  We study coarse-grained (group-level) alignment dynamics of individual-based
animal group models for {\it heterogeneous} populations consisting of informed
(on preferred directions) and uninformed individuals. The orientation of each
individual is characterized by an angle, whose dynamics are nonlinearly coupled
with those of all the other individuals, with an explicit dependence on the
difference between the individual's orientation and the instantaneous average
direction. Choosing convenient coarse-grained variables (suggested by
uncertainty quantification methods) that account for rapidly developing
correlations during initial transients, we perform efficient computations of
coarse-grained steady states and their bifurcation analysis. We circumvent the
derivation of coarse-grained governing equations, following an equation-free
computational approach.
