| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15504 |
| DP15504 Platform Design When Sellers Use Pricing Algorithms | |
| Justin Johnson; Andrew Rhodes; Matthijs Wildenbeest | |
| 发表日期 | 2020-11-30 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | Using both economic theory and Artificial Intelligence (AI) pricing algorithms, we investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and even raise its own profits. We allow sellers to use Q-learning algorithms (a common reinforcement-learning technique from the computer-science literature) to devise pricing strategies in a setting with repeated interactions, and consider the effect of platform rules that reward firms that cut prices with additional exposure to consumers. Overall, the evidence from our experiments suggests that platform design decisions can meaningfully benefit consumers even when algorithmic collusion might otherwise emerge but that achieving these gains may require more than the simplest steering policies when algorithms value the future highly. We also find that policies that raise consumer surplus can raise the profits of the platform, depending on the platform's revenue model. Finally, we document several learning challenges faced by the algorithms. |
| 主题 | Industrial Organization |
| 关键词 | Algorithms Artificial intelligence Collusion Platform design |
| URL | https://cepr.org/publications/dp15504 |
| 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/544505 |
| 推荐引用方式 GB/T 7714 | Justin Johnson,Andrew Rhodes,Matthijs Wildenbeest. DP15504 Platform Design When Sellers Use Pricing Algorithms. 2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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