Selecting the best when selection is hard
In dynamic promotion contests, where performance measurement is noisy and constrained to be ordinal, selection of the most able agent can be improved by biasing later stages in favor of early performers. We show that even in the worst-case scenario, where external random factors swamp the difference...
| Main Authors: | , , |
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| Format: | Working paper |
| Language: | English |
| Published: |
University of Oxford
2022
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