a gas, has later proved to be a powerful tool to analyze the asymptotic behavior of systems of interacting agents. Recent applications range from biological, social and economical phenomena [@albi2015invisible; @parisi08; @carrillo2014derivation] to automatic learning [@maggioni17; @huang2018learning] and optimization heuristics [@dorigo2005ant; @kennedy2011particle]. The underlying idea is that the collective behavior of large systems of particles (agent-based models, usually consisting of a set of ODE’s) can be efficiently treated by replacing the influence of all the other individuals in the dynamics on a given agent by a single averaged effect. From a mathematical point of view, this amounts to passing from a particle description to a kinetic description, consisting of a limit PDE whose unknown is the particle density distribution in the state space. The well-posedness of such models has therefore to be proven in spaces of measures (see, for instance, the results in [@CCR2011]).

In some of the applications, the interacting agents are assumed to belong to a number of different species, or populations [@albi2014boltzmann; @albi2017opinion; @cirant2015multi; @francesco2013measure; @fornasier2014mean]. This is a useful modeling assumption, *e.g.*, in the theory of mean-field games, or in control theory, where it can been used to distinguish informed agents steering pedestrians to leave unknown 