On Best-Response Bidding in GSP Auctions
Executive Summary — Keyword auctions have become a critical source of revenue for Google and Yahoo!, among others. This new form of advertising has provided a new way for advertisers to reach customers. But advertisers also face the complex task of optimizing bids to increase their exposure while avoiding unnecessary costs. HBS professor Benjamin Edelman and colleagues analyzed a class of bidding strategies that attempt to increase advertiser utility under limited assumptions about other players' behavior. Under a strategy they call Balanced Bidding (BB), advertisers converge to the advertiser-preferred equilibrium—achieving stability of bids and reducing advertisers' costs relative to other possible outcomes. Key concepts include:
- Sponsored search advertisers should consider others' responses when deciding how much to bid.
- If all players follow the BB strategy, it is possible to determine the expected time to convergence.
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.