O Comportamento Cíclico do Capital dos Bancos Brasileiros

In the context of the implementation of the Basel II accord, this paper analyzes the cyclical behavior of Brazilian bank capital under the current regulation. We use an unbalanced panel data of banks operating in Brazil between 2003 and 2008 to estimate an equation of the bank economic capital. Our results show that this variable moves with the business cycle.

Mapping dynamical systems onto complex networks

A procedure to characterize chaotic dynamical systems with concepts of complex networks is pursued, in which a dynamical system is mapped onto a network. The nodes represent the regions of space visited by the system, while edges represent the transitions between these regions. Parameters used to quantify the properties of complex networks, including those related to higher order neighborhoods, are used in the analysis. The methodology is tested for the logistic map, focusing the onset of chaos and chaotic regimes. It is found that the corresponding networks show distinct features, which are associated to the particular type of dynamics that have generated them.Comment: 13 pages, 8 eps files in 5 figure

The role of optimization in the human dynamics of tasks execution

In order to explain the empirical evidence that the dynamics of human activity may not be well modeled by Poisson processes, a model based on queuing processes were built in the literature \cite{bar05}. The main assumption behind that model is that people execute their tasks based on a protocol that execute firstly the high priority item. In this context, the purpose of this letter is to analyze the validity of that hypothesis assuming that people are rational agents that make their decisions in order minimize the cost of keeping non-executed tasks on the list. Therefore, we build and solve analytically a dynamic programming model with two priority types of tasks and show that the validity of this hypothesis depends strongly on the structure of the instantaneous costs that a person has to face if a given task is kept on the list for more than one step. Moreover, one interesting finding is that in one of the situations the protocol used to execute the tasks generates complex one dimensional dynamics

Self-organization of developing embryo using scale-invariant approach

<p>Abstract</p> <p>Background</p> <p>Self-organization is a fundamental feature of living organisms at all hierarchical levels from molecule to organ. It has also been documented in developing embryos.</p> <p>Methods</p> <p>In this study, a scale-invariant power law (SIPL) method has been used to study self-organization in developing embryos. The SIPL coefficient was calculated using a centro-axial skew symmetrical matrix (CSSM) generated by entering the components of the Cartesian coordinates; for each component, one CSSM was generated. A basic square matrix (BSM) was constructed and the determinant was calculated in order to estimate the SIPL coefficient. This was applied to developing <it>C. elegans </it>during early stages of embryogenesis. The power law property of the method was evaluated using the straight line and Koch curve and the results were consistent with fractal dimensions (fd). Diffusion-limited aggregation (DLA) was used to validate the SIPL method.</p> <p>Results and conclusion</p> <p>The fractal dimensions of both the straight line and Koch curve showed consistency with the SIPL coefficients, which indicated the power law behavior of the SIPL method. The results showed that the ABp sublineage had a higher SIPL coefficient than EMS, indicating that ABp is more organized than EMS. The fd determined using DLA was higher in ABp than in EMS and its value was consistent with type 1 cluster formation, while that in EMS was consistent with type 2.</p

Learning paths in complex networks

This letter addresses the issue of learning shortest paths in complex networks, which is of utmost importance in real-life navigation. The approach has been partially motivated by recent progress in characterizing navigation problems in networks, having as extreme situations the completely ignorant (random) walker and the rich directed walker, which can pay for information that will guide to the target node along the shortest path. A learning framework based on a first-visit Monte Carlo algorithm is implemented, together with four independent measures that characterize the learning process. The methodology is applied to a number of network classes, as well as to networks constructed from actual data. The results indicate that the navigation difficulty and learning velocity are strongly related to the network topology

Topological properties of commodities networks

This paper investigates the topological properties of the commodities networks. We have found that commodities form strong clusters and are homogeneous with relation to sector (metals, agriculture and energy). We also develop a dynamic approach suggesting that agriculture commodities are very important in the network, followed by metals and energy. Furthermore, the parameters that characterize the network seem to be changing over time