Information geometry of scaling expansions of non-exponentially growing configuration spaces
Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna,
2 Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080 Vienna, Austria
3 Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
4 IIASA, Schlossplatz 1, 2361 Laxenburg, Austria
a e-mail: firstname.lastname@example.org
Received in final form: 25 October 2019
Published online: 12 March 2020
Many stochastic complex systems are characterized by the fact that their configuration space doesn’t grow exponentially as a function of the degrees of freedom. The use of scaling expansions is a natural way to measure the asymptotic growth of the configuration space volume in terms of the scaling exponents of the system. These scaling exponents can, in turn, be used to define universality classes that uniquely determine the statistics of a system. Every system belongs to one of these classes. Here we derive the information geometry of scaling expansions of sample spaces. In particular, we present the deformed logarithms and the metric in a systematic and coherent way. We observe a phase transition for the curvature. The phase transition can be well measured by the characteristic length r, corresponding to a ball with radius 2r having the same curvature as the statistical manifold. Increasing characteristic length with respect to size of the system is associated with sub-exponential sample space growth which is related to strongly constrained and correlated complex systems. Decreasing of the characteristic length corresponds to super-exponential sample space growth that occurs for example in systems that develop structure as they evolve. Constant curvature means exponential sample space growth that is associated with multinomial statistics, and traditional Boltzmann-Gibbs, or Shannon statistics applies. This allows us to characterize transitions between statistical manifolds corresponding to different families of probability distributions.
© The Author(s) 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Open access funding provided by Medical University of Vienna.