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éToile Platform

éToile Platform | Sistemas complejos | Scoop.it

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 CS-Digital Campus.


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António F Fonseca's curator insight, January 22, 2014 4:47 AM

Portugal is on the first front in Complex Systems Studies.

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Complex systems: Ecology for bankers : Article : Nature

Complex systems: Ecology for bankers : Article : Nature | Sistemas complejos | Scoop.it

There is common ground in analysing financial systems and ecosystems, especially in the need to identify conditions that dispose a system to be knocked from seeming stability into another, less happy state.


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Chaos Forgets and Remembers: Measuring Information Creation, Destruction, and Storage

The hallmark of deterministic chaos is that it creates information—the rate being given by the Kolmogorov-Sinai metric entropy. Since its introduction half a century ago, the metric entropy has been used as a unitary quantity to measure a system’s intrinsic unpredictability. Here, we show that it naturally decomposes into two structurally meaningful components: A portion of the created information—the ephemeral information—is forgotten and a portion—the bound information—is remembered. The bound information is a new kind of intrinsic computation that differs fundamen- tally from information creation: it measures the rate of active information storage. We show that it can be directly and accurately calculated via symbolic dynamics, revealing a hitherto unknown richness in how dynamical systems compute.

 

Chaos Forgets and Remembers: Measuring Information Creation, Destruction, and Storage
Ryan G. James, Korana Burke, James P. Crutchfield

http://www.santafe.edu/research/working-papers/abstract/a0504be522643a0cc27e85bb3bf074e5/


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Modeling complex systems with adaptive networks

Modeling complex systems with adaptive networks | Sistemas complejos | Scoop.it

Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve. Many real-world complex systems can be modeled as adaptive networks, including social networks, transportation networks, neural networks and biological networks. In this paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, non-comprehensive review of recent literature on mathematical/computational modeling and analysis of such networks. We also report our recent work on several applications of computational adaptive network modeling and analysis to real-world problems, including temporal development of search and rescue operational networks, automated rule discovery from empirical network evolution data, and cultural integration in corporate merger.

 

Modeling complex systems with adaptive networks
Hiroki Sayama, , , Irene Pestov, Jeffrey Schmidt, Benjamin James Bush, Chun Wong, Junichi Yamanoi, Thilo Gross

Computers & Mathematics with Applications

In Press, Corrected Proof

http://dx.doi.org/10.1016/j.camwa.2012.12.005


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Social Factors in Epidemiology

Despite the invention of control measures like vaccines, infectious diseases remain part of human existence. Ideas, sentiments, or information can also be contagious. Such social contagion is akin to biological contagion: Both spread through a replication process that is blind to the consequences for the individual or population, and if each person transmits to more than one person, the explosive power of exponential growth creates an epidemic. Social contagions may cause irrational “fever.” Isaac Newton, having lost £20,000 in the speculative South Sea Bubble, commented that he could “calculate the movement of the stars, but not the madness of men”. Systems in which both contagion types are coupled to one another—an infectious disease spreading by biological contagion and a social contagion concerning the disease—offer unique scientific challenges and are increasingly important for public health.

 

Social Factors in Epidemiology

Chris T. Bauch, Alison P. Galvani

Science 4 October 2013:
Vol. 342 no. 6154 pp. 47-49
http://dx.doi.org/10.1126/science.1244492


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Arjen ten Have's curator insight, October 8, 2013 3:04 PM

likely a better model of how ideas spread thru society than memes as genes

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A genetic map for complex diseases

A genetic map for complex diseases | Sistemas complejos | Scoop.it

University of Chicago scientists have created one of the most expansive analyses to date of the genetic factors at play in complex diseases such as autism and heart disease by using diseases with known genetic causes to guide them.

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Nonlinear-map model for the control of an airplane schedule

We study the dynamics and control of an airplane in air transportation between two airports. The dynamic models are presented for the airplane schedule. The dynamics of an airplane is described by the piecewise map and delayed map models. The characteristics of the nonlinear maps are studied analytically and numerically. The airplane displays the complex motion in the unstable region. The motion of the airplane depends on the quantity of goods, the control method, and the delay. It is shown that the control delay has an important effect on the motion.

 

Nonlinear-map model for the control of an airplane schedule
Takashi Nagatani

Physica A

http://dx.doi.org/10.1016/j.physa.2013.08.076


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Física de las reuniones sociales

Física de las reuniones sociales | Sistemas complejos | Scoop.it
Un modelo muy sencillo logra predecir los patrones de movilidad de las personas que toman parte en un evento colectivo.
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Rescooped by Complejidady Economía from Complex Networks Everywhere
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Generating functionals for guided self-organization

Time evolution equations for dynamical systems can often be derived from generating functionals. Examples are Newton's equations of motion in classical dynamics which can be generated within the Lagrange or the Hamiltonian formalism. We propose that generating functionals for self-organizing complex systems offer several advantages. Generating functionals allow to formulate complex dynamical systems systematically and the results obtained are typically valid for classes of complex systems, as defined by the type of their respective generating functionals. The generated dynamical systems tend, in addition, to be minimal, containing only few free and undetermined parameters. We point out that two or more generating functionals may be used to define a complex system and that multiple generating function may not, and should not, be combined into a single overall objective function. We provide and discuss examples in terms of adapting neural networks.


Via Ashish Umre, Alejandro J. Alvarez S.
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Complex Adaptive Systems Modeling: A multidisciplinary Roadmap

I would like to start by extending a very warm welcome from the editorial board of Complex Adaptive Systems Modeling (CASM) journal, a new and exciting SpringerOpen/BioMed Central project. This article is the opening editorial and a call for papers for CASM. It also provides a gentle introduction to CASM as well as serves as a concise multidisciplinary roadmap and survey for CASM readership. The primary goal is to assist multidisciplinary researchers with an interest in modeling complex systems in finding more about relevant helpful resources and the numerous exciting ways in which other colleague researchers use modeling approaches in their respective disciplines. Finally I also provide advice for prospective authors regarding the preparation of manuscripts in a style appropriate to the highly multidisciplinary CASM readership. It is hoped that this will help seed fruitful cross‐fertilization of ideas transgressing disciplinary boundaries.

 

Complex Adaptive Systems Modeling: A multidisciplinary Roadmap
Muaz A Niazi

Complex Adaptive Systems Modeling 2013, 1:1 http://dx.doi.org/10.1186/2194-3206-1-1


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Sistemas Complejos como instrumentos de conocimiento y transformación del mundo

Sistemas Complejos como instrumentos de conocimiento y transformación del mundo | Sistemas complejos | Scoop.it
Complejidady Economía's insight:

Miramontes, O
"Los sistemas complejos como instrumentos de conocimiento y transformación del mundo".
En Ramírez, S. (editor), Perspectivas sobre la teoría de sistemas. Centro de Investigaciones Interdisciplinarias en Ciencias y Humanidades,
UNAM-Siglo XXI, México (1999)

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Keynote - Edgar Morin - Complex thinking for a complex world

Our world is at crisis. Global challenges abound. However, they have a "dark" and a "bright" side. The dark side is the imminent danger of the breakdown of interdependent societies with the perspective of extermination of civilised human life. The bright side marks a possible entrance to a new stage of evolution of humanity, to the self-organisation of a humane world society. Cybernetics, systems research, the sciences of complexity -- all of them have the potential to endow the subjects of history with guidance and a means for mastering the current transformation.


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Complexity map - Brian Castellani - Dec. 5 update

Complexity map - Brian Castellani - Dec. 5 update | Sistemas complejos | Scoop.it

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Barabási : The Hidden #Pattern Behind Everything We Do I #datascience #SNA


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ukituki's curator insight, November 24, 2013 8:35 AM

Barabasi argues that, rather than being random,
humans actually act in predictable patterns. We go along for long
periods of quiet routine followed suddenly by loud bursts of activity.
Barabasi demonstrates that these breaks in routine, or "bursts," are
present in all aspects of our existence— in the way we write emails,
spend our money, manage our health, form ideas. Barabasi has even
found "burstiness" in our webpage clicking activity and the online
news cycle."

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Multidisciplinary applications of complex networks modeling, simulation, visualization, and analysis

(...) complex systems are characterized by the interactions between their numerous elements. The word ‘complex’ comes from the Latin plexus which means entwined. In other words, it is difficult to correlate global properties of complex systems with the properties of the individual constituent components. This is primarily because the interactions between these individual elements partly determine the future states of the system (Gershenson 2013). If these interactions are not included in the developed models, the models would not be an accurate reflection of the modelled phenomenon.

 

Gershenson, C. & M. A. Niazi (2013). Multidisciplinary applications of complex networks modeling, simulation, visualization, and analysis. Complex Adaptive Systems Modeling 1:17  http://dx.doi.org/10.1186/2194-3206-1-17


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The Math of Segregation » American Scientist

The Math of Segregation » American Scientist | Sistemas complejos | Scoop.it

In the 1960s Schelling devised a simple model in which a mixed group of people spontaneously segregates by race even though no one in the population desires that outcome. Initially, black and white families are randomly distributed. At each step in the modeling process the families examine their immediate neighborhood and either stay put or move elsewhere depending on whether the local racial composition suits their preferences. The procedure is repeated until everyone finds a satisfactory home (or until the simulator’s patience is exhausted).


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Exactly scale-free scale-free networks

There is mounting evidence of the apparent ubiquity of scale-free networks among complex systems. Many natural and physical systems exhibit patterns of interconnection that conform, approximately, to the structure expected of a scale-free network. We propose an efficient algorithm to generate representative samples from the space of all networks defined by a particular (scale-free) degree distribution. Using this algorithm we are able to systematically explore that space with some surprising results: in particular, we find that preferential attachment growth models do not yield typical realizations and that there is a certain latent structure among such networks --- which we loosely term "hub-centric". We provide a method to generate or remove this latent hub-centric bias --- thereby demonstrating exactly which features of preferential attachment networks are atypical of the broader class of scale free networks. Based on these results we are also able to statistically determine whether experimentally observed networks are really typical realizations of a given degree distribution (scale-free degree being the example which we explore). In so doing we propose a surrogate generation method for complex networks, exactly analogous the the widely used surrogate tests of nonlinear time series analysis.


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Exact Complexity: The Spectral Decomposition of Intrinsic Computation

We give exact formulae for a wide family of complexity measures that capture the organization of hidden nonlinear processes. The spectral decomposition of operator-valued functions leads to closed-form expressions involving the full eigenvalue spectrum of the mixed-state presentation of a process's epsilon-machine causal-state dynamic. Measures include correlation functions, power spectra, past-future mutual information, transient and synchronization informations, and many others. As a result, a direct and complete analysis of intrinsic computation is now available for the temporal organization of finitary hidden Markov models and nonlinear dynamical systems with generating partitions and for the spatial organization in one-dimensional systems, including spin systems, cellular automata, and complex materials via chaotic crystallography.

 

Exact Complexity: The Spectral Decomposition of Intrinsic Computation
James P. Crutchfield, Christopher J. Ellison, Paul M. Riechers

http://arxiv.org/abs/1309.3792


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Rescooped by Complejidady Economía from Complex World
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Most Influential Emotions On Social Networks Revealed

Most Influential Emotions On Social Networks Revealed | Sistemas complejos | Scoop.it
Anger spreads faster and more broadly than joy, say computer scientists who have analysed sentiment on the Chinese Twitter-like service Weibo

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[1301.3698] Modeling human dynamics of face-to-face interaction networks

Complejidady Economía's insight:

Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of inter-conversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents which perform a random walk in a two dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.

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Researchers develop new method for understanding network connections - MIT News Office

Researchers develop new method for understanding network connections - MIT News Office | Sistemas complejos | Scoop.it
Technique could be applied to the study of disease, social networks and other diverse fields.
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¿Qué es complejidad? - Dr. Rafael Ángel Barrio

La mirada holística implica abordar el conocimiento como un proceso que es a la vez biológico, cultural, social e histórico. El Dr. Rafael Ángel Barrio obtuv...
Complejidady Economía's insight:

Excelente entrevista disponible en  Youtube por Felipe Flores

 

Felipe Flores

Vídeos científicos, culturales y recreativos propios o de terceras personas.

 twitter.com/#!/FelipeFloresMX facebook.com/profile.php?id=519526219 
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Una aproximación a los sistemas complejos. Revista Ciencias (UNAM) número 59 julio-septiembre 2000

Una aproximación a los sistemas complejos.                                              Revista Ciencias (UNAM) número 59 julio-septiembre 2000 | Sistemas complejos | Scoop.it
Revista ciencias de la Facultad de Ciencias de la Universidad Nacional Autónoma de México

 

Los llamados sistemas complejos proporcionan modelos analogías y metáforas capaces de llevar por nuevos senderos a la investigación en las ciencias físicas, biológicas, atmosféricas, económicas, sociales, lingüísticas y otras más. (editorial)

 

Recomendación:

Una aproximación a los sistemas complejos

 

 

Gustavo Martínez Mekler

 

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