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Sellers with misspecified models

Author

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  • Madarász, Kristóf
  • Prat, Andrea
Abstract
Principals often operate on misspecified models of their agents’ preferences. When preferences are such that non-local incentive constraints may bind in the optimum, even slight misspecification of the preferences can lead to large and non-vanishing losses. Instead, we propose a two-step scheme whereby the principal: (1) identifies the model-optimal menu; and (2) modifies prices by offering to share with the agent a fixed proportion of the profit she would receive if an item were sold at the model-optimal price. We show that her loss is bounded and vanishes smoothly as the model converges to the truth. Finally, two-step mechanisms without a sharing rule like (2) will not yield a valid approximation.

Suggested Citation

  • Madarász, Kristóf & Prat, Andrea, 2017. "Sellers with misspecified models," LSE Research Online Documents on Economics 87271, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:87271
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    File URL: http://eprints.lse.ac.uk/87271/
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    References listed on IDEAS

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    Cited by:

    1. Aislinn Bohren & Daniel Hauser, 2017. "Bounded Rationality And Learning: A Framwork and A Robustness Result," PIER Working Paper Archive 17-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 May 2017.
    2. Frank Yang, 2021. "Costly Multidimensional Screening," Papers 2109.00487, arXiv.org, revised Aug 2022.
    3. Duarte Gonc{c}alves & Bruno A. Furtado, 2024. "Statistical Mechanism Design: Robust Pricing, Estimation, and Inference," Papers 2405.17178, arXiv.org.
    4. Sameer Mehta & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2022. "An Approximation Scheme for Data Monetization," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2412-2428, June.
    5. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    6. Jose Luis Montiel Olea & Pietro Ortoleva & Mallesh M Pai & Andrea Prat, 2019. "Competing Models," Papers 1907.03809, arXiv.org, revised Nov 2021.
    7. Rosenthal, Maxwell, 2023. "Robust incentives for risk," Journal of Mathematical Economics, Elsevier, vol. 109(C).
    8. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Misinterpreting Others and the Fragility of Social Learning," Econometrica, Econometric Society, vol. 88(6), pages 2281-2328, November.
    9. Manea, Mihai & Maskin, Eric, 2023. "Withholding and damage in Bayesian trade mechanisms," Games and Economic Behavior, Elsevier, vol. 142(C), pages 243-265.
    10. Pham, Hien & Yamashita, Takuro, 2024. "Auction design with heterogeneous priors," Games and Economic Behavior, Elsevier, vol. 145(C), pages 413-425.
    11. Bergemann, Dirk & Yeh, Edmund & Zhang, Jinkun, 2021. "Nonlinear pricing with finite information," Games and Economic Behavior, Elsevier, vol. 130(C), pages 62-84.
    12. Pathikrit Basu, 2023. "Mechanism design with model specification," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 61(2), pages 263-276, August.
    13. Devanur, Nikhil R. & Haghpanah, Nima & Psomas, Alexandros, 2020. "Optimal multi-unit mechanisms with private demands," Games and Economic Behavior, Elsevier, vol. 121(C), pages 482-505.
    14. Aislinn Bohren & Daniel Hauser, 2018. "Social Learning with Model Misspeciification: A Framework and a Robustness Result," PIER Working Paper Archive 18-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jul 2018.

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    More about this item

    Keywords

    model uncertainty; robustness; complexity; mechanism design;
    All these keywords.

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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