Your new post is loading...
Your new post is loading...
We perform an extensive numerical study of the effects of clustering on the structural properties of complex networks. We observe that strong clustering in heterogeneous networks induces the emergence of a coreperiphery organization that has a critical effect on their percolation properties. In such situation, we observe a novel double phase transition, with an intermediate phase where only the core of the network is percolated, and a final phase where the periphery percolates regardless of the core. Interestingly, strong clustering makes simultaneously the core more robust and the periphery more fragile. These phenomena are also found in real complex networks.
This video provides a basic introduction to the science of complex systems, focusing on patterns in nature. (For more information on agentbased modeling, vi...
Via Lorien Pratt
This video shows six laboratory demonstrations of chaos andnonlinear phenomena, intended for use in a first course on nonlineardynamics. Steven Strogatz explains the principles being illustrated andwhy they are important.
This paper reports theoretical and experimental studies on spatiotemporal dynamics in the choruses of male Japanese tree frogs. First, we theoretically model their calling times and positions as a system of coupled mobile oscillators.
An MIT physicist has proposed the provocative idea that life exists because the law of increasing entropy drives matter to acquire lifelike physical properties.
Despite growing interest in quantifying and modeling the scoring dynamics within professional sports games, relative little is known about what patterns or principles, if any, cut across different sports. Using a comprehensive data set of scoring events in nearly a dozen consecutive seasons of college and professional (American) football, professional hockey, and professional basketball, we identify several common patterns in scoring dynamics. Across these sports, scoring tempowhen scoring events occurclosely follows a common Poisson process, with a sportspecific rate. Similarly, scoring balancehow often a team wins an eventfollows a common Bernoulli process, with a parameter that effectively varies with the size of the lead. Combining these processes within a generative model of gameplay, we find they both reproduce the observed dynamics in all four sports and accurately predict game outcomes. These results demonstrate common dynamical patterns underlying withingame scoring dynamics across professional team sports, and suggest specific mechanisms for driving them. We close with a brief discussion of the implications of our results for several popular hypotheses about sports dynamics.
Andrew Anthony: The astonishing story of the British man who began a parttime psychology course in his 50s – and ended up taking on America's academic establishment
Based on a theoretical model for opinion spreading on a network, through avalanches, the effect of external field is now considered, by using methods from nonequilibrium statistical mechanics. The original part contains the implementation that the avalanche is only triggered when a local variable (a so called awareness) reaches and goes above a threshold. The dynamical rules are constrained to be as simple as possible, in order to sort out the basic features, though more elaborated variants are proposed. Several results are obtained for a Erd\"osR\'enyi network and interpreted through simple analytical laws, scale free or logistic maplike, i.e., (i) the sizes, durations, and number of avalanches, including the respective distributions, (ii) the number of times the external field is applied to one possible node before all nodes are found to be above the threshold, (iii) the number of nodes still below the threshold and the number of hot nodes (close to threshold) at each time step.
This course is anchored on the seven main sections associated with the key Economics areas where the complex systems studies approach to economy has been known to have important influence. These sections are: Section I: A Philosophical and Methodological approach to Economy using Complexity Sciences; Section II: The structure of interaction; Section III: Macroeconomics and Growth; Section IV: Financial Markets; Section V: International and Monetary Economy Dynamics; Section VI: Regional Economic Systems; Section VII: Evolutionary Economic Dynamics. Other than discussing the literature, the students will be invited to model, implement and discuss some of the underlying mentioned models using social simulation programming libraries.
Via Jorge Louçã, NESS
This course is anchored on the seven main sections associated with the key Economics areas where the complex systems studies approach to economy has been known to have important influence. These sections are: Section I: A Philosophical and Methodological approach to Economy using Complexity Sciences; Section II: The structure of interaction; Section III: Macroeconomics and Growth; Section IV: Financial Markets; Section V: International and Monetary Economy Dynamics; Section VI: Regional Economic Systems; Section VII: Evolutionary Economic Dynamics. Other than discussing the literature, the students will be invited to model, implement and discuss some of the underlying mentioned models using social simulation programming libraries.
Via Jorge Louçã
The software's designers talked with SourceForge about the challenges of simulating economic models, especially in turbulent times.
An important side effect of the evolution of the human brain is an increased capacity to form opinions in a very large domain of issues, which become points of aggressive interpersonal disputes. Remarkably, such disputes are often no less vigorous on small differences of opinion than large differences. Opinion differences that may be measured on the real number line may not directly correspond to the subjective importance of an issue and extent of resistance to opinion change. This is a hard problem for field of opinion dynamics, a field that has become increasingly prominent as it has attracted more contributions to it from investigators in the natural and engineering sciences. The paper contributes a scalefree approach to assessing the extents to which individuals, with unknown heterogeneous resistances to influence, have been influenced by the opinions of others.
The friendship paradox states that your friends have on average more friends than you have. Does the paradox "hold'" for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in largescale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks.

An extensive biobibliographical overview of the work of Edgar Morin, followed by a discussion of his "complex thought" in the context of the history of ideas.
Via Dina Gálvez
Animal behavior isn't complicated, but it is complex. Nicolas Perony studies how individual animals  be they Scottish Terriers, bats or meerkats  follow simple rules that, collectively, create larger patterns of behavior. And how this complexity born of simplicity can help them adapt to new circumstances, as they arise.
The book presents the versatility of cellular automaton as models for complex systems through the study of several problems using recent techniques  InTechOpen
Students who choose this option will complete an M2 (second year) master program in introduction to modelling methodology, as well as interdisciplinary courses and applications of complex networks.
Via Shaun Coffey
The structure pattern of the tree of life clues on the key ecological issues; hence knowing the fractal dimension is the fundamental question in understanding the tree of life. Yet the fractal dimension of the tree of life remains unclear since the scale of the tree of life has hypergrown in recent years. Here we show that the tree of life display a consistent powerlaw rules for inter and intrataxonomic levels, but the fractal dimensions were different among different kingdoms. The fractal dimension of hierarchical structure (Dr) is 0.873 for the entire tree of life, which smaller than the values of Dr for Animalia and Plantae but greater than the values of Dr for Fungi, Chromista, and Protozoa. The hierarchical fractal dimensions values for prokaryotic kingdoms are lower than for other kingdoms. The Dr value for Viruses was lower than most eukaryotic kingdoms, but greater than prokaryotes. The distribution of taxa size is governed by fractal diversity but skewed by overdominating taxa with large subtaxa size. The proportion of subtaxa in taxa with small and large sizes was greater than in taxa with intermediate size. Our results suggest that the distribution of subtaxa in taxa can be predicted with fractal dimension for the accumulating taxa abundance rather than the taxa abundance. Our study determined the fractal dimensions for inter and intrataxonomic levels of the present tree of life. These results emphases the need for further theoretical studies, as well as predictive modelling, to interpret the different fractal dimension for different taxonomic groups and skewness of taxa with large subtaxa size.
The “study of complexity” refers to the attempt to find common principles underlying the behavior of complex systems—systems in which large collections of components interact in nonlinear ways. Here, the term nonlinear implies that the system can’t be understood simply by understanding its individual components; nonlinear interactions cause the whole to be “more than the sum of its parts.” How Can the Study of Complexity Transform Our Understanding of the World? Melanie Mitchell https://www.bigquestionsonline.com/content/howcanstudycomplexitytransformourunderstandingworld
Via Complexity Digest
Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or “bound” together when people attend to a stimulus, perceive, think, and act. This is a dynamic, selfassembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales, suggesting that metastable dynamics underlie the realtime coordination necessary for the brain’s dynamic cognitive, behavioral, and social functions.
The éToile Platform is an open, interactive, new way of sharing educational resources for Master and PhD levels in Complexity Sciences domains. In different modules, students and researchers can: check their knowledge using the étoile evaluation tests;interact with other people studying the same subjects;use the éToile facilities for studying and researching on the Internet;contribute for an ecology of pedagogical resources;certificated their mastery of a core curriculum in Complexity Sciences;interact with a worldwide community of students and scientific researchers within the CSDigital Campus.
We explain how specific dynamical properties give rise to the limit distribution of sums of deterministic variables at the transition to chaos via the perioddoubling route. We study the sums of successive positions generatedby an ensemble of initial conditions uniformly distributed in the entire phase space of a unimodal map as represented by the logistic map. We find that these sums acquire their salient, multiscale, features from the repellor preimage structure that dominates the dynamics toward the attractors along the perioddoubling cascade. And we explain how these properties transmit from the sums to their distribution. Specifically, we show how the stationary distribution of sums of positions at the Feigebaum point is built up from those associated with the supercycle attractors forming a hierarchical structure with multifractal and discrete scale invariance properties.
In this paper, we empirically analyze the realworld human movements which are based on GPS records, and observe rich scaling properties in the temporalspatial patterns as well as an abnormal transition in the speeddisplacement patterns together with an evidence to the realworld traffic jams. In addition, we notice that the displacements at the population level show a significant positive correlation, indicating a cascadinglike nature in human movements. Furthermore, our analysis at the individual level finds that the displacement distributions of users with stronger correlations usually are closer to the power law, suggesting a correlation between the positive correlation of the displacement series and the form of an individual's displacement distribution.
IBM and http://IBMblr.Tumblr.com celebrate the life of Benoit B. Mandelbrot, IBM Fellow Emeritus and Fractal Pioneer. In this final interview shot by filmmak...
