Skip to main content

Prediction and Welfare in Ad Auctions

  • Conference paper
Algorithmic Game Theory (SAGT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8768))

Included in the following conference series:

Abstract

We study how standard auction objectives in sponsored search markets are affected by refinement in the prediction of ad relevance (click-through rates). As the prediction algorithm takes more features into account, its predictions become more refined; a natural question is whether this is desirable from the perspective of auction objectives. Our focus is on mechanisms that optimize for a convex combination of efficiency and revenue, and our starting point is the observation that the objective of such a mechanism can only improve with refined prediction, making refinement in the best interest of the search engine. We demonstrate that the impact of refinement on market efficiency is not always positive; nevertheless we are able to identify natural – and to some extent necessary – conditions under which refinement is guaranteed to also improve efficiency. Our main technical contribution is in explaining how refinement changes the ranking of advertisers by value (efficiency-ranking), moving it either towards or away from their ranking by virtual value (revenue-ranking). These results are closely related to the literature on signaling in auctions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aggarwal, G., Goel, A., Motwani, R.: Truthful auctions for pricing search keywords. In: Proc. 8th EC (2006)

    Google Scholar 

  2. Clarke, E.H.: Multipart pricing of public goods. Public Choice 11, 17–33 (1971)

    Article  Google Scholar 

  3. Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review 97(1), 242–259 (2007)

    Article  Google Scholar 

  4. Edelman, B., Schwarz, M.: Optimal auction design and equilibrium selection in sponsored search auctions. American Economic Review 100(2), 597–602 (2010)

    Article  Google Scholar 

  5. Emek, Y., Feldman, M., Gamzu, I., Leme, R.P., Tennenholtz, M.: Signaling schemes for revenue maximization. In: EC, pp. 514–531 (2012)

    Google Scholar 

  6. Ewerhart, C.: Optimal design and ρ-concavity, working Paper (2009)

    Google Scholar 

  7. Fu, H., Jordan, P., Mahdian, M., Nadav, U., Talgam-Cohen, I., Vassilvitskii, S.: Ad auctions with data. In: Serna, M. (ed.) SAGT 2012. LNCS, vol. 7615, pp. 168–179. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  8. Ghosh, A., Nazerzadeh, H., Sundararajan, M.: Computing optimal bundles for sponsored search. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 576–583. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in Microsoft’s Bing search engine. In: 27th International Conference on Machine Learning (2010)

    Google Scholar 

  10. Groves, T.: Incentives in teams. Econometrica 41, 617–631 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  11. Lahaie, S., Pennock, D.M.: Revenue analysis of a family of ranking rules for keyword auctions. In: EC, pp. 50–56 (2007)

    Google Scholar 

  12. Lahaie, S., Pennock, D.M., Saberi, A., Vohra, R.V.: Sponsored search auctions. In: Nisan, N., Roughgarden, T., Tardos, É., Vazirani, V. (eds.) Algorithmic Game Theory, ch. 28, pp. 699–716. Cambridge University Press (2007)

    Google Scholar 

  13. Likhodedov, A., Sandholm, T.: Auction mechanism for optimally trading off revenue and efficiency. In: EC, pp. 212–213 (2003)

    Google Scholar 

  14. McAfee, R., McMillan, J.: Auctions and bidding. Journal of Economic Literature 25(2), 699–738 (1987)

    Google Scholar 

  15. Mehta, A., Saberi, A., Vazirani, U., Vazirani, V.: Adwords and generalized on-line matching. In: Proc. 46th IEEE Symp. on Foundations of Computer Science, FOCS (2002)

    Google Scholar 

  16. Milgrom, P., Weber, R.J.: A theory of auctions and competitive bidding. Econometrica 50, 1089–1122 (1982)

    Article  MATH  Google Scholar 

  17. Miltersen, P.B., Sheffet, O.: Send mixed signals: earn more, work less. In: EC, pp. 234–247 (2012)

    Google Scholar 

  18. Myerson, R.: Optimal auction design. Mathematics of Operations Research 6(1), 58–73 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  19. Myerson, R., Satterthwaite, M.: Efficient mechanisms for bilaterial trade. Journal of Economic Theory 29(1), 265–281 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  20. Sundararajan, M., Talgam-Cohen, I.: Refine predictions ad infinitum? (2013), http://arxiv.org/abs/1302.6700

  21. Varian, H.R.: Position auctions. International Journal of Industrial Organization 25(6), 1163–1178 (2007)

    Article  Google Scholar 

  22. Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. J. of Finance 16, 8–37 (1961)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sundararajan, M., Talgam-Cohen, I. (2014). Prediction and Welfare in Ad Auctions. In: Lavi, R. (eds) Algorithmic Game Theory. SAGT 2014. Lecture Notes in Computer Science, vol 8768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44803-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44803-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44802-1

  • Online ISBN: 978-3-662-44803-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics