Working Paper: NBER ID: w13788
Authors: Matthew Cary; Aparna Das; Benjamin Edelman; Ioannis Giotis; Kurtis Heimerl; Anna R. Karlin; Claire Mathieu; Michael Schwarz
Abstract: 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 (2007). We prove that convergence occurs with probability 1, and we compute the expected time until convergence.
Keywords: keyword auctions; bidding strategies; convergence
JEL Codes: C15; D44; L86; M37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| balanced bidding (BB) strategy (D44) | bids converge to a Nash equilibrium (C72) |
| bids converge to a Nash equilibrium (C72) | expected payments of players equal to those under the VCG mechanism (C71) |
| balanced bidding (BB) strategy (D44) | unique fixed point in the bidding process (D44) |
| bids converge to a Nash equilibrium (C72) | expected time until convergence can be computed (C62) |