The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: "For to all those who have, more will be given." Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew "rich get richer" effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his or her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience. T he rate of individual progress is fundamental to career development and success. In practice, the rate of progress depends on many factors, such as an individual's talent, productivity, reputation, as well as other external random factors. Using a stochastic model, here we find that the relatively small rate of progress at the beginning of the career plays a crucial role in the evolution of the career length. Our quantitative model describes career progression using two fundamental ingredients: (i) random forward progress "up the career ladder" and (ii) random stopping times, terminating a career. This model quantifies the "Matthew effect" by incorporating into ingredient (i) the common cumulative advantage property (1-8) that it is easier to move forward in the career the further along one is in the career. A direct result of the increasing progress rate with career position is the large disparity between the numbers of careers that are successful long tenures and the numbers of careers that are unsuccessful short stints.Surprisingly, despite the large differences in the numbers of long and short careers, we find a scaling law that bridges the gap between the frequent short and the infrequent long careers. We test this model for both scientific and sports careers, two careers where accomplishments are methodically recorded. We analyze publication careers within six high-impact journals: Nature, Science, the Proceedings of the National Academy of Science (PNAS), Physical Review Letters (PRL), New England Journal of Medicine (NEJM), and CELL. We also analyze sports careers within ...
Abstract. -Using transfer entropy, we observed the strength and direction of information flow between stock indices. We uncovered that the biggest source of information flow is America. In contrast, the Asia/Pacific region the biggest is receives the most information. According to the minimum spanning tree, the GSPC is located at the focal point of the information source for world stock markets.Introduction. -Economic systems have recently become an active field of research for physicists striving to transfer concepts and methodologies from statistical physics such as phase transition, fractal theory, spin models, complex networks, and information theory to the analysis of economic problems .Among the numerous methodologies put forward, time series analysis has proven to be one of the most efficient methods and is widely applied to the examination of characteristics of stock and foreign exchange markets. In order to analyze financial time series, a range of statistical measures have been introduced, including probability distribution [20][21][22][23][24][25][26] Information is a keyword in analyzing financial market data or in estimating the stock price of a given company. It is quantified through a variety of methods such as crosscorrelation, autocorrelation, and complexity. However, while they may be appropriate measures for the observation of the internal structure of information flow, they fail to illuminate the directionality of information flow. Schreiber [28] introduced transfer entropy, which measures the dependency in time between two variables and notes the directionality of information flow. This concept of transfer entropy has already been applied to the analysis of financial time series. Marschinski and Kantz [29] calculated information flow between the Dow Jones and DAX stock indexes to better observe interactions between the two huge markets. Kwon and Yang [11] measured the di-
Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of a network of interacting macroeconomic agents. In this regard, from the perspective of collective network dynamics, here we explore how the topology of the global macroeconomic network affects the patterns of spreading of economic crises. Using a simple toy model of crisis spreading, we demonstrate that an individual country's role in crisis spreading is not only dependent on its gross macroeconomic capacities, but also on its local and global connectivity profile in the context of the world economic network. We find that on one hand clustering of weak links at the regional scale can significantly aggravate the spread of crises, but on the other hand the current network structure at the global scale harbors higher tolerance of extreme crises compared to more “globalized” random networks. These results suggest that there can be a potential hidden cost in the ongoing globalization movement towards establishing less-constrained, trans-regional economic links between countries, by increasing vulnerability of the global economic system to extreme crises.
We study the critical properties of the majority voter model on d -dimensional hypercubic lattices. In two dimensions, the majority voter model belongs to the same universality class as that of the Ising model. However, the critical behaviors of the majority voter model on four dimensions do not exhibit mean-field behavior. Using the Monte Carlo simulation on d -dimensional hypercubic lattices, we obtain the critical exponents up to d=7 , and find that the upper critical dimension is 6 for the majority voter model. We also confirm our results using mean-field calculation.
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the index of the market.Comment: 8 pages, 4 figure
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