  One of the most intriguing dynamics in biological systems is the emergence of
clustering, the self-organization into separated agglomerations of individuals.
Several theories have been developed to explain clustering in, for instance,
multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of
fish, and animal herds. A persistent puzzle, however, is clustering of opinions
in human populations. The puzzle is particularly pressing if opinions vary
continuously, such as the degree to which citizens are in favor of or against a
vaccination program. Existing opinion formation models suggest that
"monoculture" is unavoidable in the long run, unless subsets of the population
are perfectly separated from each other. Yet, social diversity is a robust
empirical phenomenon, although perfect separation is hardly possible in an
increasingly connected world. Considering randomness did not overcome the
theoretical shortcomings so far. Small perturbations of individual opinions
trigger social influence cascades that inevitably lead to monoculture, while
larger noise disrupts opinion clusters and results in rampant individualism
without any social structure. Our solution of the puzzle builds on recent
empirical research, combining the integrative tendencies of social influence
with the disintegrative effects of individualization. A key element of the new
computational model is an adaptive kind of noise. We conduct simulation
experiments to demonstrate that with this kind of noise, a third phase besides
individualism and monoculture becomes possible, characterized by the formation
of metastable clusters with diversity between and consensus within clusters.
When clusters are small, individualization tendencies are too weak to prohibit
a fusion of clusters. When clusters grow too large, however, individualization
increases in strength, which promotes their splitting.
