and to valuations that are correlated across items.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2014.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1; 2025: 1; 2026: 2.
9 papers.
- 2022A Context-Integrated Transformer-Based Neural Network for Auction Designcited by 19
- 2024GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learningcited by 14
- 2023Deep Contract Design via Discontinuous Networkscited by 10
- 2021Recommender Systems meet Mechanism Designcited by 2
- 2022Learning Consumer Preferences from Bundle Sales Datacited by 2
- 2025Learning Optimal Posted Prices for a Unit-Demand Buyercited by 1
- 2026Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Designcited by 1
- 2023Refined Mechanism Design for Approximately Structured Priors via Active Regressioncited by 0
- 2026Cogentic: Multi-Agent Orchestration for Automated Proof Discoverycited by 0
- 2026Cogentic: Multi-Agent Orchestration for Automated Proof Discovery30 Sep 2026
- 2026Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design20 Jan 2026
- 2025Learning Optimal Posted Prices for a Unit-Demand Buyer2 Jun 2025
- 2024GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning11 Jun 2024
- 2023Refined Mechanism Design for Approximately Structured Priors via Active Regression11 Oct 2023
- 2023Deep Contract Design via Discontinuous Networks5 Jul 2023
- 2022Learning Consumer Preferences from Bundle Sales Data11 Sep 2022
- 2022A Context-Integrated Transformer-Based Neural Network for Auction Design29 Jan 2022
- 2021Recommender Systems meet Mechanism Design25 Oct 2021