  We evaluate the information geometric complexity of entropic motion on
low-dimensional Gaussian statistical manifolds in order to quantify how
difficult is making macroscopic predictions about a systems in the presence of
limited information. Specifically, we observe that the complexity of such
entropic inferences not only depends on the amount of available pieces of
information but also on the manner in which such pieces are correlated.
Finally, we uncover that for certain correlational structures, the
impossibility of reaching the most favorable configuration from an entropic
inference viewpoint, seems to lead to an information geometric analog of the
well-known frustration effect that occurs in statistical physics.
