| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24541 |
| 来源ID | Working Paper 24541 |
| Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth | |
| Ajay Agrawal; John McHale; Alex Oettl | |
| 发表日期 | 2018-04-23 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | Innovation is often predicated on discovering useful new combinations of existing knowledge in highly complex knowledge spaces. These needle-in-a-haystack type problems are pervasive in fields like genomics, drug discovery, materials science, and particle physics. We develop a combinatorial-based knowledge production function and embed it in the classic Jones growth model (1995) to explore how breakthroughs in artificial intelligence (AI) that dramatically improve prediction accuracy about which combinations have the highest potential could enhance discovery rates and consequently economic growth. This production function is a generalization (and reinterpretation) of the Romer/Jones knowledge production function. Separate parameters control the extent of individual-researcher knowledge access, the effects of fishing out/complexity, and the ease of forming research teams. |
| 主题 | Development and Growth ; Innovation and R& ; D ; Growth and Productivity ; Other ; Culture |
| URL | https://www.nber.org/papers/w24541 |
| 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/582214 |
| 推荐引用方式 GB/T 7714 | Ajay Agrawal,John McHale,Alex Oettl. Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth. 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 | ||
| w24541.pdf（676KB） | 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。
