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Screening multiple potentially false experts

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  • Francisco Barreras
  • Álvaro José Riascos Villegas

Abstract

A decision maker is presented with a theory from a self proclaimed expert about the probability of occurrence of certain events. The decision maker faces the possibility that the expert is completely ignorant about the data generating process and so she’s interested in mechanisms that allow her to screen informed experts from uninformed ones. The decision maker needs to control for type I error, however, since she’s also uncertain about the true stochastic process, this gives room for uninformed experts to make strategic forecasts and ignorantly pass tests and profit from contracts. We present an original multiple expert model where a contract achieves screening of informed and uninformed experts by means of pitting experts’ predictions against each other. Additionally, we present a theoretical review of the main findings in two branches of literature that attempt to solve the expert screening problem. Namely models about testing experts and models in contract theory that pursue screening of experts.

Suggested Citation

  • Francisco Barreras & Álvaro José Riascos Villegas, 2016. "Screening multiple potentially false experts," Monografías 18207, Quantil.
    • Handle: RePEc:col:000509:018207
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    References listed on IDEAS

    as
    1. Nabil I. Al-Najjar & Jonathan Weinstein, 2008. "Comparative Testing of Experts," Econometrica, Econometric Society, vol. 76(3), pages 541-559, May.
    2. Carvajal, Andrés, 2009. "Statistical calibration: A simplification of Foster's proof," Mathematical Social Sciences, Elsevier, vol. 58(2), pages 272-277, September.
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    5. Wojciech Olszewski & Alvaro Sandroni, 2006. "Strategic Manipulation of Empirical Tests," Discussion Papers 1425, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    6. 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.
    7. 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.
    8. Olszewski, Wojciech, 2015. "Calibration and Expert Testing," Handbook of Game Theory with Economic Applications,, Elsevier.
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    11. Yossi Feinberg & Colin Stewart, 2008. "Testing Multiple Forecasters," Econometrica, Econometric Society, vol. 76(3), pages 561-582, May.
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