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