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Sellers with Misspecified Models

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  • Kristóf Madarász
  • Andrea Prat

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

  • Kristóf Madarász & Andrea Prat, 2017. "Sellers with Misspecified Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(2), pages 790-815.
  • Handle: RePEc:oup:restud:v:84:y:2017:i:2:p:790-815.
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    File URL: http://hdl.handle.net/10.1093/restud/rdw030
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    Cited by:

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    2. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    3. Jose Luis Montiel Olea & Pietro Ortoleva & Mallesh M Pai & Andrea Prat, 2019. "Competing Models," Papers 1907.03809, arXiv.org, revised Nov 2021.
    4. Bergemann, Dirk & Yeh, Edmund & Zhang, Jinkun, 2021. "Nonlinear pricing with finite information," Games and Economic Behavior, Elsevier, vol. 130(C), pages 62-84.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Pham, Hien & Yamashita, Takuro, 2021. "Auction Design with Heterogeneous Priors," TSE Working Papers 21-1260, Toulouse School of Economics (TSE), revised Mar 2023.
    10. Frank Yang, 2021. "Costly Multidimensional Screening," Papers 2109.00487, arXiv.org, revised Aug 2022.
    11. 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.

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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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