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Online learning in online auctions. (English) Zbl 1094.68620
Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms, Baltimore, MD, USA, January 12–14, 2003. New York, NY: Association for Computing Machinery; Philadelphia, PA: Society for Industrial and Applied Mathematics (ISBN 0-89871-538-5/pbk). 202-204 (2003).
Summary: We consider here the problem of revenue maximization in online auctions, that is, auctions in which bids are received and dealt with one-by-one. In this note, we demonstrate that results from online learning can be usefully applied in this context, and we derive a new auction which substantially improves upon the performance of previous auctions for this problem.
We are primarily concerned with auctions for a single good available in unlimited supply, often described as a digital good, though our techniques may also be useful for the case of limited supply. The problem of designing online auctions for digital goods was first described by Z. Bar-Yossef, K. Hildrum and F. Wu [“Incentive-compatible online auctions for digital goods”, in: Proceedings of the 13th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 964–970 (2002)], one of a number of recent papers interested in analyzing revenue-maximizing auctions without malting statistical assumptions about the bidders who participate in the auction.
For the entire collection see [Zbl 1006.00017].

MSC:
68T05 Learning and adaptive systems in artificial intelligence
68M10 Network design and communication in computer systems
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