Computing and Combinatorics

Volume 4112 of the series Lecture Notes in Computer Science pp 33-41

Aggregating Strategy for Online Auctions

  • Shigeaki HaradaAffiliated withNTT Service Integration Laboratories
  • , Eiji TakimotoAffiliated withGraduate School of Information Sciences, Tohoku University
  • , Akira MaruokaAffiliated withGraduate School of Information Sciences, Tohoku University

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We consider the online auction problem in which an auctioneer is selling an identical item each time when a new bidder arrives. It is known that results from online prediction can be applied and achieve a constant competitive ratio with respect to the best fixed price profit. These algorithms work on a predetermined set of price levels. We take into account the property that the rewards for the price levels are not independent and cast the problem as a more refined model of online prediction. We then use Vovk’s Aggregating Strategy to derive a new algorithm. We give a general form of competitive ratio in terms of the price levels. The optimality of the Aggregating Strategy gives an evidence that our algorithm performs at least as well as the previously proposed ones.