Narrow Results

Economy is demanding new models, able to understand and predict the evolution of markets. To this respect, Econophysics offers models of markets as complex systems, that try to comprehend macro-, system-wide states of the economy from the interaction of many agents at micro-level. One of these models is the gas-like model for trading markets. This tries to predict money distributions in closed economies and quite simply, obtains the ones observed in real economies. However, it reveals technical hitches to explain the power law distribution, observed in individuals with high incomes. In this work, nonlinear dynamics is introduced in the gas-like model in an effort to overcomes these flaws. A particular chaotic dynamics is used to break the pairing symmetry of agents (i, j) ⇔ (j, i). The results demonstrate that a "chaotic gas-like model" can reproduce the Exponential and Power law distributions observed in real economies. Moreover, it controls the transition between them. This may give some insight of the micro-level causes that originate unfair distributions of money in a global society. Ultimately, the chaotic model makes obvious the inherent instability of asymmetric scenarios, where sinks of wealth appear and doom the market to extreme inequality.

Within the context of agent-based Monte-Carlo simulations, we study the problem of the fluctuations of tax evasion in a community of honest citizens and tax evaders by using the version of the nonequilibrium Zaklan model proposed by Lima (2010). The studied evolutionary dynamics of tax evasion are driven by a non-equilibrium majority-vote model of M. J. Oliveira, with the objective to attempt to control the fluctuations of the tax evasion in the observed community in which citizens are localized on the nodes of the Stauffer–Hohnisch–Pittnauer networks.

The Zaklan model had been proposed and studied recently using the equilibrium Ising model on square lattices (SLs) by [G. Zaklan, F. Westerhoff and D. Stauffer, J. Econ. Interact. Coord.4, 1 (2008), arXiv:0801.2980; G. Zaklan, F. W. S. Lima and F. Westerhoff, Physica A387, 5857 (2008)], near the critical temperature of the Ising model presenting a well-defined phase transition; but on normal and modified Apollonian networks (ANs), [J. S. Andrade, Jr., H. J. Herrmann, R. F. S. Andrade, and L. R. da Silva, Phys. Rev. Lett.94, 018702 (2005); R. F. S. Andrade, J. S. Andrade Jr. and H. J. Herrmann, Phys. Rev. E79, 036105 (2009)] studied the equilibrium Ising model. They showed the equilibrium Ising model not to present on ANs a phase transition of the type for the 2D Ising model. Here, using agent-based Monte Carlo simulations, we study the Zaklan model with the well-known majority-vote model (MVM) with noise and apply it to tax evasion on ANs, to show that differently from the Ising model the MVM on ANs presents a well-defined phase transition. To control the tax evasion in the economics model proposed by Zaklan et al., MVM is applied in the neighborhood of the critical noise q_c to the Zaklan model. Here we show that the Zaklan model is robust because this can also be studied, besides using equilibrium dynamics of Ising model, through the nonequilibrium MVM and on various topologies giving the same behavior regardless of dynamic or topology used here.

Boltzmann–Gibbs (BG) distribution arises as the statistical equilibrium probability distribution of money among the agents of a closed economic system where random and undirected exchanges are allowed. When considering a model with uniform savings in the exchanges, the final distribution is close to the gamma family. In this paper, we implement these exchange rules on networks and we find that these stationary probability distributions are robust and they are not affected by the topology of the underlying network. We introduce a new family of interactions: random but directed ones. In this case, it is found the topology to be determinant and the mean money per economic agent is related to the degree of the node representing the agent in the network. The relation between the mean money per economic agent and its degree is shown to be linear.

The so-called "Yard-Sale Model" of wealth distribution posits that wealth is transferred between economic agents as a result of transactions whose size is proportional to the wealth of the less wealthy agent. In recent work [B. M. Boghosian, Phys. Rev. E89, 042804 (2014)], it was shown that this results in a Fokker–Planck equation governing the distribution of wealth. With the addition of a mechanism for wealth redistribution, it was further shown that this model results in stationary wealth distributions that are very similar in form to Pareto's well-known law. In this paper, a much simpler derivation of that Fokker–Planck equation is presented.

In many markets, large and small firms coexist. As large firms can in principle out-compete small ones, the actual presence of the latter asks for an explanation. In ours, we focus on the dimensionality of markets, which can change as a consequence of product innovations, preference elaboration or institutions. We show that increasing market dimensionality substantially enlarges the market periphery relative to the market center, which creates new potential niches for small firms. We thereby provide a parsimonious explanation for small firm subsistence.

In this paper, we use the version of the nonequilibrium Zaklan model via agent-based Monte-Carlo simulations to study the problem of the fluctuations of the tax evasion on a heterogeneous agents community of honest and tax evaders citizens. The time evolution of this system is performed by a nonequilibrium model known as majority-vote model, but with a different probability for each agent to disobey the majority vote of its neighbors.

Recent analysis of a Yard–Sale (YS) exchange model supplemented with redistributive proportional taxation suggested an asymptotic behavior $P(w)\sim 1∕w^{\mu }$ for the wealth distribution, with a parameter-dependent exponent $\mu$. Revisiting this problem, it is here shown analytically, and confirmed by extensive numerical simulation, that the asymptotic behavior of $P(w)$ is not power-law but rather a Gaussian. When taxation is weak, we furthermore show that a restricted-range power-law behavior appears for wealths around the mean value. The corresponding power-law exponent equals 3/2 when the return distribution has zero mean.

In this work, we study a model of tax evasion. We considered a fixed population divided in three compartments, namely honest tax payers, tax evaders and a third class between the mentioned two, which we call susceptibles to become evaders. The transitions among those compartments are ruled by probabilities, similarly to a model of epidemic spreading. These probabilities model social interactions among the individuals, as well as the government’s fiscalization. We simulate the model on fully-connected graphs, as well as on scale-free and random complex networks. For the fully-connected and random graph cases, we observe that the emergence of tax evaders in the population is associated with an active-absorbing nonequilibrium phase transition, that is absent in scale-free networks.

From the stock markets of six countries with high GDP, we study the stock indices, S&P 500 (NYSE, USA), SSE Composite (SSE, China), Nikkei (TSE, Japan), DAX (FSE, Germany), FTSE 100 (LSE, Britain) and NIFTY (NSE, India). The daily mean growth of the stock values is exponential. The daily price fluctuations about the mean growth are Gaussian, but with a nonzero asymptotic convergence. The growth of the monthly average of stock values is statistically self-similar to their daily growth. The monthly fluctuations of the price follow a Wiener process, with a decline of the volatility. The mean growth of the daily volume of trade is exponential. These observations are globally applicable and underline regularities across global stock markets.

The Levy–Levy–Solomon (LLS) model [M. Levy, H. Levy and S. Solomon, Econ. Lett.45, 103 (1994)] is one of the most influential agent-based economic market models. In several publications this model has been discussed and analyzed. Especially Lux and Zschischang [E. Zschischang and T. Lux, Physica A: Stat. Mech. Appl.291, 563 (2001)] have shown that the model exhibits finite-size effects. In this study, we extend existing work in several directions. First, we show simulations which reveal finite-size effects of the model. Second, we shed light on the origin of these finite-size effects. Furthermore, we demonstrate the sensitivity of the LLS model with respect to random numbers. Especially, we can conclude that a low-quality pseudo-random number generator has a huge impact on the simulation results. Finally, we study the impact of the stopping criteria in the market clearance mechanism of the LLS model.

In this work, we study a dynamics of tax evasion. We considered a fully-connected population divided in three compartments, namely honest tax payers, tax evaders and susceptibles, a class that is composed by honest tax payers that can become evaders. We consider a contagion model where the transitions among the compartments are governed by probabilities. Such probabilities represent the possible interactions among the individuals, as well as the government fiscalization. We show by analytical and numerical calculations that the emergence of tax evaders in the population is associated with an active-absorbing nonequilibrium first-order phase transition. In the absorbing phase, only honest tax payers survive in the steady states of the model, and we observe a coexistence of the three subpopulations in the active phase.

A study of the distribution of the value of traded goods under the Harmonized System is presented. The ramifications of this classification system are found to exhibit an approximate power law decay, indicating complexity and self-organization in the nomenclature of traded merchandises. For almost all countries with available data, log-values of annually imported and exported goods are well described by three-parameter Weibull distributions. This distribution commonly appears in particles size distributions, suggesting a connection between random fragmentation processes and the mechanisms behind the international trade of merchandises. Analysis of the resulting values for the fitting parameters from 1995 to 2018 shows a nearly constant linear relationship between the parameters of the Weibull distributions, so that, for each country, the distribution of log-values can be approximately characterized by a single shape parameter $\beta$. The empirical findings of this paper suggest that specialization on trading a constant set of goods prevents the values of all traded merchandises from growing/decreasing simultaneously.

The objective of this work is to analyze the Indice de Precios y Cotizaciones (IPC), which is the Mexican stock market index, by using several statistical tools in order to study the tendencies that can shed light on the evolution of the IPC towards a more efficient market. The methodology used is to apply the statistical tools to the Mexican index and compare the results with a mature and well-known market index such as the Dow Jones Industrial Average (DJIA). We employ an autocorrelation analysis, and the volatility of the indexes, applied to the daily returns of the closing price on a moving time window during the studied period (1980–2018). Additionally, we perform an order three permutation entropy analysis, which can quantify the disorder present in the time series. Our results show that there is evidence that the IPC has become more mature since its creation and that it can be considered an efficient market since around year 2000. The behavior of the several techniques used shows a similar behavior to the DJIA which is not observed before that year. There are some limitations mainly because there is no high frequency data that would permit a more detailed analysis, specifically in the periods before and after a crisis is located. Our conclusion is that since around the year 2000, the Mexican stock index displays the typical behavior of other mature markets and can be considered as one.

We study an extension of the Bornholdt spin-market model using configurable network topology. The distribution of returns was studied using the probability plot correlation coefficient and indicates three different behaviors for the tails of the distribution of returns. The price volatility was studied by fitting the magnetization with a Wiener process and a power-law behavior was found for the volatility as a function of the levels of randomness and connectivity of the network. Both parameters have opposing effects on the risk as inferred from the Shannon entropy of the magnetization. Also, we show that trading can spatially auto-organize depending on the conditions of the control space. Finally, we show that there is a range of values of the control space that renders a model that reproduces real-market data.

Sales per capita (per number of employees) of the 2000 Forbes Magazine top publicly-traded companies (G-2000) and some of the world’s leading state-owned enterprises (SOE) are statistically analyzed. This hybrid or combined cumulative probability sales distribution per capita exhibits a two-class structure: a Pareto power law in the higher part and exponential in the lower part resembling a Boltzmann–Gibbs distribution, where money is conserved in economic trade like energy is conserved in elastic collisions. This global per capita sales two-class distribution is qualitatively similar to income and wealth distributions in many countries around the world.

From the years 2001 to 2017, per capita nominal and real (adjusted to inflation) GDP at purchasing power parity (PCGDP-PPP) distributions for cities and regions are fitted to various functions. For most years and regions, real PCGDP-PPP data are very well adjusted to the one-parameter Boltzmann–Gibbs distribution (BGD), in accordance with the exponential behavior predicted by the simple econophysics analogy between conserved money in economic trade and energy in elastic collisions in gases. Overall, fittings are better for large regions in recent years, which may reflect an increasing economic globalization in time. Cities, small regions and large regions values are well fitted by stretched exponential distributions.

We use the logistic equation to model the dynamics of the GDP and the trade of the six countries with the highest GDP in the world, namely, USA, China, Japan, Germany, UK and India. From the modeling of the economic data, which are made available by the World Bank, we predict the maximum values of the growth of GDP and trade, as well as the duration over which exponential growth can be sustained. We set up the correlated growth of GDP and trade as the phase solutions of an autonomous second-order dynamical system. GDP and trade are related to each other by a power law, whose exponent seems to differentiate the six national economies into two types. Under conducive conditions for economic growth, our conclusions have general validity.

International seed trade networks (iSTNs) are complex networks formed by the trade of seeds between economies. We investigate the statistical properties of the international trade networks for maize seeds and rice seeds from the viewpoint of temporal directed and weighted networks. We find that most properties, including node and edge numbers, average in- and out-degrees, total and average trade values, network density and clustering coefficient, showed an increasing trend, which indicates that the increasing globalization of seed trade has led to more and more complete and dense trade networks. We also find that links with larger trade values have greater stability. In addition, we observe positive correlations between in-degree and out-degree, link reciprocity and in- and out-degrees, link reciprocity and in- and out-strengths and clustering coefficient and in- and out-degrees. In contrast, there are negative correlations between clustering coefficient and in- and out-strengths. Furthermore, we find that both networks are assortatively mixed in most years, which indicates that large exporters and importers are more likely to trade with other large exporters and importers. The structural properties of the two iSTNs of maize and rice share many qualitative similarities, but exhibit quantitative differences.

The lattice gas automaton (LGA) is proposed for a closed economic market of agents with heterogeneous saving interests. There are two procedures in the standard LGA, i.e. “propagation” + “transaction”. If the propagation step is removed and the transaction is conducted among all agents, the LGA reduces to a more simplified kinetic model. In addition, two dealing rules are imposed on the transaction phase. Under Rule I, the trading volume depends on the average saving propensities of an arbitrary pair of agents in trade. Under Rule II, the exchange is governed by a stochastic parameter between the saving propensities of two traders. Besides, two sampling methods are introduced for the random selection of two agents in the iterative process. Specifically, Sampling I is the sampling with replacement and is easier to program. Sampling II is the sampling without replacement and owns a higher computing efficiency. There are slight differences between the stationary wealth distributions simulated by using the two transaction rules and sampling approaches. In addition, the accuracy, robustness and efficiency of the econophysics models are validated by typical numerical tests. The reduced LGA without the propagation step owns a higher computational efficiency than the standard LGA. Moreover, the impact of saving propensities of agents in two groups on the wealth distributions is studied, and the influence of proportions of agents is investigated as well. To quantitatively measure the wealth inequality, the Gini coefficients, Kolkata indices, and deviation degrees of all agents and two groups are simulated and analyzed in detail. This work is helpful to further analyze and predict the dynamic process of wealth distribution in the realistic economic market.