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Revenue Maximization for Market Intermediation with Correlated Priors

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Algorithmic Game Theory (SAGT 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9928))

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Abstract

We study the computational challenge faced by a intermediary who attempts to profit from trade with a small number of buyers and sellers of some item. In the version of the problem that we study, the number of buyers and sellers is constant, but their joint distribution of the item’s value may be complicated. We consider discretized distributions, where the complexity parameter is the support size, or number of different prices that may occur. We show that maximizing the expected revenue is computationally tractable (via an LP) if we are allowed to use randomized mechanisms. For the deterministic case, we show how an optimal mechanism can be efficiently computed for the one-seller/one-buyer case, but give a contrasting NP-completeness result for the one-seller/two-buyer case.

M. Gerstgrasser—Recipient of a DOC Fellowship of the Austrian Academy of Sciences.

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Notes

  1. 1.

    Careful analysis of the algorithm presented shows that the last summand in the recursion for R() has \((m_\ell - \beta (\ell )) \cdot \ell \) summands. It is easy to see however that we need not recompute the inner sum from scratch in each iteration. We can thus easily make the computation of the recursion run in linear time, giving the overall running time stated.

References

  1. Colini-Baldeschi, R., de Keijzer, B., Leonardi, S., Turchetta, S.: Approximately efficient double auctions with strong budget balance. In: Proceedings of SODA. ACM-SIAM (2016)

    Google Scholar 

  2. Deng, X., Goldberg, P., Tang, B., Zhang, J.: Revenue maximization in a bayesian double auction market. Theoret. Comput. Sci. 539, 1–12 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  3. Dobzinski, S., Fu, H., Kleinberg, R.D.: Optimal auctions with correlated bidders are easy. In: Proceedings of the Forty-third Annual ACM Symposium on Theory of Computing, pp. 129–138. ACM (2011)

    Google Scholar 

  4. Dütting, P., Roughgarden, T., Talgam-Cohen, I.: Modularity and greed in double auctions. In: Proceedings of the Fifteenth ACM Conference on Economics and Computation, pp. 241–258. ACM (2014)

    Google Scholar 

  5. Feldman, M., Gravin, N., Lucier, B.: Combinatorial auctions via posted prices. In: Proceedings of SODA, pp. 123–135. ACM-SIAM (2015)

    Google Scholar 

  6. Loertscher, S., Niedermayer, A.: When is seller price setting with linear fees optimal for intermediaries? Technical report, Discussion Papers, Department of Economics, Universität Bern (2007)

    Google Scholar 

  7. Loertscher, S., Niedermayer, A.: Fee setting intermediaries: on real estate agents, stock brokers, and auction houses. Technical report, Discussion paper Center for Mathematical Studies in Economics and Management Science (2008)

    Google Scholar 

  8. Loertscher, S., Niedermayer, A.: Fee-setting mechanisms: on optimal pricing by intermediaries and indirect taxation. Technical report, SFB/TR 15 Discussion Paper (2012)

    Google Scholar 

  9. McAfee, R.P.: A dominant strategy double auction. J. Econ. Theory 56(2), 434–450 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  10. Myerson, R.B.: Optimal auction design. Math. Oper. Res. 6(1), 58–73 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  11. Myerson, R.B., Satterthwaite, M.A.: Efficient mechanisms for bilateral trading. J. Econ. Theory 29(2), 265–281 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  12. Niazadeh, R., Yuan, Y., Kleinberg, R.D.: Simple and near-optimal mechanisms for market intermediation. arXiv preprint arXiv:1409.2597 (2014)

  13. Papadimitriou, C.H., Pierrakos, G.: On optimal single-item auctions. In: Proceedings of the Forty-third Annual ACM Symposium on Theory of Computing, pp. 119–128. ACM (2011)

    Google Scholar 

  14. Ronen, A.: On approximating optimal auctions. In: Proceedings of the 3rd ACM Conference on Electronic Commerce, pp. 11–17. ACM (2001)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK

    Matthias Gerstgrasser, Paul W. Goldberg & Elias Koutsoupias

Authors
  1. Matthias Gerstgrasser
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  2. Paul W. Goldberg
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  3. Elias Koutsoupias
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Corresponding authors

Correspondence to Matthias Gerstgrasser , Paul W. Goldberg or Elias Koutsoupias .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, United Kingdom

    Martin Gairing

  2. University of Liverpool, Liverpool, United Kingdom

    Rahul Savani

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Gerstgrasser, M., Goldberg, P.W., Koutsoupias, E. (2016). Revenue Maximization for Market Intermediation with Correlated Priors. In: Gairing, M., Savani, R. (eds) Algorithmic Game Theory. SAGT 2016. Lecture Notes in Computer Science(), vol 9928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53354-3_22

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  • DOI: https://doi.org/10.1007/978-3-662-53354-3_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-53353-6

  • Online ISBN: 978-3-662-53354-3

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