On best-response bidding in GSP auctions
By: Matthew Cary, Aparna Das, Benjamin G. Edelman, Ioannis Giotis, Kurtis Heimerl, Anna R. Karlin, Claire Mathieu, Michael Schwarz
Contributors: Das, Aparna; Edelman, Benjamin; Giotis, Ioannis; Heimerl, Kurtis; Karlin, Anna R.; Mathieu, Claire; Schwarz, Michael, 1970-; Harvard Business School
Series: Working paper / Harvard Business School -- 08-056; Working paper (Harvard Business School) -- 08-056
Publisher: Harvard Business School
Published: 2008
Place of publication: Boston]
Language: eng
Number of pages: 19
Description: How should players bid in keyword auctions such as those used by Google, Yahoo! and MSN? We model ad auctions as a dynamic game of incomplete information, so we can study the convergence and robustness properties of various strategies. In particular, we consider best-response bidding strategies for a repeated auction on a single keyword, where in each round, each player chooses some optimal bid for the next round, assuming that the other players merely repeat their previous bids. We focus on a strategy we call Balanced Bidding (BB). If all players use the BB strategy, we show that bids converge to a bid vector that obtains in a complete information static model proposed by Edelman, Ostrovsky, and Schwarz. We prove that convergence occurs with probability 1, and we compute the expected time until convergence.
Notes: "January 2008"--Publisher's web site.

Includes bibliographical references.