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The Principal-Agent Approach to Testing Experts

Author

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  • Wojciech Olszewski
  • Marcin Pęski

Abstract

Recent literature on testing experts shows that it is often impossible to determine whether an expert knows the stochastic process that generates data. Despite this negative result, we show that there often exist contracts that allow a decision maker to attain the first-best payoff without learning the expert's type. This kind of full-surplus extraction is always possible in infinite-horizon models in which future payoffs are not discounted. If future payoffs are discounted (but the discount factor tends to 1), the possibility of full-surplus extraction depends on a constraint involving the forecasting technology. (JEL D82)

Suggested Citation

  • Wojciech Olszewski & Marcin Pęski, 2011. "The Principal-Agent Approach to Testing Experts," American Economic Journal: Microeconomics, American Economic Association, vol. 3(2), pages 89-113, May.
    • Handle: RePEc:aea:aejmic:v:3:y:2011:i:2:p:89-113
    Note: DOI: 10.1257/mic.3.2.89
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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/mic.3.2.89
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    References listed on IDEAS

    as
    1. Fudenberg, Drew & Levine, David K., 1999. "An Easier Way to Calibrate," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 131-137, October.
    2. Vladimir Vovk & Glenn Shafer, 2005. "Good randomized sequential probability forecasting is always possible," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 747-763, November.
    3. Alvaro Sandroni, 2003. "The reproducible properties of correct forecasts," International Journal of Game Theory, Springer;Game Theory Society, vol. 32(1), pages 151-159, December.
    4. Eddie Dekel & Yossi Feinberg, 2006. "Non-Bayesian Testing of a Stochastic Prediction," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 893-906.
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    Citations

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    Cited by:

    1. Schlag, Karl H. & Zapechelnyuk, Andriy, 2017. "Dynamic benchmark targeting," Journal of Economic Theory, Elsevier, vol. 169(C), pages 145-169.
    2. Irene Valsecchi, 2013. "The expert problem: a survey," Economics of Governance, Springer, vol. 14(4), pages 303-331, November.
    3. Olszewski, Wojciech, 2015. "Calibration and Expert Testing," Handbook of Game Theory with Economic Applications,, Elsevier.
    4. Rahul Deb & Mallesh M. Pai & Maher Said, 2018. "Evaluating Strategic Forecasters," American Economic Review, American Economic Association, vol. 108(10), pages 3057-3103, October.
    5. Francisco Barreras & Álvaro J. Riascos, 2016. "Screening multiple potentially false experts," Monografías 15075, Quantil.
    6. Atulya Jain & Vianney Perchet, 2024. "Calibrated Forecasting and Persuasion," Papers 2406.15680, arXiv.org.
    7. Colin, Stewart, 2011. "Nonmanipulable Bayesian testing," Journal of Economic Theory, Elsevier, vol. 146(5), pages 2029-2041, September.
    8. Alvaro Sandroni, 2014. "At Least Do No Harm: The Use of Scarce Data," American Economic Journal: Microeconomics, American Economic Association, vol. 6(1), pages 1-3, February.
    9. Abhijit Banerjee & Sylvain Chassang & Erik Snowberg, 2016. "Decision Theoretic Approaches to Experiment Design and External Validity," NBER Working Papers 22167, National Bureau of Economic Research, Inc.

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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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