  In this paper we investigate the possibility of reducing the complexity of a
system composed of a large number of interacting agents, whose dynamics feature
a symmetry breaking. We consider first order stochastic differential equations
describing the behavior of the system at the particle (i.e., Lagrangian) level
and we get its continuous (i.e., Eulerian) counterpart via a kinetic
description. However, the resulting continuous model alone fails to describe
adequately the evolution of the system, due to the loss of granularity which
prevents it from reproducing the symmetry breaking of the particle system. By
suitably coupling the two models we are able to reduce considerably the
necessary number of particles while still keeping the symmetry breaking and
some of its large-scale statistical properties. We describe such a multiscale
technique in the context of opinion dynamics, where the symmetry breaking is
induced by the results of some opinion polls reported by the media.
