A True Expert Knows which Question Should Be Asked
AbstractWe suggest a test for discovering whether a potential expert is informed of the distribution of a stochastic process. In a non-Bayesian non-parametric setting, the expert is asked to make a prediction which is tested against a single realization of the stochastic process. It is shown that by asking the expert to predict a "small" set of sequences, the test will assure that any informed expert can pass the test with probability one with respect to the actual distribution. Moreover, for the uninformed non-expert it is impossible to pass this test, in the sense that for any choice of a "small" set of sequences, only a "small" set of measures will assign a positive probability to the given set. Hence for "most" measures, the non-expert will surely fail the test. We define small as category 1 sets, described in more detail in the paper.
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Bibliographic InfoPaper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1856.
Date of creation: Jun 2004
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Other versions of this item:
- Eddie Dekel & Yossi Feinberg, 2006. "A True Expert Knows which Question Should be Asked," Discussion Papers 1385, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
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