  Stochastic equations constitute a major ingredient in many branches of
science, from physics to biology and engineering. Not surprisingly, they appear
in many quantitative studies of complex systems. In particular, this type of
equation is useful for understanding the dynamics of urban population.
Empirically, the population of cities follows a seemingly universal law -
called Zipf's law - which was discovered about a century ago and states that
when sorted in decreasing order, the population of a city varies as the inverse
of its rank. Recent data however showed that this law is only approximate and
in some cases not even verified. In addition, the ranks of cities follow a
turbulent dynamics: some cities rise while other fall and disappear. Both these
aspects - Zipf's law (and deviations around it), and the turbulent dynamics of
ranks - need to be explained by the same theoretical framework and it is
natural to look for the equation that governs the evolution of urban
populations. We will review here the main theoretical attempts based on
stochastic equations to describe these empirical facts. We start with the
simple Gibrat model that introduces random growth rates, and we will then
discuss the Gabaix model that adds friction for allowing the existence of a
stationary distribution. Concerning the dynamics of ranks, we will discuss a
phenomenological stochastic equation that describes rank variations in many
systems - including cities - and displays a noise-induced transition. We then
illustrate the importance of exchanges between the constituents of the system
with the diffusion with noise equation. We will explicit this in the case of
cities where a stochastic equation for populations can be derived from first
principles and confirms the crucial importance of inter-urban migrations shocks
for explaining the statistics and the dynamics of the population of cities.
