Derandomization of auctions
Author(s)
Aggarwal, Gagan; Fiat, Amos; Goldberg, Andrew V.; Hartline, Jason D.; Immorlica, Nicole; Sudan, Madhu; ... Show more Show less
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We study the role of randomization in seller optimal (i.e., profit maximization) auctions. Bayesian optimal auctions (e.g., Myerson, 1981) assume that the valuations of the agents are random draws from a distribution and prior-free optimal auctions either are randomized (e.g., Goldberg et al., 2006) or assume the valuations are randomized (e.g., Segal, 2003). Is randomization fundamental to profit maximization in auctions? Our main result is a general approach to derandomize single-item multi-unit unit-demand auctions while approximately preserving their performance (i.e., revenue). Our general technique is constructive but not computationally tractable. We complement the general result with the explicit and computationally-simple derandomization of a particular auction. Our results are obtained through analogy to hat puzzles that are interesting in their own right.
Date issued
2010-08Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Games and Economic Behavior
Publisher
Elsevier
Citation
Aggarwal, Gagan, Amos Fiat, Andrew V. Goldberg, Jason D. Hartline, Nicole Immorlica, and Madhu Sudan. “Derandomization of Auctions.” Games and Economic Behavior 72, no. 1 (May 2011): 1–11.
Version: Author's final manuscript
ISSN
08998256
1090-2473