The Gompertz-Pareto income distribution

This work analyzes the Gompertz-Pareto distribution (GPD) of personal income, formed by the combination of the Gompertz curve, representing the overwhelming majority of the economically less favorable part of the population of a country, and the Pareto power law, which describes its tiny richest part. Equations for the Lorenz curve, Gini coefficient and the percentage share of the Gompertzian part relative to the total income are all written in this distribution. We show that only three parameters, determined by linear data fitting, are required for its complete characterization. Consistency checks are carried out using income data of Brazil from 1981 to 2007 and they lead to the conclusion that the GPD is consistent and provides a coherent and simple analytical tool to describe personal income distribution data.     

Properties of wealth distribution in multi-agent systems of a complex network

We present a simple model for examining the wealth distribution with agents playing evolutionary games (the Prisoners’ Dilemma and the Snowdrift Game) on complex networks. Pareto’s power law distribution of wealth (from 1897) is reproduced on a scale-free network, and the Gibbs or log-normal distribution for a low income population is reproduced on a random graph. The Pareto exponents of a scale-free network are in agreement with empirical observations. The Gini coefficient of an ER random graph shows a sudden increment with game parameters. We suggest that the social network of a high income group is scale-free, whereas it is more like a random graph for a low income group.     

Cross-correlation dynamics in financial time series

The dynamics of the equal-time cross-correlation matrix of multivariate financial time series is explored by examination of the eigenvalue spectrum over sliding time windows. Empirical results for the S&P 500 and the Dow Jones Euro Stoxx 50 indices reveal that the dynamics of the small eigenvalues of the cross-correlation matrix, over these time windows, oppose those of the largest eigenvalue. This behaviour is shown to be independent of the size of the time window and the number of stocks examined.A basic one-factor model is then proposed, which captures the main dynamical features of the eigenvalue spectrum of the empirical data. Through the addition of perturbations to the one-factor model, (leading to a ‘market plus sectors’ model), additional sectoral features are added, resulting in an Inverse Participation Ratio comparable to that found for empirical data. By partitioning the eigenvalue time series, we then show that negative index returns, (drawdowns), are associated with periods where the largest eigenvalue is greatest, while positive index returns, (drawups), are associated with periods where the largest eigenvalue is smallest. The study of correlation dynamics provides some insight on the collective behaviour of traders with varying strategies.     

Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling

We present an analysis of the empirical data and the agent-based modeling of the emotional behavior of users on the Web portals where the user interaction is mediated by posted comments, like Blogs and Diggs. We consider the dataset of discussion-driven popular Diggs, in which all comments are screened by machine-learning emotion detection in the text, to determine positive and negative valence (attractiveness and aversiveness) of each comment. By mapping the data onto a suitable bipartite network, we perform an analysis of the network topology and the related time-series of the emotional comments. The agent-based model is then introduced to simulate the dynamics and to capture the emergence of the emotional behaviors and communities. The agents are linked to posts on a bipartite network, whose structure evolves through their actions on the posts. The emotional states (arousal and valence) of each agent fluctuate in time, subject to the current contents of the posts to which the agent is exposed. By an agent’s action on a post its current emotions are transferred to the post. The model rules and the key parameters are inferred from the considered empirical data to ensure their realistic values and mutual consistency. The model assumes that the emotional arousal over posts drives the agent’s action. The simulations are preformed for the case of constant flux of agents and the results are analyzed in full analogy with the empirical data. The main conclusions are that the emotion-driven dynamics leads to long-range temporal correlations and emergent networks with community structure, that are comparable with the ones in the empirical system of popular posts. In view of pure emotion-driven agents actions, this type of comparisons provide a quantitative measure for the role of emotions in the dynamics on real blogs. Furthermore, the model reveals the underlying mechanisms which relate the post popularity with the emotion dynamics and the prevalence of negative emotions (critique). We also demonstrate how the community structure is tuned by varying a relevant parameter in the model. All data used in these works are fully anonymized.     

Ray-tracing evaluation of empirical models for predicting noise in industrial workshops

To evaluate the accuracy of empirical models for predicting steady-state noise levels and reverberation times in typical industrial workshops, predictions by these models were compared with predictions by a ray-tracing model, nominally using the same input data. Comparisons were made for three workshops—‘long’, ‘flat’ and ‘quasi-cubic’ in shape—with reflective and absorbent ceilings, when empty and with four densities of fittings. In the case of the ‘long’ room, noise levels were predicted along both the long and short major horizontal axes. In ‘long’ and ‘flat’ workshops, steady-state prediction by the empirical models often agreed with ray-tracing prediction within 2 dB. This result suggests that the empirical models are fundamentally valid. However, agreement was worse at large source/receiver distances, and at 500 Hz. Empirical reverberation-time prediction generally agreed less well with ray tracing, possibly indicating that the empirical reverberation-time prediction models are less valid. Strong disagreement occurred between the models in the case of steady-state prediction in the ‘quasi-cubic’ workshop, indicating that the empirical steady-state models are invalid in this case. Agreement for reverberation time was good with a non-absorbent ceiling, but poor with an absorbent ceiling.     

Individual and group dynamics in purchasing activity

As a major part of the daily operation in an enterprise, purchasing frequency is in constant change. Recent approaches on the human dynamics can provide some new insights into the economic behavior of companies in the supply chain. This paper captures the attributes of creation times of purchase orders to an individual vendor, as well as to all vendors, and further investigates whether they have some kind of dynamics by applying logarithmic binning to the construction of distribution plots. It’s found that the former displays a power-law distribution with approximate exponent 2.0, while the latter is fitted by a mixture distribution with both power-law and exponential characteristics. Obviously, two distinctive characteristics are presented for the interval time distribution from the perspective of individual dynamics and group dynamics. Actually, this mixing feature can be attributed to the fitting deviations as they are negligible for individual dynamics, but those of different vendors are cumulated and then lead to an exponential factor for group dynamics. To better describe the mechanism generating the heterogeneity of the purchase order assignment process from the objective company to all its vendors, a model driven by product life cycle is introduced, and then the analytical distribution and the simulation result are obtained, which are in good agreement with the empirical data.     

中国月极端高温对大气涛动的响应

利用广义帕雷托分布(GPD)模型研究了中国1960—2007年740站月极端高温(monthly extreme high-temperature,MEHT)对大气涛动响应的分布特征.通过构建空间GPD模型,分别将北大西洋涛动(NAO)和南方涛动(SO)作为强迫因子引入模型,考察了9种情景下MEHT响应的区域规律.结果表明NAO和SO对MEHT分布有潜在强迫作用,MEHT产生响应的空间分布主要集中在东北、青藏高原东部及长江、黄河流域和华南部分地区.具体表现为：1)MEHT明显依赖于NAO的敏感区为东北,其 关键词： 月极端高温 广义帕雷托分布 大气涛动 遥响应     

The temporal evolution of the city size distribution

We present an application of a growth model for a system of cities. This computer model simulates the evolution of systems with measurable entities (e.g. city size), and takes into account the growth of the entities in terms of size and number. It includes a random multiplicative process for the growth of individual entities and for the creation of new ones. We use a new mathematical expression with a positive exponent α (which we call the ‘shape exponent’) and additional three parameters, to describe the dynamics of the systems’ size distributions through time. We compare the changes of a real system of cities and the model’s results quantitatively and qualitatively. Our findings suggest that there is a good agreement at the macro level between the model and the real data.     

True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence

In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal (MSM) model. In order to see how well the estimated model captures the temporal dependence of the data, we estimate and compare the scaling exponents H(q) (for q=1,2) for both empirical data and simulated data of the MSM model. In most cases the multifractal model appears to generate ‘apparent’ long memory in agreement with the empirical scaling laws.     

Baseline Methods for the Parameter Estimation of the Generalized Pareto Distribution

In the parameter estimation of limit extreme value distributions, most employed methods only use some of the available data. Using the peaks-over-threshold method for Generalized Pareto Distribution (GPD), only the observations above a certain threshold are considered; therefore, a big amount of information is wasted. The aim of this work is to make the most of the information provided by the observations in order to improve the accuracy of Bayesian parameter estimation. We present two new Bayesian methods to estimate the parameters of the GPD, taking into account the whole data set from the baseline distribution and the existing relations between the baseline and the limit GPD parameters in order to define highly informative priors. We make a comparison between the Bayesian Metropolis–Hastings algorithm with data over the threshold and the new methods when the baseline distribution is a stable distribution, whose properties assure we can reduce the problem to study standard distributions and also allow us to propose new estimators for the parameters of the tail distribution. Specifically, three cases of stable distributions were considered: Normal, Lévy and Cauchy distributions, as main examples of the different behaviors of the tails of a distribution. Nevertheless, the methods would be applicable to many other baseline distributions through finding relations between baseline and GPD parameters via studies of simulations. To illustrate this situation, we study the application of the methods with real data of air pollution in Badajoz (Spain), whose baseline distribution fits a Gamma, and show that the baseline methods improve estimates compared to the Bayesian Metropolis–Hastings algorithm.     

Worm spreading with immunization: An interplay of spreading and immunity time scales

A model of epidemic spreading that is applicable to email worms, for instance, is studied analytically and numerically. It is built on mean-field percolation, and incorporates two time scales originating in spreading dynamics and immunization. A comparison to empirical data is provided. The long-time limit of the dynamics is governed by an exponential decay. We derive an analytic expression for the characteristic time of the decay, and find a good agreement with numerics. There is a similar decay also in empirical observations.     

Dynamics of the HIV infection under antiretroviral therapy: A cellular automata approach

The dynamics of human immunodeficiency virus infection under antiretroviral therapy is investigated using a cellular automata model where the effectiveness of each drug is self-adjusted by the concentration of CD4⁺  T infected cells present at each time step. The effectiveness of the drugs and the infected cell concentration at the beginning of treatment are the control parameters of the cell population’s dynamics during therapy. The model allows describing processes of mono and combined therapies. The dynamics that emerges from this model when considering combined antiretroviral therapies reproduces with fair qualitative agreement the phases and different time scales of the process. As observed in clinical data, the results reproduce the significant decrease in the population of infected cells and a concomitant increase of the population of healthy cells in a short timescale (weeks) after the initiation of treatment. Over long time scales, early treatment with potent drugs may lead to undetectable levels of infection. For late treatment or treatments starting with a low density of CD4⁺  T healthy cells it was observed that the treatment may lead to a steady state in which the T cell counts are above the threshold associated with the onset of AIDS. The results obtained are validated through comparison to available clinical trial data.     

A continuous model of human dynamics

Ideas and tools from statistical physics have recently been applied to the investigation of human dynamics. The timing of human activities, in particular, has been studied both experimentally and analytically. Empirical data show that, in many different situations, the time interval separating two consecutive tasks executed by an individual follows a heavy-tailed probability distribution rather than Poisson statistics. To account for this data, human behaviour has been viewed as a decision-based queuing system where individuals select and execute tasks belonging to a finite list of items as an increasing function of a task priority parameter. It is then possible to obtain analytically the empirical result P(τ)∼1/τ, where P(τ) is the waiting time probability distribution.Here a continuous model of human dynamics is introduced using instead an infinite queuing list. In contrast with the results obtained by other models in the finite case we find a waiting time distribution explicitly depending on the priority distribution density function ρ. The power-law scaling P(τ)∼1/τ is then recovered when ρ is exponentially distributed.     

Rates of profit as correlated sums of random variables

Profit realization is the dominant feature of market-based economic systems, determining their dynamics to a large extent. Rather than attaining an equilibrium, profit rates vary widely across firms, and the variation persists over time. Differing definitions of profit result in differing empirical distributions. To study the statistical properties of profit rates, I used data from a publicly available database for the US Economy for 2009–2010 (Risk Management Association). For each of three profit rate measures, the sample space consists of 771 points. Each point represents aggregate data from a small number of US manufacturing firms of similar size and type (NAICS code of principal product). When comparing the empirical distributions of profit rates, significant ‘heavy tails’ were observed, corresponding principally to a number of firms with larger profit rates than would be expected from simple models. An apparently novel correlated sum of random variables statistical model was used to model the data. In the case of operating and net profit rates, a number of firms show negative profits (losses), ruling out simple gamma or lognormal distributions as complete models for these data.     

Forward-like functions for dual parametrization of GPDs from the nonlocal chiral quark model

We derive the set of inversion relations allowing to establish the link between the dual parametrization of GPDs and a broad class of phenomenological models for GPDs. As an example we consider the results of the calculation of the pion GPD in the nonlocal chiral quark model (NlCQM) to recover the set of forward-like functions Q _2ν representing this GPD in the framework of dual parametrization. We also argue that the Abel tomography method overlooks possible δ -function-like contributions to the GPD quintessence function which make explicit contribution to the D form factor.     

Evolutionary model of the personal income distribution

The aim of this work is to develop a qualitative picture of the personal income distribution. Treating an economy as a self-organized system the key idea of the model is that the income distribution contains competitive and non-competitive contributions. The presented model distinguishes between three main income classes. 1. Capital income from private firms is shown to be the result of an evolutionary competition between products. A direct consequence of this competition is Gibrat’s law suggesting a lognormal income distribution for small private firms. Taking into account an additional preferential attachment mechanism for large private firms the income distribution is supplemented by a power law (Pareto) tail. 2. Due to the division of labor a diversified labor market is seen as a non-competitive market. In this case wage income exhibits an exponential distribution. 3. Also included is income from a social insurance system. It can be approximated by a Gaussian peak. A consequence of this theory is that for short time intervals a fixed ratio of total labor (total capital) to net income exists (Cobb–Douglas relation). A comparison with empirical high resolution income data confirms this pattern of the total income distribution. The theory suggests that competition is the ultimate origin of the uneven income distribution.     

A two-tail version of the PPS distribution with application to current account balance data

The double Pareto-positive stable (dPPS) distribution is introduced as a new model for describing countries’ global current account balance data. The dPPS distribution provides a flexible model for fitting the entire range of a set of current account data (both surplus and deficit), where zero and unimodality are possible, and the double-sided Pareto distribution is included as a particular case. Expressions for the cumulative distribution, probability density and quantile functions are given. An estimation method is discussed and a simple graphical method for studying the adequacy of the data to model is given. Finally, we consider the fit of countries’ global current account balance data for several years. The new distribution is compared with four classical models: Normal, Skew Normal, Asymmetric Laplace and Hyperbolic distributions. In all the data sets considered, the dPPS distribution outperforms the fits of these four distributions.     

Wealth distribution in modern and medieval societies

The power-law form of the upper part of the distribution of individual wealth/income (Pareto's law) is very well established for many countries and years. The Pareto index is however non-universal, varying typically from 1.5 to around 3. A recently introduced model for wealth exchange on an evolving family-network [Physica A 353, 515 (2005)] is reviewed and compared with empirical data. While the model mimics very well recent individual wealth data in a modern society (U.K.), it fails to explain results for a feudal society, based on the number of serf families owned by nobles (Hungary, mid XVI century). The unusually low (around 1) Pareto index found in this case is not compatible with the previous model. It is suggested that this fact may be interpreted as a result of the absence of active trading among agents.     

On the origin of bursts and heavy tails in animal dynamics

Over recent years there has been an accumulation of evidence that many animal behaviours are characterised by common scale-invariant patterns of switching between two contrasting activities over a period of time. This is evidenced in mammalian wake-sleep patterns, in the intermittent stop-start locomotion of Drosophila fruit flies, and in the Lévy walk movement patterns of a diverse range of animals in which straight-line movements are punctuated by occasional turns. Here it is shown that these dynamics can be modelled by a stochastic variant of Barabási’s model [A.-L. Barabási, The origin of bursts and heavy tails in human dynamics, Nature 435 (2005) 207-211] for bursts and heavy tails in human dynamics. The new model captures a tension between two competing and conflicting activities. The durations of one type of activity are distributed according to an inverse-square power-law, mirroring the ubiquity of inverse-square power-law scaling seen in empirical data. The durations of the second type of activity follow exponential distributions with characteristic timescales that depend on species and metabolic rates. This again is a common feature of animal behaviour. Bursty human dynamics, on the other hand, are characterised by power-law distributions with scaling exponents close to −1 and −3/2.     

Pareto and Boltzmann-Gibbs behaviors in a deterministic multi-agent system

A deterministic system of interacting agents is considered as a model for economic dynamics. The dynamics of the system is described by a coupled map lattice with nearest neighbor interactions. The evolution of each agent results from the competition between two factors: the agent’s own tendency to grow and the environmental influence that moderates this growth. Depending on the values of the parameters that control these factors, the system can display Pareto or Boltzmann-Gibbs statistical behaviors in its asymptotic dynamical regime. The regions where these behaviors appear are calculated on the space of parameters of the system. Other statistical properties, such as the mean wealth, the standard deviation, and the Gini coefficient characterizing the degree of equity in the wealth distribution are also calculated.