Setting discrete bid levels adaptively in repeated auctions

• Main Authors: ZHANG, Jilian, LAU, Hoong Chuin, SHEN, Jialie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Adaptive auction; Bid levels; Greedy method; Kernel density estimation; Artificial Intelligence and Robotics; Databases and Information Systems; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/517; https://ink.library.smu.edu.sg/context/sis_research/article/1516/viewcontent/p195_zhang.pdf
• Institution: Singapore Management University

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.