IDEAS home Printed from https://ideas.repec.org/a/anr/reveco/v11y2019p355-381.html
   My bibliography  Save this article

Approximately Optimal Mechanism Design

Author

Listed:
  • Tim Roughgarden
  • Inbal Talgam-Cohen

Abstract

The field of optimal mechanism design enjoys a beautiful and well-developed theory, as well as several killer applications. Rules of thumb produced by the field influence everything from how governments sell wireless spectrum licenses to how the major search engines auction off online advertising. There are, however, some basic problems for which the traditional optimal mechanism design approach is ill suited—either because it makes overly strong assumptions or because it advocates overly complex designs. This article reviews several common issues with optimal mechanisms, including exorbitant communication, computation, and informational requirements; it also presents several examples demonstrating that relaxing the goal to designing an approximately optimal mechanism allows us to reason about fundamental questions that seem out of reach of the traditional theory.

Suggested Citation

  • Tim Roughgarden & Inbal Talgam-Cohen, 2019. "Approximately Optimal Mechanism Design," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 355-381, August.
    • Handle: RePEc:anr:reveco:v:11:y:2019:p:355-381
    DOI: 10.1146/annurev-economics-080218-025607
    as

    Download full text from publisher

    File URL: https://doi.org/10.1146/annurev-economics-080218-025607
    Download Restriction: Full text downloads are only available to subscribers. Visit the abstract page for more information.
    File URL: https://libkey.io/10.1146/annurev-economics-080218-025607?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yannai A. Gonczarowski & Nicole Immorlica & Yingkai Li & Brendan Lucier, 2021. "Revenue Maximization for Buyers with Costly Participation," Papers 2103.03980, arXiv.org, revised Nov 2023.
    2. Jin Xi & Haitian Xie, 2023. "Strength in numbers: robust mechanisms for public goods with many agents," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 61(3), pages 649-683, October.
    3. Jerry Anunrojwong & Santiago R. Balseiro & Omar Besbes, 2023. "Robust Auction Design with Support Information," Papers 2305.09065, arXiv.org, revised Aug 2023.
    4. Babichenko, Yakov & Talgam-Cohen, Inbal & Xu, Haifeng & Zabarnyi, Konstantin, 2022. "Regret-minimizing Bayesian persuasion," Games and Economic Behavior, Elsevier, vol. 136(C), pages 226-248.
    5. Mira Frick & Ryota Iijima & Yuhta Ishii, 2023. "Monitoring with Rich Data," Papers 2312.16789, arXiv.org.
    6. Jin Xi & Haitian Xie, 2021. "Strength in Numbers: Robust Mechanisms for Public Goods with Many Agents," Papers 2101.02423, arXiv.org, revised May 2023.
    7. Csercsik, Dávid, 2022. "Convex combinatorial auction of pipeline network capacities," Energy Economics, Elsevier, vol. 111(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:anr:reveco:v:11:y:2019:p:355-381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: http://www.annualreviews.org (email available below). General contact details of provider: http://www.annualreviews.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.