| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29897 |
| 来源ID | Working Paper 29897 |
| Consensus and Disagreement: Information Aggregation under (not so) Naive Learning | |
| Abhijit Banerjee; Olivier Compte | |
| 发表日期 | 2022-04-04 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | We explore a model of non-Bayesian information aggregation in networks. Agents non-cooperatively choose among Friedkin-Johnsen type aggregation rules to maximize payoffs. The DeGroot rule is chosen in equilibrium if and only if there is noiseless information transmission...leading to consensus. With noisy transmission, while some disagreement is inevitable, the optimal choice of rule blows up disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, which inflates the disagreement. We use this framework to think about equilibrium versus socially efficient choice of rules and its connection to polarization of opinions across groups. |
| 主题 | Microeconomics |
| URL | https://www.nber.org/papers/w29897 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587570 |
| 推荐引用方式 GB/T 7714 | Abhijit Banerjee,Olivier Compte. Consensus and Disagreement: Information Aggregation under (not so) Naive Learning. 2022. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w29897.pdf（604KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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