Publication Type

Conference Proceeding Article

Publication Date

8-2009

Abstract

The success of an auction design often hinges on its ability to set parameters such as reserve price and bid levels that will maximize an objective function such as the auctioneer revenue. Works on designing adaptive auction mechanisms have emerged recently, and the challenge is in learning different auction parameters by observing the bidding in previous auctions. In this paper, we propose a non-parametric method for determining discrete bid levels dynamically so as to maximize the auctioneer revenue. First, we propose a non-parametric kernel method for estimating the probabilities of closing price with past auction data. Then a greedy strategy has been devised to determine the discrete bid levels based on the estimated probability information of closing price. We show experimentally that our non-parametric method is robust to changes in parameters such as the distributions of participating bidders as well as the individual bidder evaluation, and it consistently outperforms different competitors with various settings with respect to auctioneer revenue maximization.

Keywords

Adaptive auction, Bid levels, Greedy method, Kernel density estimation

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics; Data Management and Analytics

Publication

ICEC '09: Proceedings of the 11th International Conference on Electronic Commerce, August 12-15, 2009, Taipei

First Page

195

Last Page

204

ISBN

9781605585864

Identifier

10.1145/1593254.1593284

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/1593254.1593284