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The Model Selection Curse

Author

Listed:
  • Kfir Eliaz
  • Ran Spiegler

Abstract

A "statistician" takes an action on behalf of an agent, based on the agent's self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent's report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician's procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with "machine learning" algorithms.

Suggested Citation

  • Kfir Eliaz & Ran Spiegler, 2018. "The Model Selection Curse," Papers 1810.02888, arXiv.org.
    • Handle: RePEc:arx:papers:1810.02888
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    References listed on IDEAS

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    1. Abhijit Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2017. "A Theory of Experimenters," NBER Working Papers 23867, National Bureau of Economic Research, Inc.
    2. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    3. Osborne, Martin J & Rubinstein, Ariel, 1998. "Games with Procedurally Rational Players," American Economic Review, American Economic Association, vol. 88(4), pages 834-847, September.
    4. Ran Spiegler, 2006. "The Market for Quacks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1113-1131.
    5. Sylvain Chassang & Gerard Padro I Miquel & Erik Snowberg, 2012. "Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments," American Economic Review, American Economic Association, vol. 102(4), pages 1279-1309, June.
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    Cited by:

    1. Eric Danan & Thibault Gajdos & Jean-Marc Tallon, 2023. "Tailored recommendations," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 60(1), pages 15-34, January.
    2. Ganesh Iyer & T. Tony Ke, 2022. "Competitive Algorithmic Targeting and Model Selection," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    3. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
    4. Mehmet Caner & Kfir Eliaz, 2021. "Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso," Papers 2101.01144, arXiv.org, revised Sep 2021.
    5. Andreas Haupt & Dylan Hadfield-Menell & Chara Podimata, 2023. "Recommending to Strategic Users," Papers 2302.06559, arXiv.org.
    6. Gong, Aibo & Ke, Shaowei & Qiu, Yawen & Shen, Rui, 2022. "Robust pricing under strategic trading," Journal of Economic Theory, Elsevier, vol. 199(C).

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    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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