| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16833 |
| DP16833 Welfare Comparisons for Biased Learning | |
| Mira Frick; Yuhta Ishii | |
| 发表日期 | 2021-12-24 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We study robust welfare comparisons of learning biases, i.e., deviations from correct Bayesian updating. Given a true signal distribution, we deem one bias more harmful than another if it yields lower objective expected payoffs in all decision problems. We characterize this ranking in static (one signal) and dynamic (many signals) settings. While the static characterization compares posteriors signal-by-signal, the dynamic characterization employs an “efficiency index” quantifying the speed of belief convergence. Our results yield welfare-founded quantifications of the severity of well-documented biases. Moreover, the static and dynamic rankings can disagree, and “smaller” biases can be worse in dynamic settings. |
| 主题 | Organizational Economics |
| URL | https://cepr.org/publications/dp16833 |
| 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/545758 |
| 推荐引用方式 GB/T 7714 | Mira Frick,Yuhta Ishii. DP16833 Welfare Comparisons for Biased Learning. 2021. |
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