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On Best-Response Bidding in GSP Auctions

2008; RELX Group (Netherlands); Linguagem: Inglês

10.2139/ssrn.1087990

1556-5068

Matthew Cary, Aparna Das, Benjamin Edelman, Ioannis Giotis, Kurtis Heimerl, Anna R. Karlin, Claire Mathieu, Michael Schwarz,

Game Theory and Applications

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. [4]. We prove that convergence occurs with probability 1, and we compute the expected time until convergence.