  Motivated by the presence of deep connections among dynamical equations,
experimental data, physical systems, and statistical modeling, we report on a
series of findings uncovered by the Authors and collaborators during the last
decade within the framework of the so-called Information Geometric Approach to
Chaos (IGAC). The IGAC is a theoretical modeling scheme that combines methods
of information geometry with inductive inference techniques to furnish
probabilistic descriptions of complex systems in presence of limited
information. In addition to relying on curvature and Jacobi field computations,
a suitable indicator of complexity within the IGAC framework is given by the
so-called Information Geometric Entropy (IGE). The IGE is an information
geometric measure of complexity of geodesic paths on curved statistical
manifolds underlying the entropic dynamics of systems specified in terms of
probability distributions. In this manuscript, we discuss several illustrative
examples wherein our modeling scheme is employed to infer macroscopic
predictions when only partial knowledge of the microscopic nature of a given
system is available. Finally, we include comments on the strengths and
weaknesses of the current version of our proposed theoretical scheme in our
concluding remarks.
