Aggregating strategy for online auctions

Shigeaki Harada, Eiji Takimoto, Akira Maruoka

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationComputing and Combinatorics - 12th Annual International Conference, COCOON 2006, Proceedings
PublisherSpringer Verlag
Number of pages9
ISBN (Print)3540369252, 9783540369257
Publication statusPublished - 2006
Externally publishedYes
Event12th Annual International Conference on Computing and Combinatorics, COCOON 2006 - Taipei, Taiwan, Province of China
Duration: Aug 15 2006Aug 18 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th Annual International Conference on Computing and Combinatorics, COCOON 2006
Country/TerritoryTaiwan, Province of China

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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