Cary, Matthew
[Author]
;
Das, Aparna
[Other];
Karlin, Anna R.
[Other];
Edelman, Benjamin
[Other];
Mathieu, Claire
[Other];
Giotis, Ioannis
[Other];
Heimerl, Kurtis
[Other];
Schwarz, Michael
[Other]National Bureau of Economic Research
Published:
Cambridge, Mass: National Bureau of Economic Research, February 2008
Published in:NBER working paper series ; no. w13788
Extent:
1 Online-Ressource
Language:
English
DOI:
10.3386/w13788
Identifier:
Reproduction note:
Hardcopy version available to institutional subscribers
Origination:
Footnote:
Mode of access: World Wide Web
System requirements: Adobe [Acrobat] Reader required for PDF files
Description:
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