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Title:	Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment
Author:	Parkes, David C.; Ungar, Lyle H. Note: Order does not necessarily reflect citation order of authors.
Citation:	Parkes, David C., and Lyle H. Ungar. 2000. Preventing strategic manipulation in iterative auctions: Proxy agents and price-adjustment. In Proceedings: Seventeenth National Conference on Artificial Intelligence (AAAI-2000): Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000), ed. American Association for Artificial Intelligence, 82-89. Menlo Park, C.A.: AAAI Press; Cambridge, M.A.: MIT Press.
Full Text & Related Files:	Parkes_Proventing.pdf (125.8Kb; PDF)
Abstract:	Iterative auctions have many computational advantages over sealed-bid auctions, but can present new possibilities for strategic manipulation. We propose a two-stage technique to make iterative auctions that compute optimal allocations with myopic best-response bidding strategies more robust to manipulation. First, introduce proxy bidding agents to constrain bidding strategies to (possibly untruthful) myopic bestresponse. Second, after the auction terminates adjust the prices towards those given in the Vickrey auction, a sealedbid auction in which truth-revelation is optimal. We present an application of this methodology to iBundle, an iterative combinatorial auction which gives optimal allocations for myopic best-response agents.
Published Version:	https://www.aaai.org/Papers/AAAI/2000/AAAI00-013.pdf
Other Sources:	http://www.eecs.harvard.edu/econcs/pubs/iGVA00.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:4101024

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