  The bounded confidence model of opinion dynamics, introduced by Deffuant et
al, is a stochastic model for the evolution of continuous-valued opinions
within a finite group of peers. We prove that, as time goes to infinity, the
opinions evolve globally into a random set of clusters too far apart to
interact, and thereafter all opinions in every cluster converge to their
barycenter. We then prove a mean-field limit result, propagation of chaos: as
the number of peers goes to infinity in adequately started systems and time is
rescaled accordingly, the opinion processes converge to i.i.d. nonlinear Markov
(or McKean-Vlasov) processes; the limit opinion processes evolves as if under
the influence of opinions drawn from its own instantaneous law, which are the
unique solution of a nonlinear integro-differential equation of Kac type. This
implies that the (random) empirical distribution processes converges to this
(deterministic) solution. We then prove that, as time goes to infinity, this
solution converges to a law concentrated on isolated opinions too far apart to
interact, and identify sufficient conditions for the limit not to depend on the
initial condition, and to be concentrated at a single opinion. Finally, we
prove that if the equation has an initial condition with a density, then its
solution has a density at all times, develop a numerical scheme for the
corresponding functional equation, and show numerically that bifurcations may
occur.
