Stationary solution and H theorem for a generalized Fokker-Planck equation.

We investigate a family of generalized Fokker-Planck equations that contains Richardson and porous media equations as members. Considering a confining drift term that is related to an effective potential, we show that each equation of this family has a stationary solution that depends on this potential. This stationary solution encompasses several well-known probability distributions. Moreover, we verify an H theorem for the generalized Fokker-Planck equations using free-energy-like functionals. We show that the energy-like part of each functional is based on the effective potential and the entropy-like part is a generalized Tsallis entropic form, which has an unusual dependence on the position and can be related to a generalization of the Kullback-Leibler divergence. We also verify that the optimization of this entropic-like form subjected to convenient constraints recovers the stationary solution. The analysis presented here includes several studies about H theorems for other generalized Fokker-Planck equations as particular cases.

Anomalous diffusion behavior in parliamentary presence.

Concepts of statistical mechanics as well as other typical tools of physics have been largely used in the analysis of several aspects of social systems, for instance, in politics. In this work, we examine parliamentary presence utilizing data from the sessions of the 49th-54th Brazilian Chambers of Deputies (24 years, 1991-2015). For each federal deputy, we construct a random walk by considering their presence in a session as a step of unitary length and their absence as one of zero length. By using this approach, we put in evidence a quantitative description of the dynamics of the system. More specifically, we identify an anomalous diffusive process that corresponds to a robust superdiffusion, well identified with a ballistic regime. In addition, for each legislature and encompassing all its sessions, the system is modeled by a beta probability distribution, where the parliamentary presence scales with the number of sessions.

Characterizing time series via complexity-entropy curves.

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q-complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.