| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15738 |
| DP15738 Algorithmic collusion with imperfect monitoring | |
| Giacomo Calzolari; Emilio Calvano; Vincenzo Denicolò; Sergio Pastorello | |
| 发表日期 | 2021-01-29 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We show that if they are allowed enough time to complete the learning, Q-learning algorithms can learn to collude in an environment with imperfect monitoring adapted from Green and Porter (1984), without having been instructed to do so, and without communicating with one another. Collusion is sustained by punishments that take the form of "price wars" triggered by the observation of low prices. The punishments have a finite duration, being harsher initially and then gradually fading away. Such punishments are triggered both by deviations and by adverse demand shocks. |
| 主题 | Industrial Organization |
| 关键词 | Artificial intelligence Q-learning Imperfect monitoring Collusion |
| URL | https://cepr.org/publications/dp15738 |
| 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/544741 |
| 推荐引用方式 GB/T 7714 | Giacomo Calzolari,Emilio Calvano,Vincenzo Denicolò,et al. DP15738 Algorithmic collusion with imperfect monitoring. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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