| HBS Working Paper Series
On Best-Response Bidding in GSP Auctions
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.
Keywords: Online Advertising;
Bids and Bidding;
Cary, Matthew, Aparna Das, Benjamin G. Edelman, Ioannis Giotis, Kurtis Heimerl, Anna R. Karlin, Claire Mathieu, and Michael Schwarz. "On Best-Response Bidding in GSP Auctions." Harvard Business School Working Paper, No. 08–056, January 2008.