research-article

Approximate revenue maximization with multiple items

Authors:

Sergiu Hart,

Noam NisanAuthors Info & Claims

EC '12: Proceedings of the 13th ACM Conference on Electronic Commerce

Page 656

https://doi.org/10.1145/2229012.2229061

Published: 04 June 2012 Publication History

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Abstract

Myerson's classic result provides a full description of how a seller can maximize revenue when selling a single item. We address the question of revenue maximization in the simplest possible multi-item setting: two items and a single buyer who has independently distributed values for the items, and an additive valuation. In general, the revenue achievable from selling two independent items may be strictly higher than the sum of the revenues obtainable by selling each of them separately. In fact, the structure of optimal (i.e., revenue-maximizing) mechanisms for two items even in this simple setting is not understood.

In this paper we obtain approximate revenue optimization results using two simple auctions: that of selling the items separately, and that of selling them as a single bundle. Our main results (which are of a "direct sum" variety, and apply to any distributions) are as follows. Selling the items separately guarantees at least half the revenue of the optimal auction; for identically distributed items, this becomes at least 73% of the optimal revenue.

For the case of k > 2 items, we show that selling separately guarantees at least a c/log2(k) fraction of the optimal revenue; for identically distributed items, the bundling auction yields at least a c/log(k) fraction of the optimal revenue.

Cited By

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Essaidi MGoldner KWeinberg S(2024)To Regulate or Not to Regulate: Using Revenue Maximization Tools to Maximize Consumer UtilityAlgorithmic Game Theory10.1007/978-3-031-71033-9_18(315-332)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-3-031-71033-9_18
Cai YChen ZWu J(2023)Simultaneous Auctions are Approximately Revenue-Optimal for Subadditive Bidders2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS57990.2023.00017(134-147)Online publication date: 6-Nov-2023
https://doi.org/10.1109/FOCS57990.2023.00017
Chen XDiakonikolas IOrfanou APaparas DSun XYannakakis M(2022)On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms for a Unit-Demand BuyerSIAM Journal on Computing10.1137/17M113648151:3(492-548)Online publication date: 12-May-2022
https://doi.org/10.1137/17M1136481
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Index Terms

Approximate revenue maximization with multiple items
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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Published In

EC '12: Proceedings of the 13th ACM Conference on Electronic Commerce

June 2012

1016 pages

ISBN:9781450314152

DOI:10.1145/2229012

General Chair:
Boi Faltings
École Polytechnique Fédérale de Lausanne (EPFL)
,
Program Chairs:
Kevin Leyton-Brown
University of British Columbia
,
Panos Ipeirotis
New York University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2012

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EC '12

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SIGecom

EC '12: ACM Conference on Electronic Commerce

June 4 - 8, 2012

Valencia, Spain

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

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Essaidi MGoldner KWeinberg S(2024)To Regulate or Not to Regulate: Using Revenue Maximization Tools to Maximize Consumer UtilityAlgorithmic Game Theory10.1007/978-3-031-71033-9_18(315-332)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-3-031-71033-9_18
Cai YChen ZWu J(2023)Simultaneous Auctions are Approximately Revenue-Optimal for Subadditive Bidders2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS57990.2023.00017(134-147)Online publication date: 6-Nov-2023
https://doi.org/10.1109/FOCS57990.2023.00017
Chen XDiakonikolas IOrfanou APaparas DSun XYannakakis M(2022)On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms for a Unit-Demand BuyerSIAM Journal on Computing10.1137/17M113648151:3(492-548)Online publication date: 12-May-2022
https://doi.org/10.1137/17M1136481
Chawla STeng YTzamos C(2022)Buy-many mechanisms are not much better than item pricingGames and Economic Behavior10.1016/j.geb.2022.04.003134(104-116)Online publication date: Jul-2022
https://doi.org/10.1016/j.geb.2022.04.003
Babaioff MGonczarowski YNisan N(2022)The menu-size complexity of revenue approximationGames and Economic Behavior10.1016/j.geb.2021.03.001134(281-307)Online publication date: Jul-2022
https://doi.org/10.1016/j.geb.2021.03.001
Vitercik EYan TRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Revenue maximization via machine learning with noisy dataProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541065(10510-10523)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541065
Cai YGoldner KMa SZhao MMarx D(2021)On multi-dimensional gains from trade maximizationProceedings of the Thirty-Second Annual ACM-SIAM Symposium on Discrete Algorithms10.5555/3458064.3458131(1079-1098)Online publication date: 10-Jan-2021
https://dl.acm.org/doi/10.5555/3458064.3458131
Gonczarowski YWeinberg S(2021)The Sample Complexity of Up-to-ε Multi-dimensional Revenue MaximizationJournal of the ACM10.1145/343972268:3(1-28)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3439722
Chawla STeng YTzamos C(2020)Buy-many mechanismsACM SIGecom Exchanges10.1145/3440959.344096318:1(12-18)Online publication date: 2-Dec-2020
https://dl.acm.org/doi/10.1145/3440959.3440963
Zaw TKyaw SOo APorkaew KChignell MFong SWatanapa B(2020)ARMA Model for Revenue PredictionProceedings of the 11th International Conference on Advances in Information Technology10.1145/3406601.3406617(1-6)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1145/3406601.3406617
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Mechanism for optimally trading off revenue and efficiency in multi-unit auctions

Auction mechanism for optimally trading off revenue and efficiency

Using lotteries to approximate the optimal revenue

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