Article

Online learning in online auctions

Authors:

Avrim Blum,

Vijay Kumar,

Atri Rudra,

Felix WuAuthors Info & Claims

SODA '03: Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms

Pages 202 - 204

Published: 12 January 2003 Publication History

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References

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Cited By

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Hartline JLong SZhang C(2024)Regulation of Algorithmic CollusionProceedings of the Symposium on Computer Science and Law10.1145/3614407.3643706(98-108)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3614407.3643706
Kleinberg RSlivkins AUpfal E(2019)Bandits and Experts in Metric SpacesJournal of the ACM10.1145/329987366:4(1-77)Online publication date: 31-May-2019
https://dl.acm.org/doi/10.1145/3299873
Tong YWang LZhou ZChen LDu BYe JDas GJermaine CBernstein P(2018)Dynamic Pricing in Spatial CrowdsourcingProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196929(773-788)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3196929
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Index Terms

Online learning in online auctions
1. Applied computing
  1. Electronic commerce
2. Computing methodologies
  1. Machine learning

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Reviews

Reviewer: Barrett Hazeltine

Performance of an online auction can be improved by an algorithm that learns from the set of bids already made. An online auction receives bids, and deals with each individually, deciding whether to accept a bid or wait for a higher one. This paper sets bounds on the performance of a learning algorithm, and shows that, for moderately long auctions, the performance is better than that of an existing algorithm. The proofs use formal mathematics, which are difficult to summarize. The flavor of the paper may be given by a simplification of one result: For any function f(h) = o(h log log h) , where h is the ratio between the highest and lowest bids, if the sequences of bids made is greater than or equal to f(h) , then the revenue generated is comparable to that of an optimal offline auction. The paper seems to be intended for those mathematically inclined, and does not focus on the mechanics of the algorithm.

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Published In

SODA '03: Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms

January 2003

891 pages

ISBN:0898715385

Publisher

Society for Industrial and Applied Mathematics

United States

Publication History

Published: 12 January 2003

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Overall Acceptance Rate 411 of 1,322 submissions, 31%

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Hartline JLong SZhang C(2024)Regulation of Algorithmic CollusionProceedings of the Symposium on Computer Science and Law10.1145/3614407.3643706(98-108)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3614407.3643706
Kleinberg RSlivkins AUpfal E(2019)Bandits and Experts in Metric SpacesJournal of the ACM10.1145/329987366:4(1-77)Online publication date: 31-May-2019
https://dl.acm.org/doi/10.1145/3299873
Tong YWang LZhou ZChen LDu BYe JDas GJermaine CBernstein P(2018)Dynamic Pricing in Spatial CrowdsourcingProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196929(773-788)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3196929
Badanidiyuru AKleinberg RSlivkins A(2018)Bandits with KnapsacksJournal of the ACM10.1145/316453965:3(1-55)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1145/3164539
Han KHe YTan HTang SHuang HLuo J(2017)Online Pricing for Mobile Crowdsourcing with Multi-Minded UsersProceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing10.1145/3084041.3084058(1-10)Online publication date: 10-Jul-2017
https://dl.acm.org/doi/10.1145/3084041.3084058
Zheng ZPeng YWu FTang SChen G(2017)An Online Pricing Mechanism for Mobile Crowdsensing Data MarketsProceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing10.1145/3084041.3084044(1-10)Online publication date: 10-Jul-2017
https://dl.acm.org/doi/10.1145/3084041.3084044
Chawla SDevanur NKarlin ASivan B(2016)Simple pricing schemes for consumers with evolving valuesProceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete algorithms10.5555/2884435.2884536(1476-1490)Online publication date: 10-Jan-2016
https://dl.acm.org/doi/10.5555/2884435.2884536
Jalaparti VBliznets IKandula SLucier BMenache IBarcellos MCrowcroft JVahdat AKatti S(2016)Dynamic Pricing and Traffic Engineering for Timely Inter-Datacenter TransfersProceedings of the 2016 ACM SIGCOMM Conference10.1145/2934872.2934893(73-86)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1145/2934872.2934893
Hsu JMorgenstern JRogers RRoth AVohra RWichs DMansour Y(2016)Do prices coordinate markets?Proceedings of the forty-eighth annual ACM symposium on Theory of Computing10.1145/2897518.2897559(440-453)Online publication date: 19-Jun-2016
https://dl.acm.org/doi/10.1145/2897518.2897559
Babaioff MDughmi SKleinberg RSlivkins A(2015)Dynamic Pricing with Limited SupplyACM Transactions on Economics and Computation10.1145/25591523:1(1-26)Online publication date: 27-Mar-2015
https://dl.acm.org/doi/10.1145/2559152
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