Bidding to the Top: VCG and Equilibria of Position-Based Auctions

  • Gagan Aggarwal
  • Jon Feldman
  • S. Muthukrishnan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4368)


Many popular search engines run an auction to determine the placement of advertisements next to search results. Current auctions at Google and Yahoo! let advertisers specify a single amount as their bid in the auction. This bid is interpreted as the maximum amount the advertiser is willing to pay per click on its ad. When search queries arrive, the bids are used to rank the ads linearly on the search result page. Advertisers seek to be high on the list, as this attracts more attention and more clicks. The advertisers pay for each user who clicks on their ad, and the amount charged depends on the bids of all the advertisers participating in the auction.

We study the problem of ranking ads and associated pricing mechanisms when the advertisers not only specify a bid, but additionally express their preference for positions in the list of ads. In particular, we study prefix position auctions where advertiser i can specify that she is interested only in the top κ i positions.

We present a simple allocation and pricing mechanism that generalizes the desirable properties of current auctions that do not have position constraints. In addition, we show that our auction has an envy-free [1] or symmetric [2] Nash equilibrium with the same outcome in allocation and pricing as the well-known truthful Vickrey-Clarke-Groves (VCG) auction. Furthermore, we show that this equilibrium is the best such equilibrium for the advertisers in terms of the profit made by each advertiser. We also discuss other position-based auctions.


Nash Equilibrium Search Query Auction Mechanism Price Method True Valuation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. In: Second Workshop on Sponsored Search Auctions (2006)Google Scholar
  2. 2.
    Varian, H.: Position auctions (2006) Working Paper, available at
  3. 3.
    Aggarwal, G.: Privacy Protection and Advertising in a Networked World. PhD thesis, Stanford University (2005)Google Scholar
  4. 4.
    Aggarwal, G., Goel, A., Motwani, R.: Truthful auctions for pricing search keywords. In: ACM Conference on Electronic Commerce, EC 2006 (2006)Google Scholar
  5. 5.
    Vickrey, W.: Counterspeculation, auctions and competitive sealed tenders. Journal of Finance 16, 8–37 (1961)CrossRefGoogle Scholar
  6. 6.
    Clarke, E.: Multipart pricing of public goods. Public Choice 11, 17–33 (1971)CrossRefGoogle Scholar
  7. 7.
    Groves, T.: Incentives in teams. Econometrica 41, 617–631 (1973)MATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Nielsen//NetRatings: Interactive advertising bureau (IAB) search branding study (2004), Commissioned by the IAB Search Engine Committee, Available at
  9. 9.
    Mas-Collel, A., Whinston, M., Green, J.: Microeconomic Theory. Oxford University Press, Oxford (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gagan Aggarwal
    • 1
    • 2
  • Jon Feldman
    • 1
    • 2
  • S. Muthukrishnan
    • 1
    • 2
  1. 1.Google, Inc.New York
  2. 2. Mountain View

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