  We study an endogenous opinion (or, belief) dynamics model where we
endogenize the social network that models the link (`trust') weights between
agents. Our network adjustment mechanism is simple: an agent increases her
weight for another agent if that agent has been close to truth (whence, our
adjustment criterion is `past performance'). Moreover, we consider multiply
biased agents that do not learn in a fully rational manner but are subject to
persuasion bias - they learn in a DeGroot manner, via a simple `rule of thumb'
- and that have biased initial beliefs. In addition, we also study this setup
under conformity, opposition, and homophily - which are recently suggested
variants of DeGroot learning in social networks - thereby taking into account
further biases agents are susceptible to. Our main focus is on crowd wisdom,
that is, on the question whether the so biased agents can adequately aggregate
dispersed information and, consequently, learn the true states of the topics
they communicate about. In particular, we present several conditions under
which wisdom fails.
