Working Paper: CEPR ID: DP16911
Authors: Kfir Eliaz; Ran Spiegler
Abstract: 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.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| training intensity (M53) | long-run capability (D25) |
| stochastic training (C45) | long-run capability (D25) |
| optimal training plan (M53) | long-run capability (D25) |
| training design (M53) | capability adjustments (D24) |