  Within an agent-based model where moral classifications are socially learned,
we ask if a population of agents behaves in a way that may be compared with
conservative or liberal positions in the real political spectrum. We assume
that agents first experience a formative period, in which they adjust their
learning style acting as supervised Bayesian adaptive learners. The formative
phase is followed by a period of social influence by reinforcement learning. By
comparing data generated by the agents with data from a sample of 15000 Moral
Foundation questionnaires we found the following. 1. The number of information
exchanges in the formative phase correlates positively with statistics
identifying liberals in the social influence phase. This is consistent with
recent evidence that connects the dopamine receptor D4-7R gene, political
orientation and early age social clique size. 2. The learning algorithms that
result from the formative phase vary in the way they treat novelty and
corroborative information with more conservative-like agents treating it more
equally than liberal-like agents. This is consistent with the correlation
between political affiliation and the Openness personality trait reported in
the literature. 3. Under the increase of a model parameter interpreted as an
external pressure, the statistics of liberal agents resemble more those of
conservative agents, consistent with reports on the consequences of external
threats on measures of conservatism. We also show that in the social influence
phase liberal-like agents readapt much faster than conservative-like agents
when subjected to changes on the relevant set of moral issues. This suggests a
verifiable dynamical criterium for attaching liberal or conservative labels to
groups.
