|
|
|
|
||||
|
||||
On Best-Response Bidding in GSP AuctionsMatthew CaryUniversity of Washington Aparna DasBrown University Benjamin G. EdelmanHarvard University - HBS Negotiations, Organizations and Markets Unit Ioannis GiotisUniversity of Washington - College of Arts and Sciences Kurtis HeimerlUniversity of Washington Anna R. KarlinUniversity of Washington Claire Kenyon MathieuBrown University Michael SchwarzYahoo! Research Labs; National Bureau of Economic Research (NBER) January 28, 2008 Harvard Business School NOM Working Paper No. 08-056 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 et al. [4]. We prove that convergence occurs with probability 1, and we compute the expected time until convergence.
Number of Pages in PDF File: 19 Keywords: Keyword auctions, bidding strategies, convergence JEL Classification: C72, D44 working papers seriesDate posted: January 28, 2008 ; Last revised: October 25, 2012Suggested CitationContact Information
|
|
|||||||||||||||||||||||||||||||||||||||||||
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
FAQ
Terms of Use
Privacy Policy
Copyright
This page was processed by apollo8 in 0.328 seconds
