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Approximately Optimal Mechanism Design

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  • 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
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    Citations

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    Cited by:

    1. Jerry Anunrojwong & Santiago R. Balseiro & Omar Besbes, 2024. "The Best of Many Robustness Criteria in Decision Making: Formulation and Application to Robust Pricing," Papers 2403.12260, arXiv.org.
    2. 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.
    3. 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.
    4. Jerry Anunrojwong & Santiago R. Balseiro & Omar Besbes, 2023. "Robust Auction Design with Support Information," Papers 2305.09065, arXiv.org, revised Aug 2023.
    5. 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.
    6. Mira Frick & Ryota Iijima & Yuhta Ishii, 2023. "Monitoring with Rich Data," Papers 2312.16789, arXiv.org, revised Jul 2024.
    7. Jin Xi & Haitian Xie, 2021. "Strength in Numbers: Robust Mechanisms for Public Goods with Many Agents," Papers 2101.02423, arXiv.org, revised May 2023.
    8. Csercsik, Dávid, 2022. "Convex combinatorial auction of pipeline network capacities," Energy Economics, Elsevier, vol. 111(C).

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