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Complexity - Complex Systems Theory
Complex systems present problems both in mathematical modelling and philosophical foundations. The study of complex systems represents a new approach to science that investigates how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. The equations from which models of complex systems are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of natural systems that are considered fundamentally complex. wikipedia (en)
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visualcomplexity.com | Cellular Automata

visualcomplexity.com | Cellular Automata | Complexity - Complex Systems Theory | Scoop.it

Cellular automata (CA) are a much studied class of discrete dynamical network that support emergent behaviour resulting from homogeneous, local, short range interactions. They are applied in many overlapping areas; to model processes in physical, chemical and biological systems such as fluid dynamics and reaction-difusion; to study self-organization and self-reproduction by the emergence of coherent interacting structures; in mathematics and computation where the systems themselves are the focus of interest. CA dynamics are driven by complex feedback webs that are difficult to treat analytically except for special cases. Understanding these systems depends to a large extent on computer experiments, where a key notion is that state space is connected into basins of attraction.

 

 

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Embracing Complexity

Embracing Complexity | Complexity - Complex Systems Theory | Scoop.it

In his job as chief investment strategist at Legg Mason Capital Management, Michael J. Mauboussin has developed a healthy appreciation for complexity. Along the way—through his reports, books, teaching at Columbia Business School, and frequent conference appearances—he has become a leading exponent of how to navigate complex systems in financial markets and other aspects of life. In this edited conversation with HBR senior editor Tim Sullivan, Mauboussin talks about how his views on complexity feed into his daily practices and attitudes.

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Melanie Mitchell on "Using analogy to discover the meaning of images."

Melanie Mitchell on "Using analogy to discover the meaning of images."
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New England Complex Systems Institute

New England Complex Systems Institute | Complexity - Complex Systems Theory | Scoop.it

The New England Complex Systems Institute (NECSI) is an independent academic research and educational institution with students, postdoctoral fellows and faculty. In addition to the in-house research team, NECSI has co-faculty, students and affiliates from MIT, Harvard, Brandeis and other universities nationally and internationally.

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Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis

Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis | Complexity - Complex Systems Theory | Scoop.it

This chapter reviews measures of emergence, self-organization, complexity,homeostasis, and autopoiesis based on information theory. These measures arederived from proposed axioms and tested in two case studies: random Booleannetworks and an Arctic lake ecosystem.
Emergence is defined as the information a system or process produces.Self-organization is defined as the opposite of emergence, while complexity isdefined as the balance between emergence and self-organization. Homeostasisreflects the stability of a system. Autopoiesis is defined as the ratio betweenthe complexity of a system and the complexity of its environment. The proposedmeasures can be applied at different scales, which can be studied withmulti-scale profiles.

 

 

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Tony Smith's curator insight, August 2, 2013 6:56 PM

Regardless of any questions as to the degree information-theoretic metrics are useful and useable at the core of Emergence/Systems/Complexity theory, Fernandez, Maldonado & Gershenson's paper provides a valuable discussion of fundamental concepts across the field. The paper's key claim is that complexity is high not just when there is a balance between order and chaos, an old oversimplification, but also between emergence (in the whole of information not defined in the parts) and self-organisation, each of which is seen more widely as symptomatic of complexity. It is not an entirely unfamilar argument but one easily hidden in the field's Babel-like language.

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Complex systems analysis of series of blackouts: Cascading failure, critical points, and self-organization | Chaos

Complex systems analysis of series of blackouts: Cascading failure, critical points, and self-organization | Chaos | Complexity - Complex Systems Theory | Scoop.it
We give an overview of a complex systems approach to large blackouts of electric power transmission systems caused by cascading failure.
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Dynamical modeling and analysis of large cellular regulatory networks | Chaos

Dynamical modeling and analysis of large cellular regulatory networks | Chaos | Complexity - Complex Systems Theory | Scoop.it

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

 

 

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"Systems Thinking, Complexity Theory and Management" by David C. Aron, M.D., M.S.

"Systems Thinking, Complexity Theory and Management" by David C. Aron, M.D., M.S. | Complexity - Complex Systems Theory | Scoop.it

Make any device your moWeatherhead School of Management
Doctor of Management (DM) Program Alumni Residency Weekend
Presentation by David C. Aron, M.D., M.S.
Associate Chief of Staff/Education
Louis Stokes Cleveland Department of Veterans Affairs Medical Center
Presentation Title: "Systems Thinking, Complexity Theory and Management -- Panacea? Snake Oil? Or something in between?"
Recorded April 9, 2010 in The Peter B. Lewis Building at The Weatherhead School of Management on the campus of Case Western Reserve University.

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Complex Systems Models in the Social Science - UMich ICPSR Summer Program in Quantitative Methods

Complex Systems Models in the Social Science - UMich ICPSR Summer Program in Quantitative Methods | Complexity - Complex Systems Theory | Scoop.it

computational game theory and computational modeling, network science, natural language processing, randomness vs. determinism, diffusion, cascades, emergence, empirical approaches to study complexity (including measurement), social epidemiology, non-linear dynamics, etc.

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Detecting metastable states of dynamical systems by recurrence-based symbolic dynamics

Detecting metastable states of dynamical systems by recurrence-based symbolic dynamics | Complexity - Complex Systems Theory | Scoop.it
We propose an algorithm for the detection of recurrence domains of complex dynamical sys- tems from time series. Our approach exploits the characteristic checkerboard texture of recurrence domains exhibited in recurrence plots (RP). In phase space, RPs yield intersecting balls around sampling points that could be merged into cells of a phase space partition. We construct this parti- tion by a rewriting grammar applied to the symbolic dynamics of time indices. A maximum entropy principle defines the optimal size of intersecting balls. The final application to high-dimensional brain signals yields an optimal symbolic recurrence plot revealing functional components of the signal.
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Reading Ancient Minds: Metaphor, Culture, and Complexity - Santa Fe Institute

Scott Ortman, SFI Omidyar Fellow September 12, 2010


How much does culture influence human societies? The traditional view in many fields is that material forces -- land, climate, warfare -- trump less tangible human conceptualizations like currencies and laws. But Ortman argues that we don't really know, yet. Cognitive science suggests that conceptual metaphors are the building blocks of human systems, from governments to ideologies. He illustrates how new archaeological methods and linguistic analysis can reveal these metaphors and allow us to better understand the critical role culture plays in human history.

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Life as we know it

This paper presents a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any (ergodic) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if the coupling among an ensemble of dynamical systems is mediated by short-range forces, then the states of remote systems must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states in a statistical sense. The existence of a Markov blanket means that internal states will appear to minimize a free energy functional of the states of their Markov blanket. Crucially, this is the same quantity that is optimized in Bayesian inference. Therefore, the internal states (and their blanket) will appear to engage in active Bayesian inference. In other words, they will appear to model—and act on—their world to preserve their functional and structural integrity, leading to homoeostasis and a simple form of autopoiesis.

 

Life as we know it
Karl Friston

J. R. Soc. Interface 6 September 2013 vol. 10 no. 86 20130475

http://dx.doi.org/10.1098/ rsif.2013.0475


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ComplexInsight's curator insight, July 27, 2013 2:56 AM

one for reading list for later.

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Confound It! The Mathematics of Learning Language | Cocktail Party Physics, Scientific American Blog Network

Confound It! The Mathematics of Learning Language | Cocktail Party Physics, Scientific American Blog Network | Complexity - Complex Systems Theory | Scoop.it

A recent paper in Physical Review Letters by physicists in Germany and Scotland on new simulations of language acquisition [demonstrates]

that by assuming the absence of synonyms, it’s possible to pick up the vocabulary of a new language much more quickly. Yes, there is a math and physics component to how we learn new words, and it’s a lively field of study.

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Stochastic Dynamics of Lexicon Learning in an Uncertain and Nonuniform World

We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning - whereby information from multiple episodes is combined to eliminate incorrect meanings - can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally-efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially-efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.

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Protest in the connected society

Protest in the connected society | Complexity - Complex Systems Theory | Scoop.it

From New York to Istanbul, and Rio to Tunis, waves of social unrest have been sweeping across the world. Whatever they are called – Occupy Wall Street in New York, the Jasmine Revolution in Tunisia or the Arab Spring beyond, and the Salad Uprising in Brazil – the mass mobilisations share several common features.
Espousing public discontent over a range of sometimes unrelated, even conflicting issues, they were driven largely by new communication technologies coupled with an abiding distrust of government policies.
Unlike the formal, planned protests of earlier times, the latest ones are, for the most part, informal and relatively spontaneous. As such, scientists say, they reflect a shift away from conventional social hierarchies towards what some call leaderless networks.


Read more: http://www.theage.com.au/digital-life/digital-life-news/protest-in-the-connected-society-20130726-2qoyd.html#ixzz2b87OWggE


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The Magic Ratio That Wasn't - Percolator - The Chronicle of Higher Education, by Tom Bartlett

The Magic Ratio That Wasn't - Percolator - The Chronicle of Higher Education, by Tom Bartlett | Complexity - Complex Systems Theory | Scoop.it

The 2009 book Positivity: Top-Notch Research Reveals the 3 to 1 Ratio That Will Change Your Life, by Barbara Fredrickson, was praised by the heavyweights of psychology. Daniel Gilbert said it provided a “scientifically sound prescription for joy.” Daniel Goleman extolled its “surefire methods for transforming our lives.” Martin E.P. Seligman,often called the father of positive psychology, raved that “this book, like Barb, is the ‘real thing.’”

But the top-notchness of the research that underpins the book has been called into serious question. Even Fredrickson, a professor of psychology at the University of North Carolina at Chapel Hill, has now backed away from the ratio in the book’s subtitle, saying she didn’t really understand the mathematics behind it and had relied instead on the fact that it had been peer-reviewed.

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Bernard Ryefield's curator insight, August 5, 2013 11:25 AM

see my previous scoop here : http://sco.lt/8BPsmX

Bernard Ryefield's comment, August 5, 2013 11:26 AM
or here : http://sco.lt/6iaF9t
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Complex Adaptive Systems: An Introduction to Computational Models of Social Life: An Introduction to Computational Models of Social Lif by John H Miller, Scott E Page

Complex Adaptive Systems: An Introduction to Computational Models of Social Life: An Introduction to Computational Models of Social Lif by John H Miller, Scott E Page | Complexity - Complex Systems Theory | Scoop.it

This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, "Complex Adaptive Systems" focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents.

John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.

 

 

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Jay Wright Forrester - Wikipedia, the free encyclopedia

Jay Wright Forrester - Wikipedia, the free encyclopedia | Complexity - Complex Systems Theory | Scoop.it
Jay Wright Forrester (born July 14, 1918) is a pioneer American computer engineer, systems scientist and was a professor at the MIT Sloan School of Management.
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The complex dynamics of wishful thinking: The critical positivity ratio

The complex dynamics of wishful thinking: The critical positivity ratio | Complexity - Complex Systems Theory | Scoop.it

We examine critically the claims made by Fredrickson and Losada (2005) concerning the construct known as the "positivity ratio". We find no theoretical or empirical justification for the use of differential equations drawn from fluid dynamics, a subfield of physics, to describe changes in human emotions over time; furthermore, we demonstrate that the purported application of these equations contains numerous fundamental conceptual and mathematical errors. The lack of relevance of these equations and their incorrect application lead us to conclude that Fredrickson and Losada's claim to have demonstrated the existence of a critical minimum positivity ratio of 2.9013 is entirely unfounded. More generally, we urge future researchers to exercise caution in the use of advanced mathematical tools such as nonlinear dynamics and in particular to verify that the elementary conditions for their valid application have been met.

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On the (mis)use of nonlinear dynamics tools in psychology.

Have fun !

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Complex Systems Are Not Networks; They are Cases - Brian Castelli

Complex Systems Are Not Networks; They are Cases - Brian Castelli | Complexity - Complex Systems Theory | Scoop.it

Nodes are not just nodes; they are cases! As such, complex systems are not networks; complex systems are sets of cases.

1. Cases are more than nodes in some adjacency matrix. Said another way, there is more to a case than its position within a network or the relationships it shares with other nodes. Cases are complex, comprised of characteristics (measurements) that are beyond (cannot be reduced to) the relational.

2. In turn, therefore, complex systems cannot be reduced to (or studied solely as) networks, as the agents of which these systems are comprised are not just nodes or positions within some network. In other words, because network science only studies cases as nodes, it does not constitute the robust model of complex systems it is generally touted to be. Network science maps only one particular dimension (the relational) of the complex systems it studies.

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Complex Adaptive Systems Modeling | Modeling population growth in online social networks

Online social networks (OSNs) are now among the most popular applications on the web offering platforms for people to interact, communicate and collaborate with others. The rapid development of OSNs provides opportunities for people’s daily communication, but also brings problems such as burst network traffic and overload of servers. Studying the population growth pattern in online social networks helps service providers to understand the people communication manners in OSNs and facilitate the management of network resources. In this paper, we propose a population growth model for OSNs based on the study of population distribution and growth in spatiotemporal scale-space

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Bernard Ryefield's Complexity PearlTree

Bernard Ryefield's Complexity PearlTree | Complexity - Complex Systems Theory | Scoop.it

Put your bookmarks in trees and make teams around them

 

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Steve Grey's comment, July 25, 2013 7:42 PM
Perhaps I'm missing something. I see the diagram with circles in it very large as a 'wallpaper' background, that I can't navigate round and some live links in the middles, one of which bring up a small version of the diagram but that will only come up yo a smallish picture about ten centimetres across.
Bernard Ryefield's comment, July 25, 2013 8:05 PM
@Steve Grey : looks like a browser and/or mouse problem (zoom ?).
Ignacio Sáenz de Miera's curator insight, July 31, 2013 4:52 AM

Impressive!

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Power-law distributions in empirical data - Aaron Clauset, Cosma Rohilla Shalizi, M. E. J. Newman

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
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Announcing SFI's new free online courses in complex systems


The first course, "Introduction to Complexity", will be an accessible introduction to the field, with no pre-requisites and no course fees. It is free and open to anyone.

Second session starts September 30, 2013

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if you want to know what complexity is all about and have a hands-on practice

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