Derandomization of auctions
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Sudan_Derandomization of.pdf
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Author(s)
Aggarwal, Gagan
Fiat, Amos
Goldberg, Andrew V.
Hartline, Jason D.
Immorlica, Nicole
Sudan, Madhu
Date Issued
August 2010
Journal
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1016/j.geb.2010.07.007
