Analyzing Strategic Parental Leave Decisions Using Two-Player Multi-Agent Reinforcement Learning

Despite the well-documented benefits of paid parental leave, many employees hesitate to take it. This study employs a two-player stochastic game (SG) model to analyze how various factors affect parental leave decisions. The proposed SG model incorporates (1) an employee’s perceived utility from taking leave, (2) the effect of colleague’s parental leave, (3) career penalties after taking leave, and (4) a paid parental policy. To accurately obtain equilibrium strategies, we extend Nash-Q learning by incorporating backward iteration and optimistic initialization. These two methods exploit the structural properties of the model to accelerate convergence and improve solution quality. Numerical experiments reveal that a stronger willingness to take parental leave and lower career penalties increase parental leave uptake. Furthermore, the competitive career penalty, which captures interpersonal factors, is particularly influential when a colleague is less likely to take parental leave. Our results suggest that reducing career penalties can substantially increase leave uptake in typical parameter ranges, highlighting the importance of workplace policies that mitigate career penalties associated with parental leave.