| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28535 |
| 来源ID | Working Paper 28535 |
| Artificial Intelligence and Pricing: The Impact of Algorithm Design | |
| John Asker; Chaim Fershtman; Ariel Pakes | |
| 发表日期 | 2021-03-08 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | The behavior of artificial intelligences algorithms (AIAs) is shaped by how they learn about their environment. We compare the prices generated by AIAs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AIA only learns about the return from the action it took. Synchronous learning occurs when the AIA conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AIA, synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AIA places a weight on future profits. |
| 主题 | Microeconomics ; Game Theory ; Market Structure and Distribution ; Economics of Information ; Other ; Law and Economics ; Industrial Organization ; Market Structure and Firm Performance ; Antitrust ; Regulatory Economics ; Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w28535 |
| 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/586208 |
| 推荐引用方式 GB/T 7714 | John Asker,Chaim Fershtman,Ariel Pakes. Artificial Intelligence and Pricing: The Impact of Algorithm Design. 2021. |
| 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 | ||
| w28535.pdf（1086KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
