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Revenue maximization with a single sample

Revenue maximization with a single sample Revenue Maximization with a Single Sample Peerapong Dhangwatnotai — Department of Computer Science, Stanford University 460 Gates Building, 353 Serra Mall, Stanford, CA Tim Roughgarden Department of Computer Science, Stanford University 462 Gates Building, 353 Serra Mall, Stanford, CA Qiqi Yan ¡ Department of Computer Science, Stanford University 460 Gates Building, 353 Serra Mall, Stanford, CA tim@cs.stanford.edu qiqiyan@cs.stanford.edu pdh@cs.stanford.edu ABSTRACT We design and analyze approximately revenue-maximizing auctions in general single-parameter settings. Bidders have publicly observable attributes, and we assume that the valuations of indistinguishable bidders are independent draws from a common distribution. Crucially, we assume all valuation distributions are a priori unknown to the seller. Despite this handicap, we show how to obtain approximately optimal expected revenue ” nearly as large as what could be obtained if the distributions were known in advance ” under quite general conditions. Our most general result concerns arbitrary downwardclosed single-parameter environments and valuation distributions that satisfy a standard hazard rate condition. We also assume that no bidder has a unique attribute value, which is obviously necessary with unknown and attributedependent valuation distributions. Here, we give an auction that, for every such environment and unknown valuation distributions, has expected revenue at least a http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Revenue maximization with a single sample

Association for Computing Machinery — Jun 7, 2010

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Datasource
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
ISBN
978-1-60558-822-3
doi
10.1145/1807342.1807364
Publisher site
See Article on Publisher Site

Abstract

Revenue Maximization with a Single Sample Peerapong Dhangwatnotai — Department of Computer Science, Stanford University 460 Gates Building, 353 Serra Mall, Stanford, CA Tim Roughgarden Department of Computer Science, Stanford University 462 Gates Building, 353 Serra Mall, Stanford, CA Qiqi Yan ¡ Department of Computer Science, Stanford University 460 Gates Building, 353 Serra Mall, Stanford, CA tim@cs.stanford.edu qiqiyan@cs.stanford.edu pdh@cs.stanford.edu ABSTRACT We design and analyze approximately revenue-maximizing auctions in general single-parameter settings. Bidders have publicly observable attributes, and we assume that the valuations of indistinguishable bidders are independent draws from a common distribution. Crucially, we assume all valuation distributions are a priori unknown to the seller. Despite this handicap, we show how to obtain approximately optimal expected revenue ” nearly as large as what could be obtained if the distributions were known in advance ” under quite general conditions. Our most general result concerns arbitrary downwardclosed single-parameter environments and valuation distributions that satisfy a standard hazard rate condition. We also assume that no bidder has a unique attribute value, which is obviously necessary with unknown and attributedependent valuation distributions. Here, we give an auction that, for every such environment and unknown valuation distributions, has expected revenue at least a

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