Anabolic Persuasion

TL;DR

This paper models optimal training strategies for a sluggish agent, showing how stochastic, time-varying training can significantly enhance long-term capacity, with implications for exercise periodization.

Contribution

It introduces a novel Markov process model for training optimization, revealing how stochastic training schedules outperform static approaches for sluggish agents.

Findings

Stochastic training policies can maximize long-term capacity.

Incremental capacity adjustments influence training effectiveness.

Optimal policies depend on the invariant distribution of training intensity.

Abstract

We present a model of optimal training of a rational, sluggish agent. A trainer commits to a discrete-time, finite-state Markov process that governs the evolution of training intensity. Subsequently, the agent monitors the state and adjusts his capacity at every period. Adjustments are incremental: the agent's capacity can only change by one unit at a time. The trainer's objective is to maximize the agent's capacity - evaluated according to its lowest value under the invariant distribution - subject to an upper bound on average training intensity. We characterize the trainer's optimal policy, and show how stochastic, time-varying training intensity can dramatically increase the long-run capacity of a rational, sluggish agent. We relate our theoretical findings to "periodization" training techniques in exercise physiology.