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Motivated by the presence of deep connections among dynamical equations,experimental data, physical systems, and statistical modeling, we report on aseries of findings uncovered by the Authors and collaborators during the lastdecade within the framework of the so-called Information Geometric Approach toChaos (IGAC). The IGAC is a theoretical modeling scheme that combines methodsof information geometry with inductive inference techniques to furnishprobabilistic descriptions of complex systems in presence of limitedinformation. In addition to relying on curvature and Jacobi field computations,a suitable indicator of complexity within the IGAC framework is given by theso-called Information Geometric Entropy (IGE). The IGE is an informationgeometric measure of complexity of geodesic paths on curved statisticalmanifolds underlying the entropic dynamics of systems specified in terms ofprobability distributions. In this manuscript, we discuss several illustrativeexamples wherein our modeling scheme is employed to infer macroscopicpredictions when only partial knowledge of the microscopic nature of a givensystem is available. Finally, we include comments on the strengths andweaknesses of the current version of our proposed theoretical scheme in ourconcluding remarks.

An information geometric perspective on the complexity of macroscopic predictions arising from incomplete information