On Random Sampling Auctions for Digital Goods

Author:

Alaei Saeed1,Malekian Azarakhsh1,Srinivasan Aravind1

Affiliation:

1. University of Maryland

Abstract

In the context of auctions for digital goods, an interesting random sampling auction has been proposed by Goldberg et al. [2001]. This auction has been analyzed by Feige et al. [2005], who have shown that it obtains in expectation at least 1/15 fraction of the optimal revenue, which is substantially better than the previously proven constant bounds but still far from the conjectured lower bound of 1/4. In this article, we prove that the aforementioned random sampling auction obtains at least 1/4 fraction of the optimal revenue for a large class of instances where the number of bids above (or equal to) the optimal sale price is at least 6. We also show that this auction obtains at least 1/4.68 fraction of the optimal revenue for the small class of remaining instances, thus leaving a negligible gap between the lower and upper bound. We employ a mix of probabilistic techniques and dynamic programming to compute these bounds.

Funder

Division of Computing and Communication Foundations

Division of Computer and Network Systems

Publisher

Association for Computing Machinery (ACM)

Subject

Computational Mathematics,Marketing,Economics and Econometrics,Statistics and Probability,Computer Science (miscellaneous)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Provision-After-Wait with Common Preferences;ACM Transactions on Economics and Computation;2017-05-31

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