| 来源类型 | Discussion paper
|
| 规范类型 | 论文
|
| 来源ID | DP12036
|
| DP12036 Learning with Heterogeneous Misspecified Models: Characterization and Robustness |
| Aislinn Bohren; Daniel Hauser
|
| 发表日期 | 2017-05-09
|
| 出版年 | 2017
|
| 语种 | 英语
|
| 摘要 | This paper develops a general framework to study how misinterpreting information impacts learning. Our main result is a simple criterion to characterize long-run beliefs based on the underlying form of misspecification. We present this characterization in the context of social learning, then highlight how it applies to other learning environments, including individual learning. A key contribution is that our characterization applies to settings with model heterogeneity and provides conditions for entrenched disagreement. Our characterization can be used to determine whether a representative agent approach is valid in the face of heterogeneity, study how differing levels of bias or unawareness of others' biases impact learning, and explore whether the impact of a bias is sensitive to parametric specification or the source of information. This unified framework synthesizes insights gleaned from previously studied forms of misspecification and provides novel insights in specific applications, as we demonstrate in settings with partisan bias, overreaction, naive learning, and level-k reasoning. |
| 主题 | Industrial Organization
|
| 关键词 | Model misspecification
Social learning
|
| URL | https://cepr.org/publications/dp12036-3
|
| 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/540848
|
推荐引用方式
GB/T 7714 |
Aislinn Bohren,Daniel Hauser. DP12036 Learning with Heterogeneous Misspecified Models: Characterization and Robustness. 2017.
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