Efficient empirical revenue maximization in single-parameter auction environments?

Yannai A. Gonczarowski; Noam Nisan

doi:10.1145/3055399.3055427

Efficient empirical revenue maximization in single-parameter auction environments?

Yannai A. Gonczarowski, Noam Nisan

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

46 Scopus citations

Abstract

We present a polynomial-time algorithm that, given samples from the unknown valuation distribution of each bidder, learns an auction that approximately maximizes the auctioneer's revenue in a variety of single-parameter auction environments including matroid environments, position environments, and the public project environment. The valuation distributions may be arbitrary bounded distributions (in particular, they may be irregular, and may differ for the various bidders), thus resolving a problem left open by previous papers. The analysis uses basic tools, is performed in its entirety in value-space, and simplifies the analysis of previously known results for special cases. Furthermore, the analysis extends to certain single-parameter auction environments where precise revenue maximization is known to be intractable, such as knapsack environments.

Original language	English
Title of host publication	STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing
Editors	Pierre McKenzie, Valerie King, Hamed Hatami
Publisher	Association for Computing Machinery
Pages	856-868
Number of pages	13
ISBN (Electronic)	9781450345286
DOIs	https://doi.org/10.1145/3055399.3055427
State	Published - 19 Jun 2017
Event	49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017 - Montreal, Canada Duration: 19 Jun 2017 → 23 Jun 2017

Publication series

Name	Proceedings of the Annual ACM Symposium on Theory of Computing
Volume	Part F128415
ISSN (Print)	0737-8017

Conference

Conference	49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017
Country/Territory	Canada
City	Montreal
Period	19/06/17 → 23/06/17

Bibliographical note

Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

Keywords

Approximate revenue maximization
PAC learning

Access to Document

10.1145/3055399.3055427

Cite this

Gonczarowski, Y. A., & Nisan, N. (2017). Efficient empirical revenue maximization in single-parameter auction environments? In P. McKenzie, V. King, & H. Hatami (Eds.), STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (pp. 856-868). (Proceedings of the Annual ACM Symposium on Theory of Computing; Vol. Part F128415). Association for Computing Machinery. https://doi.org/10.1145/3055399.3055427

Gonczarowski, Yannai A. ; Nisan, Noam. / Efficient empirical revenue maximization in single-parameter auction environments?. STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing. editor / Pierre McKenzie ; Valerie King ; Hamed Hatami. Association for Computing Machinery, 2017. pp. 856-868 (Proceedings of the Annual ACM Symposium on Theory of Computing).

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title = "Efficient empirical revenue maximization in single-parameter auction environments?",

abstract = "We present a polynomial-time algorithm that, given samples from the unknown valuation distribution of each bidder, learns an auction that approximately maximizes the auctioneer's revenue in a variety of single-parameter auction environments including matroid environments, position environments, and the public project environment. The valuation distributions may be arbitrary bounded distributions (in particular, they may be irregular, and may differ for the various bidders), thus resolving a problem left open by previous papers. The analysis uses basic tools, is performed in its entirety in value-space, and simplifies the analysis of previously known results for special cases. Furthermore, the analysis extends to certain single-parameter auction environments where precise revenue maximization is known to be intractable, such as knapsack environments.",

keywords = "Approximate revenue maximization, PAC learning",

author = "Gonczarowski, {Yannai A.} and Noam Nisan",

note = "Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017 ; Conference date: 19-06-2017 Through 23-06-2017",

year = "2017",

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series = "Proceedings of the Annual ACM Symposium on Theory of Computing",

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Gonczarowski, YA & Nisan, N 2017, Efficient empirical revenue maximization in single-parameter auction environments? in P McKenzie, V King & H Hatami (eds), STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing. Proceedings of the Annual ACM Symposium on Theory of Computing, vol. Part F128415, Association for Computing Machinery, pp. 856-868, 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017, Montreal, Canada, 19/06/17. https://doi.org/10.1145/3055399.3055427

Efficient empirical revenue maximization in single-parameter auction environments? / Gonczarowski, Yannai A.; Nisan, Noam.
STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing. ed. / Pierre McKenzie; Valerie King; Hamed Hatami. Association for Computing Machinery, 2017. p. 856-868 (Proceedings of the Annual ACM Symposium on Theory of Computing; Vol. Part F128415).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review