  We present an opinion model founded upon the principles of the bounded
confidence interaction among agents. Our objective is to explain the
polarization effects inherent to vector-valued opinions. The evolutionary
process adheres to the rule where each agent aspires to increase polarization
through communication with a single friend during each discrete time step. The
dynamics ensure that agents' ultimate (temporal) configuration will encompass a
finite number of outlier states. We introduce deterministic and stochastic
models, accompanied by a comprehensive mathematical analysis of their inherent
properties. Additionally, we provide compelling illustrative examples and
introduce a stochastic solver tailored for scenarios featuring an extensive set
of agents. Furthermore, in the context of smaller agent populations, we
scrutinize the suitability of neural networks for the rapid inference of limit
configurations.
