| 来源类型 | Discussion paper
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| 规范类型 | 论文
|
| 来源ID | DP16911
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| DP16911 Training a Sluggish System |
| Kfir Eliaz; Ran Spiegler
|
| 发表日期 | 2022-01-17
|
| 出版年 | 2022
|
| 语种 | 英语
|
| 摘要 | Many organizational and biological systems need to maintain preparedness for external challenges. However, such systems tend to change their capabilities only gradually. How should we design training plans to enhance such systems' long-run preparedness? We present a model of optimal training plans for a rational, slowly adjusting system. A "trainer" commits to a Markov process governing the evolution of training intensity. At every time period, the system adjusts its "capability", which can only change by one unit at a time. The trainer maximizes long-run capability, subject to an upper bound on average training intensity. We consider two models of the system's adjustment: myopic/mechanistic and forward-looking. We characterize the optimal training plan in both cases and show how stochastic, time-varying intensity (resembling "periodization" techniques familiar from exercise physiology) dramatically increases long-run capability. |
| 主题 | Organizational Economics
|
| URL | https://cepr.org/publications/dp16911
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| 来源智库 | Centre for Economic Policy Research (United Kingdom)
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/545844
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推荐引用方式
GB/T 7714 |
Kfir Eliaz,Ran Spiegler. DP16911 Training a Sluggish System. 2022.
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