Models for Truthful Online Double Auctions
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CitationBredin, 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.
AbstractOnline 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4031550
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