Article

Online Learning in Online Auctions

University of California, Berkeley, Berkeley, California, United States
Theoretical Computer Science (Impact Factor: 0.52). 08/2003; 324(2-3). DOI: 10.1016/j.tcs.2004.05.012
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ABSTRACT We consider 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 for digital goods that achieves a constant competitive ratio with respect to the best possible (o#ine) fixed price revenue. This substantially improves upon the best previously known competitive ratio [3] of O(exp( # log log h)) for this problem. We apply our techniques to the related problem of online posted price mechanisms, where the auctioneer declares a price and a bidder only communicates his acceptance/rejection of the price. For this problem we obtain results that are (somewhat surprisingly) similar to the online auction problem.

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