Uncertainty in Artificial Intelligence
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Models for Truthful Online Double Auctions
Jonathan Bredin, David Parkes
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.
Keywords:
Pages: 50-59
PS Link:
PDF Link: /papers/05/p50-bredin.pdf
BibTex:
@INPROCEEDINGS{Bredin05,
AUTHOR = "Jonathan Bredin and David Parkes",
TITLE = "Models for Truthful Online Double Auctions",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "50--59"
}


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