Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn
研究了异质性员工随时间学习时的招聘与留任问题，将其建模为带切换成本的无限臂赌博机，并利用Bergemann和Välimäki的结果刻画最优政策，数值例子表明主动监控和筛选员工的价值显著。
We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinite-armed bandit with switching costs, and we apply results from Bergemann and Välimäki [Bergemann D, Välimäki J (2001) Stationary multi-choice bandit problems. J. Econom. Dynam. Control 25(10):1585–1594] to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial. This paper was accepted by Yossi Aviv, operations management.