19 Pages Posted: 28 Jan 2008 Last revised: 25 Oct 2012
Date Written: January 28, 2008
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 et al. . We prove that convergence occurs with probability 1, and we compute the expected time until convergence.
Keywords: Keyword auctions, bidding strategies, convergence
JEL Classification: C72, D44
Suggested Citation: Suggested Citation
Cary, Matthew and Das, Aparna and Edelman, Benjamin G. and Giotis, Ioannis and Heimerl, Kurtis and Karlin, Anna R. and Kenyon Mathieu, Claire and Schwarz, Michael, On Best-Response Bidding in GSP Auctions (January 28, 2008). Harvard Business School NOM Working Paper No. 08-056. Available at SSRN: https://ssrn.com/abstract=1087990 or http://dx.doi.org/10.2139/ssrn.1087990