Models for Truthful Online Double Auctions

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Models for Truthful Online Double Auctions

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Title: Models for Truthful Online Double Auctions
Author: Bredin, Jonathan; Parkes, David C.

Note: Order does not necessarily reflect citation order of authors.

Citation: Bredin, Jonathan, and David C. Parkes. 2005. Models for truthful online double auctions. In Uncertainty in artificial intelligence: Proceedings of the Twenty-First Conference: July 26-29, 2005, Edinburgh, Scotland, ed. F. Bacchus, T. Jaakkola, et al., 50-59. Corvallis, Oregon: AUAI Press.
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Abstract: Online double auctions (DAs) model a dynamic two-sided matching problem with private information and self-interest, and are relevant for dynamic resource and task allocation problems. We present a general method to design truthful DAs, such that no agent can benefit from misreporting its arrival time, duration, or value. The family of DAs is parameterized by a pricing rule, and includes a generalization of McAfee’s truthful DA to this dynamic setting. We present an empirical study, in which we study the allocative-surplus and agent surplus for a number of different DAs. Our results illustrate that dynamic pricing rules are important to provide good market efficiency for markets with high volatility or low volume.
Published Version: http://www.informatik.uni-trier.de/~ley/db/conf/uai/uai2005.html
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/uai05.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4031550
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