Selecting the Best when Selection is Hard

Mikhail Drugov, Margaret Meyer and Marc M Ller
Authors registered in the RePEc Author Service: Marc Möller ()

Diskussionsschriften from Universitaet Bern, Departement Volkswirtschaft

Abstract: In dynamic promotion contests, where performance measurement is noisy and ordinal, selection can be improved by biasing later stages in favor of early leaders. Even in the worst-case scenario, where noise swamps ability differences in determining relative performance, optimal bias is i) strictly positive; ii) locally insensitive to changes in the heterogeneity-to-noise ratio. A close relationship with expected opti- mal bias under cardinal information helps explain this surprising result. Properties i) and ii) imply that the simple rule of setting bias as if in the worst-case scenario achieves most of the potential gains in selective efficiency from biasing dynamic rank-order contests.

Keywords: Dynamic Contests; Selective Efficiency; Bias; Learning; Promotions. (search for similar items in EconPapers)
JEL-codes: D21 D82 D83 M51 (search for similar items in EconPapers)
Date: 2022-08
New Economics Papers: this item is included in nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Selecting the Best when Selection is Hard (2022)
Working Paper: Selecting the Best when Selection is Hard (2022)
Working Paper: Selecting the best when selection is hard (2022)
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