Multi-time scales decision-making processes emerge in several modern engineering systems. In this paper, we deal with a leader and a population of followers in a hierarchical decision-making structure. A Stackelberg game learning framework is proposed to solve bilevel optimization problems as dynamical systems, considering their applications to hierarchical decision-making problems. For the upper-level, gradient descent dynamics in continuous time are proposed, and population dynamics is proposed to solve the lower-level optimization algorithm, in order to obtain learning dynamics in two-time scales. Stability and convergence results are presented for the learning dynamics of the proposed Stackelberg population dynamics. Finally, an application of the proposed framework is developed for pricing coordination of distributed energy resources in a distribution power network.