Bidding Strategies for Simultaneous Ascending Auctions
dc.contributor.author | Wellman, Michael P. | |
dc.contributor.author | Osepayshvili, Anna V. | |
dc.contributor.author | MacKie-Mason, Jeffrey K. | |
dc.contributor.author | Reeves, Daniel M. | |
dc.date.accessioned | 2008-01-23T23:19:49Z | |
dc.date.available | 2008-01-23T23:19:49Z | |
dc.date.issued | 2008-01-23 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/57741 | |
dc.description.abstract | Simultaneous 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.extent | 153700 bytes | |
dc.format.extent | 286581 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | en_US |
dc.subject | Auctions | en_US |
dc.subject | Game Theory | en_US |
dc.subject | Scheduling | en_US |
dc.subject | Strategies | en_US |
dc.title | Bidding Strategies for Simultaneous Ascending Auctions | en_US |
dc.type | Working Paper | en_US |
dc.subject.hlbsecondlevel | Information and Library Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.contributor.affiliationum | Computer Science, Division of | en_US |
dc.contributor.affiliationum | Information, School of | en_US |
dc.contributor.affiliationum | Information, School of | en_US |
dc.contributor.affiliationother | Yahoo! Research | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/57741/2/saa-appendix.pdf | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/57741/1/ppsaa.pdf | en_US |
dc.owningcollname | Information, School of (SI) |
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