Double power laws, fractals and self-similarity

Power law (PL) distributions have been largely reported in the modeling of distinct real phenomena and have been associated with fractal structures and self-similar systems. In this paper, we analyze real data that follows a PL and a double PL behavior and verify the relation between the PL coefficient and the capacity dimension of known fractals. It is to be proved a method that translates PLs coefficients into capacity dimension of fractals of any real data.

Double power laws, fractals and self-similarity

Power law (PL) distributions have been largely reported in the modeling of distinct real phenomena and have been associated with fractal structures and self-similar systems. In this paper, we analyze real data that follows a PL and a double PL behavior and verify the relation between the PL coefficient and the capacity dimension of known fractals. It is to be proved a method that translates PLs coefficients into capacity dimension of fractals of any real data.

Can power laws help us understand gene and proteome information?

Proteins are biochemical entities consisting of one or more blocks typically folded in a 3D pattern. Each block (a polypeptide) is a single linear sequence of amino acids that are biochemically bonded together. The amino acid sequence in a protein is defined by the sequence of a gene or several genes encoded in the DNA-based genetic code. This genetic code typically uses twenty amino acids, but in certain organisms the genetic code can also include two other amino acids. After linking the amino acids during protein synthesis, each amino acid becomes a residue in a protein, which is then chemically modified, ultimately changing and defining the protein function. In this study, the authors analyze the amino acid sequence using alignment-free methods, aiming to identify structural patterns in sets of proteins and in the proteome, without any other previous assumptions. The paper starts by analyzing amino acid sequence data by means of histograms using fixed length amino acid words (tuples). After creating the initial relative frequency histograms, they are transformed and processed in order to generate quantitative results for information extraction and graphical visualization. Selected samples from two reference datasets are used, and results reveal that the proposed method is able to generate relevant outputs in accordance with current scientific knowledge in domains like protein sequence/proteome analysis.

A review of power laws in real life phenomena

Power law distributions, also known as heavy tail distributions, model distinct real life phenomena in the areas of biology, demography, computer science, economics, information theory, language, and astronomy, amongst others. In this paper, it is presented a review of the literature having in mind applications and possible explanations for the use of power laws in real phenomena. We also unravel some controversies around power laws.

Power laws and scaling of rain events and dry spells in the Catalonia region

We analyze the statistics of rain-event sizes, rain-event durations, and dry-spell durations in a network of 20 rain gauges scattered in an area situated close to the NW Mediterranean coast. Power-law distributions emerge clearly for the dryspell durations, with an exponent around 1.50 ± 0.05, although for event sizes and durations the power-law ranges are rather limited, in some cases. Deviations from power-law behavior are attributed to finite-size effects. A scaling analysis helps to elucidate the situation, providing support for the existence of scale invariance in these distributions. It is remarkable that rain data of not very high resolution yield findings in agreement with self-organized critical phenomena.

Power laws, Discontinuities and Regional City Size Distributions

Urban systems are manifestations of human adaptation to the natural environment. City size distributions are the expression of hierarchical processes acting upon urban systems. In this paper, we test the entire city size distributions for the southeastern and southwestern United States (1990), as well as the size classes in these regions for power law behavior. We interpret the differences in the size of the regional city size distributions as the manifestation of variable growth dynamics dependent upon city size. Size classics in the city size distributions are snapshots of stable states within urban systems in flux.

A Lei de ZIPF ou Regra 80/20 da Internet

As Leis de Potência, LP, (Power Laws, em inglês), Leis de Pareto ou Leis de Zipf são distribuições estatísticas, com inúmeras aplicações práticas, em sistemas naturais e artificiais. Alguns exemplos são a variação dos rendimentos pessoais ou de empresas, a ocorrência de palavras em textos, as repetições de sons ou conjuntos de sons em composições musicais, o número de vítimas em guerras ou outros cataclismos, a magnitude de tremores de terra, o número de vendas de livros ou CD’s na internet, o número de sítios mais acedidos na Internet, entre muitos outros. Vilfredo Pareto (1897-1906) afirma, no manual de economia política “Cours d’Economie Politique”, que grande parte da economia mundial segue uma determinada distribuição, em que 20% da população reúne 80% da riqueza total do país, estando, assim uma pequena fração da sociedade a controlar a maior fatia do dinheiro. Isto resume o comportamento de uma variável que segue uma distribuição de Pareto (ou Lei de Potência). Neste trabalho pretende-se estudar em pormenor a aplicação das leis de potência a fenómenos da internet, como sendo o número de sítios mais visitados, o número de links existentes em determinado sítio, a distribuição de nós numa rede da internet, o número livros vendidos e as vendas em leilões online. Os resultados obtidos permitem-nos concluir que todos os dados estudados são bem aproximados, numa escala logarítmica, por uma reta com declive negativo, seguindo, assim, uma distribuição de Pareto. O desenvolvimento e crescimento da Web, tem proporcionado um aumento do número dos utilizadores, conteúdos e dos sítios. Grande parte dos exemplos presentes neste trabalho serão alvo de novos estudos e de novas conclusões. O fato da internet ter um papel preponderante nas sociedades modernas, faz com que esteja em constante evolução e cada vez mais seja possível apresentar fenómenos na internet associados Lei de Potência.

Leis de Pareto para Guerras e Actos de Terrorismo

Mestrado em Engenharia Eletrotécnica e de Computadores - Área de Especialização de Sistemas e Planeamento Industrial

Leis de potência em economia e finanças

Nesta dissertação aborda-se a aplicação de Leis de Potência (LPs), também designadas de Leis de Pareto ou Leis de Zipf, a dados económicos. As LPs são distribuições estatísticas amplamente usadas na compreensão de sistemas naturais e artificiais. O aparecimento das LPs deve-se a Vilfredo Pareto que, no século XIX, publicou o manual de economia política,“Cours d’Economie Politique”. Nesse manual refere que grande parte da economia mundial segue uma LP, em que 20% da população reúne 80% da riqueza do país. Esta propriedade carateriza uma variável que segue uma distribuição de Pareto (ou LP). Desde então, as LPs foram aplicadas a outros fenómenos, nomeadamente a ocorrência de palavras em textos, os sobrenomes das pessoas, a variação dos rendimentos pessoais ou de empresas, o número de vítimas de inundações ou tremores de terra, os acessos a sítios da internet, etc. Neste trabalho, é estudado um conjunto de dados relativos às fortunas particulares ou coletivas de pessoas ou organizações. Mais concretamente são analisados dados recolhidos sobre as fortunas das mulheres mais ricas do mundo, dos homens mais ricos no ramo da tecnologia, das famílias mais ricas, das 20 mulheres mais ricas da América, dos 400 homens mais ricos da América, dos homens mais ricos do mundo, dos estabelecimentos mais ricos do mundo, das empresas mais ricas do mundo e dos países mais ricos do mundo, bem como o valor de algumas empresas no mercado de ações. Os resultados obtidos revelam uma boa aproximação de parte desses dados a uma LP simples e uma boa aproximação pelos restantes dados a uma LP dupla. Observa-se, assim, diferenciação na forma de crescimento das fortunas nos diferentes casos estudados. Como trabalho futuro, procurar-se-á analisar estes e outros dados, utilizando outras distribuições estatísticas, como a exponencial ou a lognormal, que possuem comportamentos semelhantes à LP, com o intuito de serem comparados os resultados. Um outro aspeto interessante será o de encontrar a explicação analítica para as vantagens da aproximação de dados económicos por uma LP simples vs por uma LP dupla.

Power Law Behavior and Self-similarity in Modern Industrial Accidents

Advances in technology have produced more and more intricate industrial systems, such as nuclear power plants, chemical centers and petroleum platforms. Such complex plants exhibit multiple interactions among smaller units and human operators, rising potentially disastrous failure, which can propagate across subsystem boundaries. This paper analyzes industrial accident data-series in the perspective of statistical physics and dynamical systems. Global data is collected from the Emergency Events Database (EM-DAT) during the time period from year 1903 up to 2012. The statistical distributions of the number of fatalities caused by industrial accidents reveal Power Law (PL) behavior. We analyze the evolution of the PL parameters over time and observe a remarkable increment in the PL exponent during the last years. PL behavior allows prediction by extrapolation over a wide range of scales. In a complementary line of thought, we compare the data using appropriate indices and use different visualization techniques to correlate and to extract relationships among industrial accident events. This study contributes to better understand the complexity of modern industrial accidents and their ruling principles.

Power law and entropy analysis of catastrophic phenomena

Catastrophic events, such as wars and terrorist attacks, tornadoes and hurricanes, earthquakes, tsunamis, floods and landslides, are always accompanied by a large number of casualties. The size distribution of these casualties has separately been shown to follow approximate power law (PL) distributions. In this paper, we analyze the statistical distributions of the number of victims of catastrophic phenomena, in particular, terrorism, and find double PL behavior. This means that the data sets are better approximated by two PLs instead of a single one. We plot the PL parameters, corresponding to several events, and observe an interesting pattern in the charts, where the lines that connect each pair of points defining the double PLs are almost parallel to each other. A complementary data analysis is performed by means of the computation of the entropy. The results reveal relationships hidden in the data that may trigger a future comprehensive explanation of this type of phenomena.

Power law analysis of financial index dynamics

Power law PL and fractional calculus are two faces of phenomena with long memory behavior. This paper applies PL description to analyze different periods of the business cycle. With such purpose the evolution of ten important stock market indices DAX, Dow Jones, NASDAQ, Nikkei, NYSE, S&P500, SSEC, HSI, TWII, and BSE over time is studied. An evolutionary algorithm is used for the fitting of the PL parameters. It is observed that the PL curve fitting constitutes a good tool for revealing the signal main characteristics leading to the emergence of the global financial dynamic evolution.

Distribuições de eventos extremos e telecomunicações

As distribuições de Lei de Potência (PL Power Laws), tais como a lei de Pareto e a lei de Zipf são distribuições estatísticas cujos tamanhos dos eventos são inversamente proporcionais à sua frequência. Estas leis de potência são caracterizadas pelas suas longas caudas. Segundo Vilfredo Pareto (1896), engenheiro, cientista, sociólogo e economista italiano, autor da Lei de Pareto, 80% das consequências advêm de 20% das causas. Segundo o mesmo, grande parte da economia mundial segue uma determinada distribuição, onde 80% da riqueza mundial é detida por 20% da população ou 80% da poluição mundial é feita por 20% dos países. Estas percentagens podem oscilar nos intervalos [75-85] e [15-25]. A mesma percentagem poderá ser aplicada à gestão de tempo, onde apenas 20% do tempo dedicado a determinado assunto produzirá cerca de 80% dos resultados obtidos. A lei de Pareto, também designada de regra 80/20, tem aplicações nas várias ciências e no mundo físico, nomeadamente na biodiversidade. O número de ocorrências de fenómenos extremos, aliados ao impacto nas redes de telecomunicações nas situações de catástrofe, no apoio imediato às populações e numa fase posterior de reconstrução, têm preocupado cada vez mais as autoridades oficiais de protecção civil e as operadoras de telecomunicações. O objectivo é o de preparar e adaptarem as suas estruturas para proporcionar uma resposta eficaz a estes episódios. Neste trabalho estuda-se o comportamento de vários fenómenos extremos (eventos críticos) e aproximam-se os dados por uma distribuição de Pareto (Lei de Pareto) ou lei de potência. No final, especula-se sobre a influência dos eventos críticos na utilização das redes móveis. É fundamental que as redes móveis estejam preparadas para lidar com as repercussões de fenómenos deste tipo.

A Review on the Characterization of Signals and Systems by Power Law Distributions

Power laws, also known as Pareto-like laws or Zipf-like laws, are commonly used to explain a variety of real world distinct phenomena, often described merely by the produced signals. In this paper, we study twelve cases, namely worldwide technological accidents, the annual revenue of America׳s largest private companies, the number of inhabitants in America׳s largest cities, the magnitude of earthquakes with minimum moment magnitude equal to 4, the total burned area in forest fires occurred in Portugal, the net worth of the richer people in America, the frequency of occurrence of words in the novel Ulysses, by James Joyce, the total number of deaths in worldwide terrorist attacks, the number of linking root domains of the top internet domains, the number of linking root domains of the top internet pages, the total number of human victims of tornadoes occurred in the U.S., and the number of inhabitants in the 60 most populated countries. The results demonstrate the emergence of statistical characteristics, very close to a power law behavior. Furthermore, the parametric characterization reveals complex relationships present at higher level of description.

Mapping power law distributions in digital health social networks: methods, interpretations, and practical implications

BACKGROUND: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. OBJECTIVES: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. METHODS: Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. RESULTS: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). CONCLUSIONS: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.