  In many real-world large-scale decision problems, self-interested agents have
individual dynamics and optimize their own long-term payoffs. Important
examples include the competitive access to shared resources (e.g., roads,
energy, or bandwidth) but also non-engineering domains like epidemic
propagation and control. These problems are natural to model as mean-field
games. Existing mathematical formulations of mean field games have had limited
applicability in practice, since they require solving non-standard
initial-terminal-value problems that are tractable only in limited special
cases. In this letter, we propose a novel formulation, along with computational
tools, for a practically relevant class of Dynamic Population Games (DPGs),
which correspond to discrete-time, finite-state-and-action, stationary
mean-field games. Our main contribution is a mathematical reduction of
Stationary Nash Equilibria (SNE) in DPGs to standard Nash Equilibria (NE) in
static population games. This reduction is leveraged to guarantee the existence
of a SNE, develop an evolutionary dynamics-based SNE computation algorithm, and
derive simple conditions that guarantee stability and uniqueness of the SNE. We
provide two examples of applications: fair resource allocation with
heterogeneous agents and control of epidemic propagation. Open source software
for SNE computation: https://gitlab.ethz.ch/elokdae/dynamic-population-games
