  We study opinion dynamics in a population of interacting adaptive agents
voting on a set of complex multidimensional issues. We consider agents which
can classify issues into for or against. The agents arrive at the opinions
about each issue in question using an adaptive algorithm. Adaptation comes from
learning and the information for the learning process comes from interacting
with other neighboring agents and trying to change the internal state in order
to concur with their opinions. The change in the internal state is driven by
the information contained in the issue and in the opinion of the other agent.
We present results in a simple yet rich context where each agent uses a Boolean
Perceptron to state its opinion. If there is no internal clock, so the update
occurs with asynchronously exchanged information among pairs of agents, then
the typical case, if the number of issues is kept small, is the evolution into
a society thorn by the emergence of factions with extreme opposite beliefs.
This occurs even when seeking consensus with agents with opposite opinions. The
curious result is that it is learning from those that hold the same opinions
that drives the emergence of factions. This results follows from the fact that
factions are prevented by not learning at all from those agents that hold the
same opinion. If the number of issues is large, the dynamics becomes trapped
and the society does not evolve into factions and a distribution of moderate
opinions is observed. We also study the less realistic, but technically simpler
synchronous case showing that global consensus is a fixed point. However, the
approach to this consensus is glassy in the limit of large societies if agents
adapt even in the case of agreement.
