Abstract
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 language | English |
|---|---|
| Title of host publication | Computing and Combinatorics - 12th Annual International Conference, COCOON 2006, Proceedings |
| Publisher | Springer Verlag |
| Pages | 33-41 |
| Number of pages | 9 |
| ISBN (Print) | 3540369252, 9783540369257 |
| Publication status | Published - Jan 1 2006 |
| Event | 12th Annual International Conference on Computing and Combinatorics, COCOON 2006 - Taipei, Taiwan, Province of China Duration: Aug 15 2006 → Aug 18 2006 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4112 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 12th Annual International Conference on Computing and Combinatorics, COCOON 2006 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taipei |
| Period | 8/15/06 → 8/18/06 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
Aggregating strategy for online auctions. / Harada, Shigeaki; Takimoto, Eiji; Maruoka, Akira.
Computing and Combinatorics - 12th Annual International Conference, COCOON 2006, Proceedings. Springer Verlag, 2006. p. 33-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4112 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Aggregating strategy for online auctions
AU - Harada, Shigeaki
AU - Takimoto, Eiji
AU - Maruoka, Akira
PY - 2006/1/1
Y1 - 2006/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33749579483&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749579483&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33749579483
SN - 3540369252
SN - 9783540369257
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 33
EP - 41
BT - Computing and Combinatorics - 12th Annual International Conference, COCOON 2006, Proceedings
PB - Springer Verlag
ER -