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Alexander S. Belenky.
Zlotnik A.A., Zlotnik I.A.
Computational Mathematics and Mathematical Physics. 2020. Vol. 60. No. 2. P. 240-257.
Myachin A. L.
In bk.: Proceedings of the 20th International Conference on Group Decision and Negotiation. Ryerson University, 2020. P. 22.1-22.10.
Aleskerov F. T., Yakuba V. I.
Математические методы анализа решений в экономике, бизнесе и политике. WP7. Высшая школа экономики, 2020. No. 2323.
Co-author: Matt Backus (Columbia Graduate School of Business)Abstract:
What should one infer when an expert say ``I don't know" --- that the question is difficult or that the expert is unqualified? If the latter, unqualified (and qualified but uninformed) experts will be tempted to hide their uncertainty. We introduce a principal-expert model with heterogeneity in both the competence of experts and the difficulty of the questions they are asked. Our main results examine how different information structures affect the possibility of admitting uncertainty when experts care about appearing competent. When faced with problems that are likely to be difficult, it is better (in terms of eliciting honesty) for those evaluating experts to learn whether the problem is solvable than for them to learn whether the expert was correct. The model matches anecdotal evidence about when admitting uncertainty is feasible and offers new perspectives on the management of experts in various political and economic contexts.