Many time-series exhibit "long memory": Their autocorrelation function decays slowly with lag. This behavior has traditionally been modeled via unit roots or fractional Brownian motion and explained via aggregation of heterogenous processes, nonlinearity, learning dynamics, regime switching or...

The concept of self-organizedcriticality evolved from studies of three simplecellular-automata models: the sand-pile, slider-block,and forest-fire models. In each case, there is asteady “input” and the “loss” is associated with afractal (power-law) distribution of “avalanches.” Each...

In this paper we define a new class of weighted complex networks sharing several properties with fractal sets, and whose topology can be completely analytically characterized in terms of the involved parameters and of the fractal dimension. General networks with fractal or hierarchical...

Daily fluctuations of the American dollar–Mexican peso exchange currency market are studied using multifractal analysis methods. It is found evidence of multiaffinity of daily fluctuations in the sense that the qth-order (roughness) Hurst exponent Hq varies with changes in q. It is also found...

The distribution of living languages is investigated and scaling relations are found for the diversity of languages as a function of the country area and population. These results are compared with data from Ecology and from computer simulations of fragmentation dynamics where similar scalings...

We introduce an inverse-cascade model to explain self-organized critical behavior. This model is motivated by the forest-fire model. In the forest-fire model trees are randomly planted on a grid, sparks are also dropped on the grid resulting in fires in which trees are lost. In the...

We propose a model which induces power-law behavior in double logarithmic diagram of highest income against rank. This model is so simple that we can take more factors of complicated situations into our simulation. A good agreement with the combined data of CEO in Japan and US is obtained.