research-article

Horizon-Independent Optimal Pricing in Repeated Auctions with Truthful and Strategic Buyers

Author:

Alexey DrutsaAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 33 - 42

https://doi.org/10.1145/3038912.3052700

Published: 03 April 2017 Publication History

Abstract

We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a (truthful or strategic) buyer that holds a fixed valuation. We focus on a practical situation in which the seller does not know in advance the number of played rounds (the time horizon) and has thus to use a horizon-independent pricing. First, we consider straightforward modifications of previously best known algorithms and show that these horizon-independent modifications have worser or even linear regret bounds. Second, we provide a thorough theoretical analysis of some broad families of consistent algorithms and show that there does not exist a no-regret horizon-independent algorithm in those families. Finally, we introduce a novel deterministic pricing algorithm that, on the one hand, is independent of the time horizon T and, on the other hand, has an optimal strategic regret upper bound in O(log log T). This result closes the logarithmic gap between the previously best known upper and lower bounds on strategic regret.

References

[1]

D. Agarwal, S. Ghosh, K. Wei, and S. You. Budget pacing for targeted online advertisements at linkedin. In KDD'2014, pages 1613--1619, 2014.

Digital Library

[2]

G. Aggarwal, G. Goel, and A. Mehta. Efficiency of (revenue-) optimal mechanisms. In EC'2009, pages 235--242, 2009.

Digital Library

[3]

G. Aggarwal, S. Muthukrishnan, D. Pál, and M. Pál. General auction mechanism for search advertising. In WWW'2009, pages 241--250, 2009.

Digital Library

[4]

K. Amin, M. Kearns, and U. Syed. Bandits, query learning, and the haystack dimension. In COLT, pages 87--106, 2011.

[5]

K. Amin, A. Rostamizadeh, and U. Syed. Learning prices for repeated auctions with strategic buyers. In NIPS'2013, pages 1169--1177, 2013.

Digital Library

[6]

K. Amin, A. Rostamizadeh, and U. Syed. Repeated contextual auctions with strategic buyers. In NIPS'2014, pages 622--630, 2014.

Digital Library

[7]

I. Ashlagi, C. Daskalakis, and N. Haghpanah. Sequential mechanisms with ex-post participation guarantees. In EC'2016, 2016.

Digital Library

[8]

I. Ashlagi, B. G. Edelman, and H. S. Lee. Competing ad auctions. Harvard Business School NOM Unit Working Paper, (10-055), 2013.

[9]

M. Babaioff, S. Dughmi, R. Kleinberg, and A. Slivkins. Dynamic pricing with limited supply. ACM Transactions on Economics and Computation, 3(1):4, 2015.

Digital Library

[10]

Y. Bachrach, S. Ceppi, I. A. Kash, P. Key, and D. Kurokawa. Optimising trade-offs among stakeholders in ad auctions. In EC'2014, pages 75--92, 2014.

Digital Library

[11]

S. Balseiro, O. Besbes, and G. Y. Weintraub. Dynamic mechanism design with budget constrained buyers under limited commitment. In EC'2016, 2016.

Digital Library

[12]

S. R. Balseiro, O. Besbes, and G. Y. Weintraub. Repeated auctions with budgets in ad exchanges: Approximations and design. Management Science, 61(4):864--884, 2015.

Digital Library

[13]

C. Borgs, J. Chayes, N. Immorlica, K. Jain, O. Etesami, and M. Mahdian. Dynamics of bid optimization in online advertisement auctions. In WWW'2007, pages 531--540, 2007.

Digital Library

[14]

L. E. Celis, G. Lewis, M. M. Mobius, and H. Nazerzadeh. Buy-it-now or take-a-chance: a simple sequential screening mechanism. In WWW'2011, pages 147--156, 2011.

Digital Library

[15]

N. Cesa-Bianchi, C. Gentile, and Y. Mansour. Regret minimization for reserve prices in second-price auctions. In SODA'2013, pages 1190--1204, 2013.

Digital Library

[16]

D. Charles, N. R. Devanur, and B. Sivan. Multi-score position auctions. In WSDM'2016, pages 417--425, 2016.

Digital Library

[17]

X. Chen and Z. Wang. Bayesian dynamic learning and pricing with strategic customers. Available at SSRN 2715730, 2016.

[18]

Y. Chen and V. F. Farias. Robust dynamic pricing with strategic customers. In EC'2015, pages 777--777, 2015.

Digital Library

[19]

M. Chhabra and S. Das. Learning the demand curve in posted-price digital goods auctions. In ICAAMS'2011, pages 63--70, 2011.

Digital Library

[20]

M. C. Cohen, I. Lobel, and R. Paes Leme. Feature-based dynamic pricing. In EC'2016, 2016.

Digital Library

[21]

A. V. den Boer. Dynamic pricing and learning: historical origins, current research, and new directions. Surveys in operations research and management science, 20(1):1--18, 2015.

[22]

P. Dütting, M. Henzinger, and I. Weber. An expressive mechanism for auctions on the web. In WWW'2011, pages 127--136, 2011.

Digital Library

[23]

B. Edelman and M. Ostrovsky. Strategic bidder behavior in sponsored search auctions. Decision support systems, 43(1):192--198, 2007.

Digital Library

[24]

G. Goel and M. R. Khani. Revenue monotone mechanisms for online advertising. In WWW'2014, pages 723--734, 2014.

Digital Library

[25]

R. Gomes and V. Mirrokni. Optimal revenue-sharing double auctions with applications to ad exchanges. In WWW'2014, pages 19--28, 2014.

Digital Library

[26]

D. He, W. Chen, L. Wang, and T.-Y. Liu. A game-heoretic machine learning approach for revenue maximization in sponsored search. In IJCAI'2013, pages 206--212, 2013.

Digital Library

[27]

H. Heidari, M. Mahdian, U. Syed, S. Vassilvitskii, and S. Yazdanbod. Pricing a low-regret seller. In ICML'2016, pages 2559--2567, 2016.

Digital Library

[28]

P. Hummel and P. McAfee. Machine learning in an auction environment. In WWW'2014, pages 7--18, 2014.

Digital Library

[29]

K. Iyer, R. Johari, and M. Sundararajan. Mean field equilibria of dynamic auctions with learning. ACM SIGecom Exchanges, 10(3):10--14, 2011.

Digital Library

[30]

Y. Kanoria and H. Nazerzadeh. Dynamic reserve prices for repeated auctions: Learning from bids. Available at SSRN 2444495, 2014.

[31]

R. Kleinberg and T. Leighton. The value of knowing a demand curve: Bounds on regret for online posted-price auctions. In Foundations of Computer Science, pages 594--605, 2003.

Digital Library

[32]

V. Krishna. Auction theory. Academic press, 2009.

[33]

T. Lin, J. Li, and W. Chen. Stochastic online greedy learning with semi-bandit feedbacks. In NIPS'2015, pages 352--360, 2015.

Digital Library

[34]

B. Lucier, R. Paes Leme, and E. Tardos. On revenue in the generalized second price auction. In WWW'2012, pages 361--370, 2012.

Digital Library

[35]

M. Mohri and A. M. Medina. Learning theory and algorithms for revenue optimization in second price auctions with reserve. In ICML'2014, pages 262--270, 2014.

Digital Library

[36]

M. Mohri and A. M. Medina. Non-parametric revenue optimization for generalized second price auctions. In UAI'2015, 2015.

Digital Library

[37]

M. Mohri and A. Munoz. Optimal regret minimization in posted-price auctions with strategic buyers. In NIPS'2014, pages 1871--1879, 2014.

Digital Library

[38]

M. Mohri and A. Munoz. Revenue optimization against strategic buyers. In NIPS'2015, pages 2530--2538, 2015.

Digital Library

[39]

J. H. Morgenstern and T. Roughgarden. On the pseudo-dimension of nearly optimal auctions. In NIPS'2014, pages 136--144, 2015.

Digital Library

[40]

R. B. Myerson. Optimal auction design. Mathematics of operations research, 6(1):58--73, 1981.

Digital Library

[41]

G. Noti, N. Nisan, and I. Yaniv. An experimental evaluation of bidders' behavior in ad auctions. In WWW'2014, pages 619--630, 2014.

Digital Library

[42]

M. Ostrovsky and M. Schwarz. Reserve prices in internet advertising auctions: A field experiment. In EC'2011, pages 59--60, 2011.

Digital Library

[43]

R. Paes Leme, M. Pál, and S. Vassilvitskii. A field guide to personalized reserve prices. In WWW'2016, pages 1093--1102, 2016.

Digital Library

[44]

T. Roughgarden and J. R. Wang. Minimizing regret with multiple reserves. In EC'2016, pages 601--616, 2016.

Digital Library

[45]

M. R. Rudolph, J. G. Ellis, and D. M. Blei. Objective variables for probabilistic revenue maximization in second-price auctions with reserve. In WWW'2016, pages 1113--1122, 2016.

Digital Library

[46]

Y. Sun, Y. Zhou, and X. Deng. Optimal reserve prices in weighted gsp auctions. Electronic Commerce Research and Applications, 13(3):178--187, 2014.

[47]

D. R. Thompson and K. Leyton-Brown. Revenue optimization in the generalized second-price auction. In EC'2013, pages 837--852, 2013.

Digital Library

[48]

H. R. Varian. Position auctions. international Journal of industrial Organization, 25(6):1163--1178, 2007.

[49]

H. R. Varian. Online ad auctions. The American Economic Review, 99(2):430--434, 2009.

[50]

H. R. Varian and C. Harris. The vcg auction in theory and practice. The Amer. Econ. Rev., 104(5):442--445, 2014.

[51]

J. Weed, V. Perchet, and P. Rigollet. Online learning in repeated auctions. JMLR, 49:1--31, 2016.

[52]

S. Yuan, J. Wang, B. Chen, P. Mason, and S. Seljan. An empirical study of reserve price optimisation in real-time bidding. In KDD'2014, pages 1897--1906, 2014.

Digital Library

[53]

Y. Zhu, G. Wang, J. Yang, D. Wang, J. Yan, J. Hu, and Z. Chen. Optimizing search engine revenue in sponsored search. In SIGIR'2009, pages 588--595, 2009.

Digital Library

[54]

M. Zoghi, Z. S. Karnin, S. Whiteson, and M. De Rijke. Copeland dueling bandits. In NIPS'2015, pages 307--315, 2015.

Digital Library

Cited By

Huh JKandasamy KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Nash incentive-compatible online mechanism learning via weakly differentially private online learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692898(20643-20659)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692898
Castro Fernandez RIves ZBonifati AEl Abbadi A(2022)Protecting Data Markets from Strategic BuyersProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517855(1755-1769)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517855
Leme RSchneider J(2022)Contextual Search via Intrinsic VolumesSIAM Journal on Computing10.1137/20M138571851:4(1096-1125)Online publication date: 18-Jul-2022
https://doi.org/10.1137/20M1385718
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Index Terms

Horizon-Independent Optimal Pricing in Repeated Auctions with Truthful and Strategic Buyers
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Electronic commerce
        Online auctions
2. Theory of computation
  1. Design and analysis of algorithms
    1. Online algorithms
      1. Online learning algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Regret bounds

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cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 03 April 2017

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WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Huh JKandasamy KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Nash incentive-compatible online mechanism learning via weakly differentially private online learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692898(20643-20659)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692898
Castro Fernandez RIves ZBonifati AEl Abbadi A(2022)Protecting Data Markets from Strategic BuyersProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517855(1755-1769)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517855
Leme RSchneider J(2022)Contextual Search via Intrinsic VolumesSIAM Journal on Computing10.1137/20M138571851:4(1096-1125)Online publication date: 18-Jul-2022
https://doi.org/10.1137/20M1385718
Deng YZhang HRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Prior-independent dynamic auctions for a value-maximizing buyerProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541322(13847-13858)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541322
Rhuggenaath JOliveira da Costa PZhang YAkcay AKaymak U(2021)Low-Regret Algorithms for Strategic Buyers with Unknown Valuations in Repeated Posted-Price AuctionsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-67661-2_25(416-436)Online publication date: 25-Feb-2021
https://doi.org/10.1007/978-3-030-67661-2_25
Zhiyanov ADrutsa ADaumé HSingh A(2020)Bisection-based pricing for repeated contextual auctions against strategic buyerProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3526001(11469-11480)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3526001
Drutsa ADaumé HSingh A(2020)Reserve pricing in repeated second-price auctions with strategic biddersProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525189(2678-2689)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525189
Drutsa ADaumé HSingh A(2020)Optimal non-parametric learning in repeated contextual auctions with strategic buyerProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525188(2668-2677)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525188
Deng YLahaie SMirrokni VDaumé HSingh A(2020)Robust pricing in dynamic mechanism designProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525171(2494-2503)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525171
Liu APaes Leme RSchneider JLarochelle HRanzato MHadsell RBalcan MLin H(2020)Myersonian regressionProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3496490(9135-9144)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3496490
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