| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17484 |
| DP17484 Selecting the Best when Selection is Hard | |
| Mikhail Drugov; Margaret Meyer; Marc Möller | |
| 发表日期 | 2022-07-20 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | 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 in agents' abilities in determining their relative performance, optimal bias is (i) strictly positive and (ii) locally insensitive to changes in the ratio of heterogeneity to noise. To explain these, arguably surprising, limiting results, we demonstrate a close relationship in the limit between optimal bias under ordinal information and the expected optimal bias when bias can be conditioned on cardinal information about relative performance. As a consequence of these two limiting properties, 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. |
| 主题 | Organizational Economics |
| 关键词 | Dynamic contests Selective efficiency Bias Learning Promotions |
| URL | https://cepr.org/publications/dp17484 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/546579 |
| 推荐引用方式 GB/T 7714 | Mikhail Drugov,Margaret Meyer,Marc Möller. DP17484 Selecting the Best when Selection is Hard. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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