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Alexandre Tavares Baraviera

Publications and source records attributed to Alexandre Tavares Baraviera.

3 recordsLinked to original sources

Metric mean dimension and mean Hausdorff dimension varying the metric

Let $f: M\rightarrow M$ be a continuous map on a compact metric space $M$ equipped with a fixed metric $d$, and let $\tau$ be the topology on $M$ induced by $d$. First, we will establish some fundamental properties of the mean Hausdorff dimension. Furthermore, it is important to note that the metric mean dimension and mean Hausdorff dimension depend on the metric chosen for $M$. In this work, we will prove that, for a fixed dynamical system $f:M\rightarrow M$, the functions $\text{mdim}_{\text{M}}(M, f):M(\tau)\rightarrow \mathbb{R}\cup \{\infty\}$ and $\text{mdim}_{\text{H}}(M, f):M(\tau)\rightarrow \mathbb{R}\cup \{\infty\}$ are not continuous. Here, $ \text{mdim}_{\text{M}}(M, f)(\rho)= \text{mdim}_{\text{M}}(M,\rho, f)$ and $ \text{mdim}_{\text{H}}(M, f)(\rho)= \text{mdim}_{\text{H}}(M,\rho, f)$ represent, respectively, the metric mean dimension and the mean Hausdorff dimension of $f$ with respect to $\rho\in M(\tau)$ and $M(\tau)$ is the set consisting of all equivalent metrics to $d$ on $M$. Furthermore, we will present examples of certain classes of metrics for which the metric mean dimension is a continuous function.

math.DS

Entropy for $k$-trees defined by $k$ transition matrices

We study Markov tree-shifts given by $k$ transition matrices, one for each of its $k$ directions. We provide a method to characterize the complexity function for these tree-shifts, used to calculate the tree entropies defined by Ban and Chang arXiv:1509.08325 and Petersen and Salama arXiv:1712.02251. Moreover, we compare these definitions of entropy in order to determine some of their properties. The characterization of the complexity function provided is used to calculate the entropy of some examples. The question of existence of a specific type of invariant measures for such tree-shifts is addressed. Finally, we analyse some topological properties introduced by Ban and Chang arXiv:1509.01355 for the purpose of answering two of the questions raised by these authors.

math.DS

Modelling investment in artificial stock markets: Analytical and Numerical Results

In this article we study the behavior of a group of economic agents in the context of cooperative game theory, interacting according to rules based on the Potts Model with suitable modifications. Each agent can be thought of as belonging to a chain, where agents can only interact with their nearest neighbors (periodic boundary conditions are imposed). Each agent can invest an amount σ_{i}=0,...,q-1. Using the transfer matrix method we study analytically, among other things, the behavior of the investment as a function of a control parameter (denoted β) for the cases q=2 and 3. For q>3 numerical evaluation of eigenvalues and high precision numerical derivatives are used in order to assess this information.

cs.CE