  In the learning of systems of interacting particles or agents, coercivity
condition ensures identifiability of the interaction functions, providing the
foundation of learning by nonparametric regression. The coercivity condition is
equivalent to the strictly positive definiteness of an integral kernel arising
in the learning. We show that for a class of interaction functions such that
the system is ergodic, the integral kernel is strictly positive definite, and
hence the coercivity condition holds true.
