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Testing theories with learnable and predictive representations |
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| Authors: | Nabil I. Al-Najjar Alvaro Sandroni Rann Smorodinsky |
| Affiliation: | a Department of Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, Evanston, IL 60208, United States b Department of Economics, University of Pennsylvania, United States c Davidson Faculty of Industrial Engineering and Management, Technion, Haifa 32000, Israel |
| Abstract: | We study the problem of testing an expert whose theory has a learnable and predictive parametric representation, as do standard processes used in statistics. We design a test in which the expert is required to submit a date T by which he will have learned enough to deliver a sharp, testable prediction about future frequencies. We show that this test passes an expert who knows the data-generating process and cannot be manipulated by a uninformed one. Such a test is not possible if the theory is unrestricted. |
| Keywords: | C70 D83 |
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