  In this work we investigate knowledge acquisition as performed by multiple
agents interacting as they infer, under the presence of observation errors,
respective models of a complex system. We focus the specific case in which, at
each time step, each agent takes into account its current observation as well
as the average of the models of its neighbors. The agents are connected by a
network of interaction of Erd\H{o}s-Renyi or Barabasi-Albert type. First we
investigate situations in which one of the agents has a different probability
of observation error (higher or lower). It is shown that the influence of this
special agent over the quality of the models inferred by the rest of the
network can be substantial, varying linearly with the respective degree of the
agent with different estimation error. In case the degree of this agent is
taken as a respective fitness parameter, the effect of the different estimation
error is even more pronounced, becoming superlinear. To complement our
analysis, we provide the analytical solution of the overall behavior of the
system. We also investigate the knowledge acquisition dynamic when the agents
are grouped into communities. We verify that the inclusion of edges between
agents (within a community) having higher probability of observation error
promotes the loss of quality in the estimation of the agents in the other
communities.

Physics and Society;Computational Physics