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SODA
2003
ACM

Online learning in online auctions

9 years 2 months ago
Online learning in online auctions
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 (offline) 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. 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...
Avrim Blum, Vijay Kumar, Atri Rudra, Felix Wu
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2003
Where SODA
Authors Avrim Blum, Vijay Kumar, Atri Rudra, Felix Wu
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