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The evacuation of football stadium scenarios as examples of evolution of complex system are discussed. The models are presented as movements of individuals according to fields of displacements, calculated correspondingly to the given scenario. The assumption has been made that the most efficient evacuation is left based on the accordance of symmetry of allowed space, and this symmetry is taken into account while calculating the displacements field. The displacements related to every point of this space are calculated by the symmetry analysis method and fulfill the symmetry conditions of allowed space. The speed of each individual at every point in the presented model has the same quantity. Consequently, the times of evacuation and average presses acting on individuals during the evacuation are given. Both parameters are compared with and without symmetry considerations. They are calculated in the simulation procedure. For the realization of the simulation tasks the new program (using modified Helbing model) has been elaborated.
The primary thesis of this paper is that a nonlinear dynamical systems theory provides a basis for conducting all kinds of comparisons in the theory of business cycles, and it also enables its further development. A cognitive aim was to show that applying the theory of bifurcations and morphogenesis in the domain of economic fluctuations allows us to construct models of the cycle with greater explicatory and utility values than there were so far. In this way, the precision and consistency of the theory increases. In this field, applications of catastrophe theory are of great importance. Another fact was indicated, namely the theory of deterministic chaos places the issues of explanation and forecasting in economics in a totally different light. It turns out that we are dealing with at least three sources of complexity in economic systems: chaotic attractors, invariant chaotic sets that are not attracting in the form of chaotic saddles and the effects of fractal basin boundaries. This, in turn, limits the effectiveness of traditional economic policy. Economic management should be based on procedures that lower complexity of economic systems, however sometimes lower complexity incurs bigger instability. The paper is a survey of applications of nonlinear dynamical systems to mathematical business cycle models. The survey encompasses both earlier models that were built in 1970s, as well as later concepts. The paper also features a few of my newest results of numerical studies of some nonlinear economic systems.
The term "complexity" used frequently as a kind of "buzzword" has gained a specific role in the language of modern social sciences and social practice. A question is arising - how can we understand complexity of social systems/social phenomena which are characterized by limited possibility of explanation, unpredictability or low reliability of prediction? The aim of the paper is to provide a partial answer to this question. A survey of characteristics of complex systems and typology of various kinds of complexity, and of their sources is presented. It is also shown that broadly defined social systems (human systems) are affected by all kinds of complexity - they are "complexities of complexities". Using the typology of interpretations of complexity as an example, it is shown what are the limitations of transferring knowledge from physics, chemistry, information theory and biology to the studies of complexity of social systems. It is especially emphasized that mathematical models, which are treated as objective when applied in social sciences must be considered as an element of intersubjective discourse.
Using the United Nations Commodity Trade Statistics Database we construct the Google matrix of the world trade network and analyze its properties for various trade commodities for all countries and all available years from 1962 to 2009. The trade flows on this network are classified with the help of PageRank and CheiRank algorithms developed for the World Wide Web and other large scale directed networks. For the world trade this ranking treats all countries on equal democratic grounds independent of country richness. Still this method puts at the top a group of industrially developed countries for trade in all commodities. Our study establishes the existence of two solid state like domains of rich and poor countries which remain stable in time, while the majority of countries are shown to be in a gas like phase with strong rank fluctuations. A simple random matrix model provides a good description of statistical distribution of countries in two-dimensional rank plane. The comparison with usual ranking by export and import highlights new features and possibilities of our approach.
We study the inter-stock correlations for the largest companies listed on Warsaw Stock Exchange and included in the WIG20 index. Our results from the correlation matrix analysis indicate that the Polish stock market can be well described by a one-factor model. We also show that the stock- stock correlations tend to increase with the timescale of returns and they approach a saturation level for the timescales of at least 200 min, i.e. an order of magnitude longer than in the case of some developed markets. We also show that the strength of correlations among the stocks crucially depends on their capitalization. These results combined with our earlier findings together suggest that now the Polish stock market situates itself somewhere between an emerging market phase and a mature market phase.
The phenomenon of epidemic spreading in a real social network is described and investigated numerically. On the basis of data concerning amount of time devoted daily to social interactions, the influence of human activity on spreading process is investigated in the frame of SIRS model. It was found that the activity of an individual is positively correlated with its connectivity and the relation has power law form. The influence of control measures on the spreading process is investigated as a function of initial conditions. The cost-effectiveness of mass immunizations campaigns and target vaccinations is compared. It was found that the form of activity distribution has significant influence on the spreading phenomena in the network.
The catastrophe theory and deterministic chaos constitute the basic elements of economic complexity. Elementary catastrophes were the first remarkable form of nonlinear, topological complexity that were thoroughly studied in economics. Another type of catastrophe is the complexity catastrophe, namely an increase in the complexity of a system beyond a certain threshold which marks the beginning of a decrease in a system's adaptive capacity. As far as the ability to survive is concerned, complex adaptive systems should function within the range of optimal complexity which is neither too low or too high. Deterministic chaos and other types of complexity follow from the catastrophe theory. In general, chaos is seemingly random behavior of a deterministic system which stems from its high sensitivity to the initial condition. The theory of nonlinear dynamical systems, which unites various manifestations of complexity into one integrated system, runs contrary to the assumption that markets and economies spontaneously strive for a state of equilibrium. The opposite applies: their complexity seems to grow due to the influence of classical economic laws.
Different variants of multifractal detrended fluctuation analysis technique are applied in order to investigate various (artificial and real-world) time series. Our analysis shows that the calculated singularity spectra are very sensitive to the order of the detrending polynomial used within the multifractal detrended fluctuation analysis method. The relation between the width of the multifractal spectrum (as well as the Hurst exponent) and the order of the polynomial used in calculation is evident. Furthermore, type of this relation itself depends on the kind of analyzed signal. Therefore, such an analysis can give us some extra information about the correlative structure of the time series being studied.
Results of Monte Carlo simulations of p-spin models on scale-free hypernetworks are presented. The hypernetworks are obtained using the preferential attachment algorithm, the spins are located in the nodes and the hyperedges connecting p nodes correspond to non-zero ferromagnetic interactions involving p spins. Such models show high degeneracy of the ground state: apart from the ferromagnetic state, depending on the parameters of the preferential attachment algorithm leading to different topologies of the obtained hypernetworks, there are several or even infinitely many disordered (glassy) states with the same energy. For various network topologies quantities such as the specific heat or magnetic susceptibility show maxima as functions of the temperature, which suggests the occurrence of the glassy or ferromagnetic phase transition. The models under study may serve as a starting point for modelling various forms of cooperation in social and economic sciences involving many-body rather than two-body interactions.
This article presents a network algorithm for identifying transactions which may constitute a violation of restricted periods, namely, making transactions in company shares by persons possessing inside information. The empirical research was performed on the basis of publicly available information on exchange trading, originating from the Warsaw Stock Exchange. The analysis is based on a numerical model which describes information spreading in a network with an information bottleneck. The applied method can confirm with high probability the use of inside information for carrying out unauthorized stock market transactions.
We use methods of non-extensive statistical physics to describe quantitatively the memory effect involved in returns of companies from WIG 30 index on the Warsaw Stock Exchange. The entropic approach based on the generalization of the Boltzmann-Gibbs entropy to non-additive Tsallis q-entropy is applied to fit fat tailed distribution of returns to q-normal (Tsallis) distribution. The existence of long term memory effects in price returns generated by two-point autocorrelations are checked via calculation of the Hurst exponent within detrended fluctuation analysis approach. The results are collected for diversified frequency of data sampling. We confirm the perfect inverse cubic power law for low time-lags (≈1 min) of returns for the main WIG 30 index as well as for the most of separate stocks, however this relationship does not hold for longer time-lags. The particular emphasis is given to a study of an independent fit of probability distribution of positive and negative returns to q-normal distribution. We discuss in this context the asymmetry between tails in terms of the Tsallis parameters q^{±}. A qualitative and quantitative relationship between the frequency of data sampling, the parameters q and q^{±}, and the corresponding main Hurst exponent H is provided to analyze the effect of memory in data caused by linear and nonlinear autocorrelations. A new quantifier based on asymmetry of the Tsallis index instead of skewness of distribution is proposed which we believe is able to describe the stage of market development and its robustness to speculation.
This paper presents the quantitative characteristics of correlations (and cross-correlations) of plant main eco-factors i.e. the ground and over-ground temperature, the wind speed, and the humidity. The study is based upon hourly data statistical observations collected in the region of Lublin, in Poland for the period 2001.05.07-2009.04.10. This paper indicates that plant growth conditions constitute an emergent response to the above direct eco-factors. Then, the dynamics properties of each eco-factor is first analyzed alone for its multifractal structure. We apply the multifractal detrended correlation analysis and multifractal detrended cross-correlation analysis. We show that the widest multifractal spectrum is for over-ground temperature and the strongest power-law cross-correlations exist between ground and over-ground temperature. Next, an impulse response analysis is carried out to measure dynamical inter causalities within all the considered variables. As far as cross-impact between different eco-variables is concerned, one observes that the wind speed, the ground temperature and the air humidity dynamics are the most influenced, in terms of memory length time, by external temperature.
The econophysics approach to socio-economic systems is based on the assumption of their complexity. Such assumption inevitably leads to another assumption, namely that underlying interconnections within socio-economic systems, particularly financial markets, are nonlinear, which is shown to be true even in mainstream economic literature. Thus it is surprising to see that network analysis of financial markets is based on linear correlation and its derivatives. An analysis based on partial correlation is of particular interest as it leads to the vicinity of causality detection in time series analysis. In this paper we generalise the planar maximally filtered graphs and partial correlation planar graphs to incorporate nonlinearity using partial mutual information.
Path dependence is a key feature of complex economic systems. It implies that history matters in the long-term evolution of markets and economies. Path dependence can be viewed as the dynamic version of positive feedback effects. This paper focuses on the nonlinear neoclassical economic growth model with the Cobb-Douglas production function, which accounts for problems related to pollutant emissions. It was found that only selected initial forms have a chance to develop. Present states depend on past states, even though the historical circumstances that had affected the past states may no longer be relevant. The choice among different histories may be a stochastic process. The understanding of economic growth suggested in this study stands in opposition to the neoclassical tradition based on equilibrium states or paths independent of the system's history. In contemporary economics, the idea of path dependence is most often used in studies on the high-tech industry, where the researchers are focused on such phenomena as innovation processes, monopolization, or the causes of ineffective technical solutions. The analysis of historical conditions is almost entirely carried out with the use of qualitative methods, since the subject of the research is non-formalized. In addition, the theoretical basis for conducting relevant empirical research is still missing. As a result of the development of complexity economics in recent years, numerous dynamic features of complex economic systems can be examined with the application of quantitative methods which, in effect, strengthens the bonds between theory and practice. Rare exceptions include path dependence relations. The aim of this article is to fill this gap and to create a theoretical basis for quantitative research on historical conditions in economics. This is a necessary condition for undertaking empirical research. The theoretical search started with the Keynesian model of the Samuelson-Hicks trade cycle, to demonstrate that conventional economics completely omits the most interesting path-dependence cases. It turns out that only the neoclassical model of economic growth, taking into account two power laws, provides appropriate dynamic characteristics for a full description of path dependence relations. Therefore, appropriate theoretical bases can be provided only by complexity economics. It may seem that, in this work, the dependence on history is restricted to two successive time steps in the case of the Samuelson-Hicks model and a single step in the neoclassical model of economic growth by Day. However, it examines an ordered path dependence, where events are chronologically ordered and the impact of earlier events on the later ones occurs through intermediary events. It should be remembered that events are constantly affected by environmental stimuli that are reflected not only in initial conditions, but also in the values of the parameters for all periods. Thus, it is not a case of short-term memory.
We extend the well-known Cont-Bouchaud model to include a hierarchical topology of agent's interactions. The influence of hierarchy on system dynamics is investigated by two models. The first one is based on a multi-level, nested Erdős-Rényi random graph and individual decisions by agents according to Potts dynamics. This approach does not lead to a broad return distribution outside a parameter regime close to the original Cont-Bouchaud model. In the second model we introduce a limited hierarchical Erdős-Rényi graph, where merging of clusters at a level h+1 involves only clusters that have merged at the previous level h and we use the original Cont-Bouchaud agent dynamics on resulting clusters. The second model leads to a heavy-tail distribution of cluster sizes and relative price changes in a wide range of connection densities, not only close to the percolation threshold.
In this paper, we investigate the statistical features of the weighted international-trade network. By finding the maximum weight spanning trees for this network we make the extraction of the truly relevant connections forming the network's backbone. We discuss the role of large-sized countries (strongest economies) in the tree. Finally, we compare the topological properties of this backbone to the maximum weight spanning trees obtained from the gravity model of trade. We show that the model correctly reproduces the backbone of the real-world economy.
We investigate the presence of multifractal residual background effect for monofractal signals which appears due to the finite length of the signals and (or) due to the constant long memory the signals reveal. This phenomenon is investigated numerically within the multifractal detrended fluctuation analysis (MF-DFA) for artificially generated time series. Next, the analytical formulas enabling to describe the multifractal content in such signals are provided. Final results are shown in the frequently used generalized Hurst exponent h(q) multifractal scenario as a function of time series length L and the autocorrelation scaling exponent value γ. The obtained results may be significant in any practical application of multifractality, including financial data analysis, because the "true" multifractal effect should be clearly separated from the so called "multifractal noise" resulting from the finite data length. Examples from finance in this context are given. The provided formulas may help to decide whether one deals with the signal of real multifractal origin or not and make further step in analysis of the so called spurious or corrupted multifractality discussed in literature.
The model of community isolation was extended to the case when individuals are randomly placed at the nodes of hierarchical modular networks. It was shown that the average number of blocked nodes (individuals) increases in time as a power function, with the exponent depending on the network parameters. The distribution of the time when the first isolated cluster appears is unimodal, non-gaussian. The developed analytical approach is in a good agreement with the simulation data.
In most sociophysical simulations on public opinion, only two opinions are allowed: Pro and Contra. However, in all political elections many people do not vote. Here we analyse two models of dynamics of public opinion, taking into account Indifferent voters: (i) the Sznajd model with symmetry Pro-Contra, (ii) the outflow one move voter model, where Contra's are converted to Indifferent by their Pro neighbours. Our results on the Sznajd model are in an overall agreement with the results of the mean field approach and with those known from the initial model formulation. The simulation on the voter model shows that an amount of Contra's who remain after convertion depends on the network topology.
The Sznajd model is investigated in the directed ErdH os-Rényi network with the clusterization coefficient enhanced to 0.3 by the method of Holme and Kim. Within additional triangles, all six links are present. In this network, some nodes preserve the minority opinion. The time τof getting equilibrium is found to follow the log-normal distribution and it increases linearly with the system size. Its dependence on the initial opinion distribution is different from the analytical results for fully connected networks.
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