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Bidding Strategies for Simultaneous Ascending Auctions

dc.contributor.authorWellman, Michael P.
dc.contributor.authorOsepayshvili, Anna V.
dc.contributor.authorMacKie-Mason, Jeffrey K.
dc.contributor.authorReeves, Daniel M.
dc.date.accessioned2008-01-23T23:19:49Z
dc.date.available2008-01-23T23:19:49Z
dc.date.issued2008-01-23
dc.identifier.urihttps://hdl.handle.net/2027.42/57741
dc.description.abstractSimultaneous ascending auctions present agents with various strategic problems, depending on preference structure. As long as bids represent non-repudiable offers, submitting non-contingent bids to separate auctions entails an \emph{exposure problem}: bidding to acquire a bundle risks the possibility of obtaining an undesired subset of the goods. With multiple goods (or units of a homogeneous good) bidders also need to account for their own effects on prices. Auction theory does not provide analytic solutions for optimal bidding strategies in the face of these problems. We present a new family of decision-theoretic bidding strategies that use probabilistic predictions of final prices: \emph{self-confirming distribution-prediction} strategies. Bidding based on these is provably not optimal in general. But evidence using empirical game-theoretic methods we developed indicates the strategy is quite effective compared to other known methods when preferences exhibit complementarities. When preferences exhibit substitutability, simpler \emph{demand-reduction} strategies address the own price effect problem more directly and perform better.en_US
dc.format.extent153700 bytes
dc.format.extent286581 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.subjectAuctionsen_US
dc.subjectGame Theoryen_US
dc.subjectSchedulingen_US
dc.subjectStrategiesen_US
dc.titleBidding Strategies for Simultaneous Ascending Auctionsen_US
dc.typeWorking Paperen_US
dc.subject.hlbsecondlevelInformation and Library Science
dc.subject.hlbtoplevelSocial Sciences
dc.contributor.affiliationumComputer Science, Division ofen_US
dc.contributor.affiliationumInformation, School ofen_US
dc.contributor.affiliationumInformation, School ofen_US
dc.contributor.affiliationotherYahoo! Researchen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/57741/2/saa-appendix.pdfen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/57741/1/ppsaa.pdfen_US
dc.owningcollnameInformation, School of (SI)


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