Complexity & Systems
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Complexity & Systems
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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Systems thinking and courage | Rethinking Complexity

Systems thinking and courage | Rethinking Complexity | Complexity & Systems | Scoop.it

In all the books and research papers on systems thinking that I have read, I don't think I have yet found the word courage as part of the language used. There is a lot written about systems thinking in terms of it's relevance and importance, it's theories and methodologies, but nothing about what it takes--emotionally. And I'm convinced: systems thinking not only requires skill, it also takes courage.


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David Hain's curator insight, November 8, 2013 2:51 AM

Corage is easier when shared purpose lights the flame!

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The Math of Segregation » American Scientist

The Math of Segregation » American Scientist | Complexity & Systems | 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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Digithead's Lab Notebook: Complex systems

Digithead's Lab Notebook: Complex systems | Complexity & Systems | Scoop.it

One hundred years from now, the role of science and technology will be about becoming part of nature rather than trying to control it. - Joichi Ito, MIT Media Lab director
In complex systems, trade-offs are everywhere. Engineers designing technological artifacts carefully balance competing objectives. An economy allocates resources like land, labor and capital to one use or another. Even evolution faces trade-offs.

 

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Lorien Pratt's curator insight, November 10, 2013 12:29 AM

Some insights into how to make trade-offs in complex systems when there are multiple competing outcomes.

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Emergence of small-world anatomical networks in self-organizing clustered neuronal cultures

In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to experimentally investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. At all the different stages of the culture's development, identification of neurons' and neurites' location by means of a dedicated software allows to ultimately extract an adjacency matrix from each image of the culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main network's characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graph's micro- and meso-scale properties emerge. Finally, we identify the main physical processes ruling the culture's morphological transformations, and embed them into a simplified growth model qualitatively reproducing the overall set of experimental observations.

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The Mathematical Regularities Underlying Both Biological and Social Systems

Geoffrey West, Luis Bettencourt October 2013 Project Highlights On the mathematical regularities underlying both biological and social systems
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Powerlaw: a Python package for analysis of heavy-tailed distributions

Powerlaw: a Python package for analysis of heavy-tailed distributions | Complexity & Systems | Scoop.it

Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user.The source code is publicly available and easily extensible.

 

 

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An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks

An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks | Complexity & Systems | Scoop.it

Many real world, complex phenomena have underlying structures of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics,with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of topological features and node attributes can improve link prediction. We provide an approach to predicting future links by applying Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine a large dynamic social network with over 106 nodes (Twitter reciprocal reply networks), both as a test of our general method and as a problem of scientific interest in itself. Our method exhibits fast convergence and high levels of precision for the top twenty predicted links, and to our knowledge, strongly outperforms all extant methods. Based on our findings, we suggest possible factors which may be driving the evolution of Twitter reciprocal reply networks.

 

 

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Powerlaws and Self-Organized Criticality in Theory and Nature

Powerlaws and Self-Organized Criticality in Theory and Nature | Complexity & Systems | Scoop.it

Powerlaws and distributions with heavy tails are common features of many experimentally studied complex systems, like the distribution of the sizes of earthquakes and solar flares, or the duration of neuronal avalanches in the brain. It had been tempting to surmise that a single general concept may act as a unifying underlying generative mechanism, with the theory of self organized criticality being a weighty contender.
On the theory side there has been, lively activity in developing new and extended models. Three classes of models have emerged. The first line of models is based on a separation between the time scales of drive and dissipation, and includes the original sandpile model and its extensions, like the dissipative earthquake model. Within this approach the steady state is close to criticality in terms of an absorbing phase transition. The second line of approach is based on external drives and internal dynamics competing on similar time scales and includes the coherent noise model, which has a non-critical steady state characterized by heavy-tailed distributions. The third line of modeling proposes a non-critical state which is self-organizing, being guided by an optimization principle, such as the concept of highly optimized tolerance.
We present a comparative overview regarding distinct modeling approaches together with a discussion of their potential relevance as underlying generative models for real-world phenomena. The complexity of physical and biological scaling phenomena has been found to transcend the explanatory power of individual paradigmal concepts, like the theory of self-organized criticality, the interaction between theoretical development and experimental observations has been very fruitful, leading to a series of novel concepts and insights.

 

 

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Bernard Ryefield's comment, November 13, 2013 7:06 AM
UPDATED: [v2] Tue, 12 Nov 2013
Bernard Ryefield's comment, December 13, 2013 6:38 AM
UPDATED [v3] Thu, 12 Dec 2013
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Law of Urination: all mammals empty their bladders over the same duration

The urinary system evolved to eject fluids from the body quickly and efficiently. Despite a long history of successful urology treatments in humans and animals, the physics of urination has received comparatively little attention. In this combined experimental and theoretical investigation, we elucidate the hydrodynamics of urination across five orders of magnitude in animal mass, from mice to elephants. Using high-speed fluid dynamics videos and flow-rate measurement at Zoo Atlanta, we discover the "Law of Urination", which states animals empty their bladders over nearly constant duration of average 21 seconds (standard deviation 13 seconds), despite a difference in bladder volume from 100 mL to 100 L. This feat is made possible by the increasing urethra length of large animals which amplifies gravitational force and flow rate. We also demonstrate the challenges faced by the urinary system for rodents and other small mammals for which urine flow is limited to single drops. Our findings reveal the urethra evolved as a flow-enhancing device, enabling the urinary system to be scaled up without compromising its function. This study may help in the diagnosis of urinary problems in animals and in inspiring the design of scalable hydrodynamic systems based on those in nature.

Bernard Ryefield's insight:

Well, this study had to be done, don't you think ?

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Records of conflict and cooperation reveal wisdom of the ages

Records of conflict and cooperation reveal wisdom of the ages | Complexity & Systems | Scoop.it

One of the basic principles of science is that good theories extrapolate well. The laws of gravity hold both here and on the moon, and this universality enables us to land a spacecraft on Mars. Darwin drew the theory of evolution from studies of a remote island in the Pacific, but today we use it to explain the emergence of drug-resistant tuberculosis in a city hospital.

The theories of gravity and of evolution are two of our greatest scientific achievements. But in contrast to the universal nature of these laws, our understanding of the human world — the messy realm of newspapers and cafes, traffic jams and gossip, governments and social movements — is remarkably limited.

Take, for example, some of the most basic questions in politics. How do societies resolve conflict? How do new methods for resolving conflict emerge? A newspaper story can give us incredible detail on a particular fight — the war in Syria, say, or the political brinksmanship over “Obamacare.”

 

 

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The Mathematical Shape of Big Science Data | Simons Foundation

The Mathematical Shape of Big Science Data | Simons Foundation | Complexity & Systems | Scoop.it

Scientific data sets are becoming more dynamic, requiring new mathematical techniques on par with the invention of calculus.

 

 

Bernard Ryefield's insight:

big and noisy scientific data sets need new methods of analysis, turning to complexity science

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Introduction to Complex Systems

Introduction to Complex Systems | Complexity & Systems | Scoop.it
Complex systems are systems that exhibit several defining characteristics (Kastens et al., 2009), including: Feedback loops, where change in a variable results in either an amplification (positive feedback) or a ...
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a short illustrated introduction to what constitutes a complex systems

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Djebar Hammouche's curator insight, October 6, 2013 11:33 AM
Introduction to Complex Systems
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2013 CSSS Proceedings | Santa Fe Institute

2013 CSSS Proceedings | Santa Fe Institute | Complexity & Systems | Scoop.it

What features make the international banking network fragile or robust against major shocks, such as the failure of Lehman Brothers in fall 2008? 

Could our understanding of marine ecosystems help us better understand the human body and lead to better health and well being?

How do the strategies of attacker and defender co-evolve in a network attack, and how can examining this co-evolution help make online networks more secure? Graduate students and postdocs participating in SFI's 2013 Complex Systems Summer School collaborated to develop some 15 original research papers.

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PLOS ONE: Collective Phenomena and Non-Finite State Computation in a Human Social System

PLOS ONE: Collective Phenomena and Non-Finite State Computation in a Human Social System | Complexity & Systems | Scoop.it

We investigate the computational structure of a paradigmatic example of distributed social interaction: that of the open-source Wikipedia community. We examine the statistical properties of its cooperative behavior, and perform model selection to determine whether this aspect of the system can be described by a finite-state process, or whether reference to an effectively unbounded resource allows for a more parsimonious description. We find strong evidence, in a majority of the most-edited pages, in favor of a collective-state model, where the probability of a “revert” action declines as the square root of the number of non-revert actions seen since the last revert. We provide evidence that the emergence of this social counter is driven by collective interaction effects, rather than properties of individual users.

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How to predict community responses to perturbations in the face of imperfect knowledge and network complexity

How to predict community responses to perturbations in the face of imperfect knowledge and network complexity | Complexity & Systems | Scoop.it

Recent attempts to predict the response of large food webs to perturbations have revealed that in larger systems increasingly precise information on the elements of the system is required. Thus, the effort needed for good predictions grows quickly with the system's complexity. Here, we show that not all elements need to be measured equally well, suggesting that a more efficient allocation of effort is possible. We develop an iterative technique for determining an efficient measurement strategy. In model food webs, we find that it is most important to precisely measure the mortality and predation rates of long-lived, generalist, top predators. Prioritizing the study of such species will make it easier to understand the response of complex food webs to perturbations.

 

 

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Hunting the spark of creativity

Hunting the spark of creativity | Complexity & Systems | Scoop.it

Until recently, decision makers could only effectively harness shared creativity from relatively small mastermind groups such as boards, panels or committees. Data from these could be placed in pre-organized, well-structured and well-categorized "buckets" to extract creative knowledge.

The relatively recent growth and development of the Internet, however, along with social network technology, provides an opportunity to expand the mastermind concept to hundreds, or thousands or even hundreds of thousands of geographically distant people.

University of Cincinnati complex systems scientist Ali Minai and a team of researchers funded by the National Science Foundation (NSF) are attempting to do just that—to develop computer-based tools to mine the Internet and communities of social media for creative insights.

 

 

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Reggie Walker's curator insight, November 10, 2013 7:44 PM

Cool study. Find your spark and share it!

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Environmental structure and competitive scoring advantages in team competitions

Environmental structure and competitive scoring advantages in team competitions | Complexity & Systems | Scoop.it

In most professional sports, playing field structure is kept neutral so that scoring imbalances may be attributed to differences in team skill. It thus remains unknown what impact environmental heterogeneities can have on scoring dynamics or competitive advantages. Applying a novel generative model of scoring dynamics to roughly 10 million team competitions drawn from an online game, we quantify the relationship between the structure within a competition and its scoring dynamics, while controlling the impact of chance. Despite wide structural variations, we observe a common three-phase pattern in the tempo of events. Tempo and balance are highly predictable from a competition's structural features alone and teams exploit environmental heterogeneities for sustained competitive advantage. Surprisingly, the most balanced competitions are associated with specific environmental heterogeneities, not from equally skilled teams. These results shed new light on the design principles of balanced competition, and illustrate the potential of online game data for investigating social dynamics and competi


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Universal growth constraints of human systems

Universal growth constraints of human systems | Complexity & Systems | Scoop.it

Scale independence is a ubiquitous feature of complex systems which implies a highly skewed distribution of resources with no characteristic scale. Research has long focused on why systems as varied as protein networks, evolution and stock actions all feature scale independence. Assuming that they simply do, we focus here on describing exactly how this behavior emerges. We show that growing towards scale independence implies strict constraints: the first is the well-known preferential attachment principle and the second is a new form of temporal scaling. These constraints pave a precise evolution path, such that an instantaneous snapshot of a distribution is enough to reconstruct its past and to predict its future. We validate our approach on diverse spheres of human activities ranging from scientific and artistic productivity, to sexual relations and online traffic.

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Naomi Klein: How science is telling us all to revolt

Naomi Klein: How science is telling us all to revolt | Complexity & Systems | Scoop.it

Is our relentless quest for economic growth killing the planet? Climate scientists have seen the data – and they are coming to some incendiary conclusions. Complex scientist Brad Werner is saying that his research shows that our entire economic paradigm is a threat to ecological stability. And indeed that challenging this economic paradigm – through mass-movement counter-pressure – is humanity’s best shot at avoiding catastrophe.

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Scientists identify a mathematical 'crystal ball' that may predict calamities

Scientists identify a mathematical 'crystal ball' that may predict calamities | Complexity & Systems | Scoop.it
Neuroscientists have come up with a mathematical equation that may help predict calamities such as financial crashes in economic systems and epileptic seizures in the brain.
Bernard Ryefield's insight:

the "crystal ball" demands to be tested on more complex systems

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Modeling complex systems with adaptive networks

Modeling complex systems with adaptive networks | Complexity & Systems | 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


Via Complexity Digest, NESS, Complejidady Economía
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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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The sierpinski triangle page to end most sierpinski triangle pages

The sierpinski triangle page to end most sierpinski triangle pages | Complexity & Systems | Scoop.it

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luiy's curator insight, October 17, 2013 9:54 AM
Constructing the Sierpinski triangle

Throughout my years playing around with fractals, the Sierpinski triangle has been a consistent staple. The triangle is named after Wacław Sierpiński and as fractals are wont the pattern appears in many places, so there are many different ways of constructing the triangle on a computer.

All of the methods are fundamentally iterative. The most obvious method is probably the triangle-in-triangle approach. We start with one triangle, and at every step we replace each triangle with 3 subtriangles:

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The Viable System Model

The Viable System Model | Complexity & Systems | Scoop.it

Around 1970, Stafford Beer developed the viable system model to diagnose the faults in any organizational system, programme, institution, nation, or enterprise. It uses cybernetics, which – according to its originator Norbert Wiener – is the study of control and communication in the animal and the machine, but there are many other interesting definitions. The central question could be formulated as: How can organizational efficacy be maintained? How can organizations sustain their own existence? Or: How do organizations create viability?

 

 

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Using a complex system approach to address world challenges in Food and Agriculture

World food supply is crucial to the well-being of every human on the planet in the basic sense that we need food to live. It also has a profound impact on the world economy, international trade and global political stability. Furthermore, consumption of certain types and amounts foods can affect health, and the choice of livestock and plants for food production can impact sustainable use of global resources. There are communities where insufficient food causes nutritional deficiencies, and at the same time other communities eating too much food leading to obesity and accompanying diseases. These aspects reflect the utmost importance of agricultural production and conversion of commodities to food products. Moreover, all factors contributing to the food supply are interdependent, and they are an integrative part of the continuously changing, adaptive and interdependent systems in the world around us. The properties of such interdependent systems usually cannot be inferred from the properties of its parts. In addressing current challenges, like the apparent incongruences of obesity and hunger, we have to account for the complex interdependencies among areas such as physics and sociology. This is possible using the complex system approach. It encompasses an integrative multi-scale and inter-disciplinary approach. Using a complex system approach that accounts for the needs of stakeholders in the agriculture and food domain, and determines which research programs will enable these stakeholders to better anticipate emerging developments in the world around them, will enable them to determine effective intervention strategies to simultaneously optimise and safeguard their interests and the interests of the environment. Using a complex system approach to address world challenges in Food and Agriculture H.G.J. van Mil, E.A. Foegeding, E.J. Windhab, N. Perrot, E. van der Linden http://arxiv.org/abs/1309.0614


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