| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24243 |
| 来源ID | Working Paper 24243 |
| Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence | |
| Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb | |
| 发表日期 | 2018-01-29 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | We interpret recent developments in the field of artificial intelligence (AI) as improvements in prediction technology. In this paper, we explore the consequences of improved prediction in decision-making. To do so, we adapt existing models of decision-making under uncertainty to account for the process of determining payoffs. We label this process of determining the payoffs ‘judgment.’ There is a risky action, whose payoff depends on the state, and a safe action with the same payoff in every state. Judgment is costly; for each potential state, it requires thought on what the payoff might be. Prediction and judgment are complements as long as judgment is not too difficult. We show that in complex environments with a large number of potential states, the effect of improvements in prediction on the importance of judgment depend a great deal on whether the improvements in prediction enable automated decision-making. We discuss the implications of improved prediction in the face of complexity for automation, contracts, and firm boundaries. |
| 主题 | Microeconomics ; Economics of Information ; Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w24243 |
| 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/581917 |
| 推荐引用方式 GB/T 7714 | Ajay K. Agrawal,Joshua S. Gans,Avi Goldfarb. Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence. 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 | ||
| w24243.pdf（557KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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