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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Rescooped by Bernard Ryefield from Networks and Graphs
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Layer aggregation and reducibility of multilayer interconnected networks

Many complex systems can be represented as networks composed by distinct layers, interacting and depending on each others. For example, in biology, a good description of the full protein-protein interactome requires, for some organisms, up to seven distinct network layers, with thousands of protein-protein interactions each. A fundamental open question is then how much information is really necessary to accurately represent the structure of a multilayer complex system, and if and when some of the layers can indeed be aggregated. Here we introduce a method, based on information theory, to reduce the number of layers in multilayer networks, while minimizing information loss. We validate our approach on a set of synthetic benchmarks, and prove its applicability to an extended data set of protein-genetic interactions, showing cases where a strong reduction is possible and cases where it is not. Using this method we can describe complex systems with an optimal trade--off between accuracy and complexity.

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World Conference on Complex Systems (WCCS) | 10–13 Nov 2014

World Conference on Complex Systems (WCCS) will provide a high-level, international forum for scientists, researchers, industrial professionals,...
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International workshop on Complex Networks and their Applications – November 23-27, 2014, Marrakech, Morocco

International workshop on Complex Networks and their Applications – November 23-27, 2014, Marrakech, Morocco | Complexity & Systems | Scoop.it
Real-world entities often interconnect with each other through explicit or implicit relationships to form a complex network.
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4th International Conference of Complex Systems and Applications (ICCSA’2014) – June 23-26, 2014, Le Havre

4th International Conference of Complex Systems and Applications (ICCSA’2014) – June 23-26, 2014, Le Havre | Complexity & Systems | Scoop.it
The fourth ICCSA will focus on recent advances in complex systems and applications in all fields of science and engineering.
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A Sceptics View: “Kleiber’s Law” or the “3/4 Rule” is neither a Law nor a Rule but Rather an Empirical Approximation

A Sceptics View: “Kleiber’s Law” or the “3/4 Rule” is neither a Law nor a Rule but Rather an Empirical Approximation | Complexity & Systems | Scoop.it

Early studies showed the metabolic rate (MR) of different-sized animals was not directly related to body mass. The initial explanation of this difference, the “surface law”, was replaced by the suggestion that MR be expressed relative to massn, where the scaling exponent “n” be empirically determined. Basal metabolic rate (BMR) conditions were developed and BMR became important clinically, especially concerning thyroid diseases. Allometry, the technique previously used to empirically analyse relative growth, showed BMR of endotherms varied with 0.73–0.74 power of body mass. Kleiber suggested that mass3/4 be used, partly because of its easy calculation with a slide rule. Later studies have produced a range of BMR scaling exponents, depending on species measured. Measurement of maximal metabolism produced scaling exponents ranging from 0.80 to 0.97, while scaling of mammalian MR during growth display multi-phasic allometric relationships with scaling exponents >3/4 initially, followed by scaling exponents <3/4. There is no universal metabolic scaling exponent. The fact that “allometry” is an empirical technique to analyse relative change and not a biological law is discussed. Relative tissue size is an important determinant of MR. There is also a need to avoid simplistic assumptions regarding the allometry of surface area.

 

 

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Stochastic modeling of a serial killer

We analyze the time pattern of the activity of a serial killer, who during twelve years had murdered 53 people. The plot of the cumulative number of murders as a function of time is of "Devil's staircase" type. The distribution of the intervals between murders (step length) follows a power law with the exponent of 1.4. We propose a model according to which the serial killer commits murders when neuronal excitation in his brain exceeds certain threshold. We model this neural activity as a branching process, which in turn is approximated by a random walk. As the distribution of the random walk return times is a power law with the exponent 1.5, the distribution of the inter-murder intervals is thus explained. We illustrate analytical results by numerical simulation. Time pattern activity data from two other serial killers further substantiate our analysis.

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The Difference Between Complex and Complicated, And Why It Matters | Sam McNerney

The Difference Between Complex and Complicated, And Why It Matters | Sam McNerney | Complexity & Systems | Scoop.it
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A combined nelder-mead simplex and genetic algorithm

A combined nelder-mead simplex and genetic algorithm | Complexity & Systems | Scoop.it
It is usually said that genetic algorithm should be used when nothing else works. In practice, genetic algorithm are very often used for large sized global optimization problems, but are not very efficient for local optimization problems.
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Adaptive Cluster Synchronization of Directed Complex Networks with Time Delays

Adaptive Cluster Synchronization of Directed Complex Networks with Time Delays | Complexity & Systems | Scoop.it

This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks.

 

 

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Chaotic and non-chaotic phases in experimental responses of a single neuron

Consistency and predictability of brain functionalities depend on reproducible activity of a single neuron. We identify a reproducible non-chaotic neuronal phase where deviations between concave response latency profiles of a single neuron do not increase with the number of stimulations. A chaotic neuronal phase emerges at a transition to convex latency profiles which diverge exponentially, indicating irreproducible response timings. Our findings are supported by a quantitative mathematical framework and found robust to periodic and random stimulation patterns. In addition, these results put a bound on the neuronal temporal resolution which can be enhanced below a millisecond using neuronal chains.

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Complexity Digest joins Complex Systems Society

It is my pleasure to announce that Complexity Digest has become the official news channel of the the Complex Systems Society (CSS). We trust that this merger will service the growing number of people interested in the scientific study of complex systems.

 

We will continue offering our services for free. The scope of the content should be more varied, as more people become involved editing material for ComDig. This will be a gradual process which should benefit both members and non-members of the CSS.

 

The first change has been the logo of ComDig, which is in line with the new CSS logo. Further changes will continue in coming months.

 

Complex wishes,

Carlos Gershenson

Editor-in-Chief


Via Complexity Digest
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What’s the use of “econo-physics”? — The Physics of Finance — Medium

What’s the use of “econo-physics”? — The Physics of Finance — Medium | Complexity & Systems | Scoop.it

About 20 years ago, a few physicists got interested in applying some ideas and concepts from physics to problems in finance and economics. The area is sometimes called "econophysics" – I actually don't like the name – and it tends to be controversial. Some economists find it annoying, although a few others either work in the area or do work that is closely associated conceptually. On occasion, you'll find people suggesting that research in econophysics has never achieved anything worthwhile (this was seven years ago, and that writer may possibly have changed his mind by now).

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Flow, Conflux | Smart Cities

Flow, Conflux | Smart Cities | Complexity & Systems | Scoop.it

“The city is not only a community, it is a conflux. ….The real city, as a center of industry, is a conflux of streams of traffic; as a center of culture, it is conflux of streams of thought.” So wrote Benton MacKaye in 1928 in his book The New Exploration: A Philosophy of Regional Planning. When I sent a copy of my own recent book The New Science of Cities to my erstwhile colleague and old friend Lionel March, he quickly scowered it and said: “I see in your Preamble that you cite Castells’ ‘space of flows’ and that your approach makes much of flows and networks. I immediately turned to your bibliography to search for the name Benton MacKaye. It is not there! The author of The New Exploration (1928) is my hero of metropolitan/regional development. I’m sure you know of him”.

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Eli Levine's curator insight, April 17, 2014 12:00 PM

Location, location, location.

 

The natural geography has to fit with the demands of the population and the society.  It's not something that someone on high chooses, but rather one where things grow up naturally according to the relative advantages and disadvantages of the area.  Then you build and with building in these geographically advantageous (or, sometimes, just convenient) areas you reinforce their advantages as centers of commerce, trade and "flows" as Batty would put it.

 

It makes sense to have it be on the regional, national and/or international scale, such that we, as humans, take advantage of the most strategic places and the most strategic resources that are available.  With this comes the flourishing of new life, happiness and possible/hopefully sustainable prosperity for the present and for the future well being of our civilizations.

 

The climate is changing and that's going to force a lot of changes on our part.  If we can survive the environmental tumult, and the economic and social tumult that it is going to cause, we could potentially, get off on a better footing than before, in spite of the losses which we incur as a result of the present silliness of our political, social and economic "leadership".

 

Good stuff!

 

Think about it.

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Air traffic complexity based on dynamical systems

This paper presents a new air traffic complexity metric based on dynamical systems. Based on a set of radar observations (position and speed) a vector field interpolating these data is constructed. Once the field has been obtained, the Lyapunov spectrum of the associated dynamical system is computed on points evenly spaced on a spatial grid. The results of the computations are summarized on complexity maps, with high values indicating areas to avoid or to carefully monitor. A first approach based on linear dynamical system enable to compute an aggregate complexity metric. In order to produce complexity maps, two extensions of the previous approach have been developed (one in space and another in space and time). Finally, an approximation is proposed in order to localize the computation of the vector field by the mean of Local Linear Models.

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Think Complexity

Think Complexity | Complexity & Systems | Scoop.it

This book is also about complexity science, which is an interdisciplinary field—at the intersection of mathematics, computer science and natural science—that focuses on discrete models of physical systems.  In particular, it focuses on complex systems, which are systems with many interacting components.

Complex systems include networks and graphs, cellular automata, agent-based models and swarms, fractals and self-organizing systems, chaotic systems and cybernetic systems.

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Interdisciplinary Symposium on Complex Systems (ISCS’14) – September 15-18, 2014, Florence, Italy

Interdisciplinary Symposium on Complex Systems (ISCS’14) – September 15-18, 2014, Florence, Italy | Complexity & Systems | Scoop.it
The main aim of the 2014 Interdisciplinary Symposium on Complex Systems is to bring together researchers working on complex systems.
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Decision accuracy in complex environments is often maximized by small group sizes

Decision accuracy in complex environments is often maximized by small group sizes | Complexity & Systems | Scoop.it

Individuals in groups, whether composed of humans or other animal species, often make important decisions collectively, including avoiding predators, selecting a direction in which to migrate and electing political leaders. Theoretical and empirical work suggests that collective decisions can be more accurate than individual decisions, a phenomenon known as the ‘wisdom of crowds’. In these previous studies, it has been assumed that individuals make independent estimates based on a single environmental cue. In the real world, however, most cues exhibit some spatial and temporal correlation, and consequently, the sensory information that near neighbours detect will also be, to some degree, correlated. Furthermore, it may be rare for an environment to contain only a single informative cue, with multiple cues being the norm. We demonstrate, using two simple models, that taking this natural complexity into account considerably alters the relationship between group size and decision-making accuracy. In only a minority of environments do we observe the typical wisdom of crowds phenomenon (whereby collective accuracy increases monotonically with group size). When the wisdom of crowds is not observed, we find that a finite, and often small, group size maximizes decision accuracy. We reveal that, counterintuitively, it is the noise inherent in these small groups that enhances their accuracy, allowing individuals in such groups to avoid the detrimental effects of correlated information while exploiting the benefits of collective decision-making. Our results demonstrate that the conventional view of the wisdom of crowds may not be informative in complex and realistic environments, and that being in small groups can maximize decision accuracy across many contexts.

 

 

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Homophily and the Speed of Social Mobilization: The Effect of Acquired and Ascribed Traits

Large-scale mobilization of individuals across social networks is becoming increasingly prevalent in society. However, little is known about what affects the speed of social mobilization. Here we use a framed field experiment to identify and measure properties of individuals and their relationships that predict mobilization speed. We ran a global social mobilization contest and recorded personal traits of the participants and those they recruited. We studied the effects of ascribed traits (gender, age) and acquired traits (geography, and information source) on the speed of mobilization. We found that homophily, a preference for interacting with other individuals with similar traits, had a mixed role in social mobilization. Homophily was present for acquired traits, in which mobilization speed was faster when the recuiter and recruit had the same trait compared to different traits. In contrast, we did not find support for homophily for the ascribed traits. Instead, those traits had other, non-homophily effects: Females mobilized other females faster than males mobilized other males. Younger recruiters mobilized others faster, and older recruits mobilized slower. Recruits also mobilized faster when they first heard about the contest directly from the contest organization, and decreased in speed when hearing from less personal source types (e.g. family vs. media). These findings show that social mobilization includes dynamics that are unlike other, more passive forms of social activity propagation. These findings suggest relevant factors for engineering social mobilization tasks for increased speed.

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Social Evolution: New Horizons

Cooperation is a widespread natural phenomenon yet current evolutionary thinking is dominated by the paradigm of selfish competition. Recent advanced in many fronts of Biology and Non-linear Physics are helping to bring cooperation to its proper place. In this contribution, the most important controversies and open research avenues in the field of social evolution are reviewed. It is argued that a novel theory of social evolution must integrate the concepts of the science of Complex Systems with those of the Darwinian tradition. Current gene-centric approaches should be reviewed and com- plemented with evidence from multilevel phenomena (group selection), the constrains given by the non-linear nature of biological dynamical systems and the emergent nature of dissipative phenomena.

 

Chapter in forthcoming open access book "Frontiers in Ecology, Evolution and Complexity"

by Octavio Miramontes, Og DeSouza

arXiv:1404.6267 [q-bio.PE]


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Eli Levine's curator insight, April 28, 2014 3:14 PM

Indeed, one example of this cooperative method in evolution that springs to mind is the coevolution of dogs and humans.  By being domesticated, dogs earned themselves protection from the elements and a reliable source of food, water and even medical care in case of injury.  Humans gained more sleep, due to the watchfulness of dogs at night, and thus, gained a greater degree of cognitive functionality that otherwise wouldn't have existed had dogs not been kept with us.

 

Sometimes, the cooperative option is the selfish option, and vice versa.

 

Imagine if political leaders, rather than competing with each other for relative power and pride within the society, worked together to solve actual common problems in our society?  What if nations worked together to solve common problems in humanity, amongst humanity, sucht that we're focused on survival and well being, rather than wealth or relative power?

 

What is so difficult and onerous about these changes?

 

I don't understand.

 

Think about it.

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Allometric Relations and Scaling Laws for the Cardiovascular System of Mammals

Allometric Relations and Scaling Laws for the Cardiovascular System of Mammals | Complexity & Systems | Scoop.it
The modeling of the cardiovascular system of mammals is discussed within the framework of governing allometric relations and related scaling laws for mammals.
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Measuring the Complexity of Self-Organizing Traffic Lights

We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby’s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.

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John Niles's curator insight, April 27, 2014 11:04 PM

USDOT works to educate traffic engineers on adaptive traffic signals.  http://www.nc-ite.org/images/files/Adaptive%20Signal%20Control%20Technology%20Overview%20Presentation.pdf

 

This paper provides insight into difficulty. Adaptive traffic signal timing is not commonly implemented in U.S. urban areas.


If vehicles were to communicate with traffic signals, another layer of complexity would be added.

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Resilience of modular complex networks

Complex networks often have a modular structure, where a number of tightly- connected groups of nodes (modules) have relatively few interconnections. Modularity had been shown to have an important effect on the evolution and stability of biological networks, on the scalability and efficiency of large-scale infrastructure, and the development of economic and social systems. An analytical framework for understanding modularity and its effects on network vulnerability is still missing. Through recent advances in the understanding of multilayer networks, however, it is now possible to develop a theoretical framework to systematically study this critical issue. Here we study, analytically and numerically, the resilience of modular networks under attacks on interconnected nodes, which exhibit high betweenness values and are often more exposed to failure. Our model provides new understandings into the feedback between structure and function in real world systems, and consequently has important implications as diverse as developing efficient immunization strategies, designing robust large-scale infrastructure, and understanding brain function.

 

Resilience of modular complex networks
Saray Shai, Dror Y. Kenett, Yoed N. Kenett, Miriam Faust, Simon Dobson, Shlomo Havlin

http://arxiv.org/abs/1404.4748


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Rescooped by Bernard Ryefield from Networks and Graphs
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Dynamics of Information Spreading in Online Social Networks

 Online social networks (OSNs) are changing the way information spreads throughout the Internet. A deep understanding of information spreading in OSNs leads to both social and commercial benefits. In this paper, dynamics of information spreading (e.g., how fast and widely the information spreads against time) in OSNs are characterized, and a general and accurate model based on Interactive Markov Chains (IMCs) and mean-field theory is established. This model shows tight relations between network topology and information spreading in OSNs, e.g., the information spreading ability is positively related to the heterogeneity of degree distributions whereas negatively related to the degree-degree correlations in general. Further, the model is extended to feature the time-varying user behavior and the ever-changing information popularity. By leveraging the mean-field theory, the model is able to characterize the complicated information spreading process (e.g., the dynamic patterns of information spreading) with six parameters. Extensive evaluations based on Renren's data set illustrate the accuracy of the model, e.g., it can characterize dynamic patterns of video sharing in Renren precisely and predict future spreading dynamics successfully.

Bernard Ryefield's insight:

updated Apr 24 : http://arxiv.org/abs/1404.5562v2

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Air traffic complexity based on non linear dynamical systems

Air traffic complexity based on non linear dynamical systems | Complexity & Systems | Scoop.it
This paper presents a new air traffic complexity metric based on non-linear dynamical systems. The goal of this metric is to identify any trajectory organisation in the traffic pattern in order to quantify the associated control difficulty.
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Institute for Complex System Simulations (Home page)


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