| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24284 |
| 来源ID | Working Paper 24284 |
| Human Judgment and AI Pricing | |
| Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb | |
| 发表日期 | 2018-02-05 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | Recent artificial intelligence advances can be seen as improvements in prediction. We examine how such predictions should be priced. We model two inputs into decisions: a prediction of the state and the payoff or utility from different actions in that state. The payoff is unknown, and can only be learned through experiencing a state. It is possible to learn that there is a dominant action across all states, in which case the prediction has little value. Therefore, if predictions cannot be credibly contracted upfront, the seller cannot extract the full value, and instead charges the same price to all buyers. |
| 主题 | Microeconomics ; Economics of Information ; Industrial Organization ; Market Structure and Firm Performance ; Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w24284 |
| 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/581956 |
| 推荐引用方式 GB/T 7714 | Ajay K. Agrawal,Joshua S. Gans,Avi Goldfarb. Human Judgment and AI Pricing. 2018. |
| 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 | ||
| w24284.pdf（198KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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