Complex Networks Everywhere
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Complex Networks Everywhere
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Grand challenges for Computational Intelligence

The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent.

 

Prokopenko M (2014) Grand challenges for computational intelligence. Front. Robot. AI 1:2. http://journal.frontiersin.org/Journal/10.3389/frobt.2014.00002/full


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A draft map of the human proteome

The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org ) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

 

A draft map of the human proteome
• Min-Sik Kim, et al.

Nature 509, 575–581 (29 May 2014) http://dx.doi.org/10.1038/nature13302


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How population heterogeneity in susceptibility and infectivity influences epidemic dynamics

How population heterogeneity in susceptibility and infectivity influences epidemic dynamics | Complex Networks Everywhere | Scoop.it

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The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group

The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.

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Uncovering the structure and temporal dynamics of information propagation

Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

 

Uncovering the structure and temporal dynamics of information propagation
MANUEL GOMEZ RODRIGUEZ, JURE LESKOVEC, DAVID BALDUZZI, BERNHARD SCHÖLKOPF
Network Science , Volume 2 , Issue 01 , April 2014, pp 26 - 65
http://dx.doi.org/10.1017/nws.2014.3 ;


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Transittability of complex networks and its applications to regulatory biomolecular networks : Scientific Reports : Nature Publishing Group

Transittability of complex networks and its applications to regulatory biomolecular networks : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
We have often observed unexpected state transitions of complex systems. We are thus interested in how to steer a complex system from an unexpected state to a desired state. Here we introduce the concept of transittability of complex networks, and derive a new sufficient and necessary condition for state transittability which can be efficiently verified. We define the steering kernel as a minimal set of steering nodes to which control signals must directly be applied for transition between two specific states of a network, and propose a graph-theoretic algorithm to identify the steering kernel of a network for transition between two specific states. We applied our algorithm to 27 real complex networks, finding that sizes of steering kernels required for transittability are much less than those for complete controllability. Furthermore, applications to regulatory biomolecular networks not only validated our method but also identified the steering kernel for their phenotype transitions.

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Shock waves on complex networks : Scientific Reports : Nature Publishing Group

Shock waves on complex networks : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node.

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Eli Levine's curator insight, May 20, 2014 8:19 AM

Indeed, this is intuitive enough without the mathematics to back it up.  This could be mapped out and used for prioritizing the defense or attack of various points within the network, either in the digital or analog worlds.

 

Way cool science!

 

Think about it.

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Marie Curie says stop hating on quilt plots already. | Simply Statistics

Marie Curie says stop hating on quilt plots already. | Simply Statistics | Complex Networks Everywhere | Scoop.it

"There are sadistic scientists who hurry to hunt down error instead of establishing the truth." -Marie Curie (http://en.wikiquote.org/wiki/Marie_Curie)


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Dmitry Alexeev's curator insight, January 29, 2014 12:24 AM

we support no ntrolling quiltplots)

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Scoring dynamics across professional team sports: tempo, balance and predictability

Despite growing interest in quantifying and modeling the scoring dynamics within professional sports games, relative little is known about what patterns or principles, if any, cut across different sports. Using a comprehensive data set of scoring events in nearly a dozen consecutive seasons of college and professional (American) football, professional hockey, and professional basketball, we identify several common patterns in scoring dynamics. Across these sports, scoring tempo---when scoring events occur---closely follows a common Poisson process, with a sport-specific rate. Similarly, scoring balance---how often a team wins an event---follows a common Bernoulli process, with a parameter that effectively varies with the size of the lead. Combining these processes within a generative model of gameplay, we find they both reproduce the observed dynamics in all four sports and accurately predict game outcomes. These results demonstrate common dynamical patterns underlying within-game scoring dynamics across professional team sports, and suggest specific mechanisms for driving them. We close with a brief discussion of the implications of our results for several popular hypotheses about sports dynamics.


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Correlations and Scaling Laws in Human Mobility

Correlations and Scaling Laws in Human Mobility | Complex Networks Everywhere | Scoop.it

In this paper, we empirically analyze the real-world human movements which are based on GPS records, and observe rich scaling properties in the temporal-spatial patterns as well as an abnormal transition in the speed-displacement patterns together with an evidence to the real-world traffic jams. In addition, we notice that the displacements at the population level show a significant positive correlation, indicating a cascading-like nature in human movements. Furthermore, our analysis at the individual level finds that the displacement distributions of users with stronger correlations usually are closer to the power law, suggesting a correlation between the positive correlation of the displacement series and the form of an individual's displacement distribution.


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Generalized friendship paradox in complex networks

The friendship paradox states that your friends have on average more friends than you have. Does the paradox "hold'" for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in large-scale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks.


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António F Fonseca's curator insight, January 13, 2014 4:03 AM

Maybe a good metric to characterize people on social networks, to have more or less friends than the average of their friends.

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Complexity Rising: From Human Beings to Human Civilization, a Complexity Profile | Yaneer Bar-Yam

Complexity Rising: From Human Beings to Human Civilization, a Complexity Profile | Yaneer Bar-Yam | Complex Networks Everywhere | Scoop.it

It is generally recognized that life is becoming more complex. This article analyzes the 
human social environment using the "complexity profile," a mathematical tool for 
characterizing the collective behavior of a system. The analysis is used to justify the 
qualitative observation that complexity of existence has increased and is increasing. The 
increase in complexity is directly related to sweeping changes in the structure and 
dynamics of human civilization—the increasing interdependence of the global economic 
and social system, and the instabilities of dictatorships, communism and corporate 
hierarchies. Our complex social environment is consistent with identifying global human 
civilization as an organism capable of complex behavior that protects its components 
(us) and which should be capable of responding effectively to complex environmental 
demands


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Lorien Pratt's curator insight, January 25, 2014 9:47 PM

This is so important!  We all feel that things are becoming more complex, now here's some evidence to show we're right. And as we suspected, it comes from increasing interdependence and "sweeping changes in the structure and dynamics of civilization".   Thought so!

Eli Levine's curator insight, February 5, 2014 4:34 PM

You see this in the devolution of religion from hierarchically based forms of morality.

 

Or on the decentralization of wealth from a handful of individuals to the general masses.

 

Or the collaborative work of government, rather than the command and control central systems that dominated the 20th century.

 

We're evolving.

 

And we've only begun this journey.

 

Think about it.

Anastasia Baranowski's curator insight, April 3, 2014 2:40 PM

Dear Sirs,

 

I think that people from different nationalities have to have marriages between different nationalities. The idea is multinational planet, where people live everywhere they want and they don't have any ideas of nationalizm or rasism. In this case all people can live in peace and harmony. No wars, only worldwide police. Economical development is possible only if people from different countries can come to each other and co-operate. The most important problem behind the human race is ecological! All people on the planet have to co-operate and communicate and help each other to save the planet and themselves and future generations!

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Guided Self-Organization: Inception (Emergence, Complexity and Computation): Mikhail Prokopenko

Is it possible to guide the process of self-organisation towards specific patterns and outcomes? Wouldn’t this be self-contradictory? After all, a self-organising process assumes a transition into a more organised form, or towards a more structured functionality, in the absence of centralised control. Then how can we place the guiding elements so that they do not override rich choices potentially discoverable by an uncontrolled process?
This book presents different approaches to resolving this paradox. In doing so, the presented studies address a broad range of phenomena, ranging from autopoietic systems to morphological computation, and from small-world networks to information cascades in swarms. A large variety of methods is employed, from spontaneous symmetry breaking to information dynamics to evolutionary algorithms, creating a rich spectrum reflecting this emerging field.
Demonstrating several foundational theories and frameworks, as well as innovative practical implementations, Guided Self-Organisation: Inception, will be an invaluable tool for advanced students and researchers in a multiplicity of fields across computer science, physics and biology, including information theory, robotics, dynamical systems, graph theory, artificial life, multi-agent systems, theory of computation and machine learning.


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António F Fonseca's curator insight, January 7, 2014 2:56 AM

A potpourri about self organization, maybe good ideas for new theories.

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The world after Big Data: What the digital revolution means for us

The world after Big Data: What the digital revolution means for us | Complex Networks Everywhere | Scoop.it

Never before were politicians, business leaders, and scientists more urgently needed to master the challenges ahead of us. We are in the middle of a third industrial revolution. While we see the symptoms, such as the financial and economic crisis, cybercrime and cyberwar, we haven't understood the implications well. But at the end of this socio-economic transformation, we will live in a digital society. This comes with breath-taking opportunities and challenges, as they occur only every 100 years.

 

http://futurict.blogspot.mx/2014/05/the-world-after-big-data-what-digital.html

 

See also What's the Next Big Thing after Big Data? https://www.youtube.com/watch?v=P5Y76UB080M&list=UUYrlsSzinJN42rKmFlOOYxA


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Gary Bamford's curator insight, May 30, 2014 2:37 AM

Come on, keep up!

Rick Frank's curator insight, May 30, 2014 9:38 AM

This is a bit idealistic but I like the thought process behind it.

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Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group

Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics.

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António F Fonseca's curator insight, December 28, 2013 7:14 AM

Another paper on opinion dynamics.

Luciano Lampi's curator insight, January 11, 2014 5:45 PM

Humanrithms....

Claude Emond's curator insight, January 20, 2014 5:51 PM

Opinions are an unescapable part of sharing and influencing the direction of collective intelligence

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Origin of Peer Influence in Social Networks

Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.
DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702

Origin of Peer Influence in Social Networks
Phys. Rev. Lett. 112, 098702 – Published 6 March 2014
Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco


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Eli Levine's curator insight, March 10, 2014 5:16 PM

Indeed, we are all interconnected in very profound and subtle ways, whether we accept it or not.


This one's for the Libertarians and conservatives out there, who don't seem to think that their actions effect the other, or that the other can effect them, or that the actions done onto the other will effect the actions that are done onto them by the other.

 

Kind of like how they blame the poor for being angry at the rich, after the poor produced the wealth that engorges the rich.

 

Silly people....

 

Think about it.

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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Complex Networks Everywhere | Scoop.it

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luiy's curator insight, March 27, 2014 1:44 PM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 2014 6:01 AM

Another paper about popularity prediction.

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Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics : Scientific Reports : Nature Publishing Group

Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that-for the SIS model-differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive, and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals.

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Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group

Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
Recently, the impact of network structure on evolutionary dynamics has been at the center of attention when studying the evolutionary process of structured populations. This paper aims at finding out the key structural feature of network to capture its impact on evolutionary dynamics. To this end, a novel concept called heat heterogeneity is introduced to characterize the structural heterogeneity of network, and the correlation between heat heterogeneity of structure and outcome of evolutionary dynamics is further investigated on various networks. It is found that the heat heterogeneity mainly determines the impact of network structure on evolutionary dynamics on complex networks. In detail, the heat heterogeneity readjusts the selection effect on evolutionary dynamics. Networks with high heat heterogeneity amplify the selection effect on the birth-death process and suppress the selection effect on the death-birth process. Based on the above results, an effective algorithm is proposed to generate selection adjusters with desired size and average degree.

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The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan

While studies of aging are widely framed in terms of their demarcation of degenerative processes, the brain provides a unique opportunity to uncover the adaptive effects of getting older. Though intuitively reasonable, that life-experience and wisdom should reside somewhere in human cortex, these features have eluded neuroscientific explanation. The present study utilizes a “Bayesian Brain” framework to motivate an analysis of cortical circuit processing. From a Bayesian perspective, the brain represents a model of its environment and offers predictions about the world, while responding, through changing synaptic strengths to novel interactions and experiences. We hypothesized that these predictive and updating processes are modified as we age, representing an optimization of neuronal architecture. Using novel sensory stimuli we demonstrate that synaptic connections of older brains resist trial by trial learning to provide a robust model of their sensory environment. These older brains are capable of processing a wider range of sensory inputs – representing experienced generalists. We thus explain how, contrary to a singularly degenerative point-of-view, aging neurobiological effects may be understood, in sanguine terms, as adaptive and useful.

 

Moran RJ, Symmonds M, Dolan RJ, Friston KJ (2014) The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan. PLoS Comput Biol 10(1): e1003422. http://dx.doi.org/10.1371/journal.pcbi.1003422


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Spatio-Temporal Dynamics in Collective Frog Choruses Examined by Mathematical Modeling and Field Observations : Scientific Reports : Nature Publishing Group

Spatio-Temporal Dynamics in Collective Frog Choruses Examined by Mathematical Modeling and Field Observations : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
This paper reports theoretical and experimental studies on spatio-temporal dynamics in the choruses of male Japanese tree frogs. First, we theoretically model their calling times and positions as a system of coupled mobile oscillators.

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Sums of variables at the onset of chaos

Sums of variables at the onset of chaos | Complex Networks Everywhere | Scoop.it

We explain how specific dynamical properties give rise to the limit distribution of sums of deterministic variables at the transition to chaos via the period-doubling route. We study the sums of successive positions generatedby an ensemble of initial conditions uniformly distributed in the entire phase space of a unimodal map as represented by the logistic map. We find that these sums acquire their salient, multiscale, features from the repellor preimage structure that dominates the dynamics toward the attractors along the period-doubling cascade. And we explain how these properties transmit from the sums to their distribution. Specifically, we show how the stationary distribution of sums of positions at the Feigebaum point is built up from those associated with the supercycle attractors forming a hierarchical structure with multifractal and discrete scale invariance properties.


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Minsky's Creators Talk Economic Modeling, Bitcoin, and Chaos Theory

Minsky's Creators Talk Economic Modeling, Bitcoin, and Chaos Theory | Complex Networks Everywhere | Scoop.it

The software's designers talked with SourceForge about the challenges of simulating economic models, especially in turbulent times.


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▶ On the Nature of Causality in Complex Systems, George F.R. Ellis - YouTube

Big Bang cosmology, chemical and biological evolutionary theory, and associated sciences have been extraordinarily successful in revealing and enabling us to understand the development of the universe from the Planck era to the present, as well as the emergence of complexity, life, and consciousness here on Earth. After briefly sketching this amazing story, and the key characteristics of nature, this paper will reflect on the different types and levels of causality involved -- stressing the important and pervasive role of highly differentiated and dynamic relationships and networks of relationships. Philosophical considerations build on and enrich scientific ones to probe these relationships. They also take us beyond the limits of strictly scientific methodology to consider and model -- however inadequately -- the ultimate sources of existence and order. This is the issue of creation, which introduces another very different -- and transcendent -- level of causality. We show that this is compatible with the -- and even essential to -- the causalities operative in nature, including those of quantum cosmology, if we acknowledge the limits of physics.

This lecture was delivered by George Ellis during the 16th Kraków Methodological Conference "The Causal Universe", May 17-18, 2012.


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António F Fonseca's curator insight, January 25, 2014 5:57 AM

Very interesting from the philosophical point of view.

Zaphod Beeblebrox's curator insight, January 30, 2014 1:39 PM

This really brings up the question - what is the nature of choice inherent in the universe?  If causality defines itself as the operation of a cause onto a single, definable effect, then how does it relate to the POSSIBILITES that exist as a consequence of probability and human limitation?  In other words, can physics be the perpatrator of - and therefore the potential predictor of - what can be viewed as choice as an inexorable consequence of the surrounding conditions?

Eli Levine's curator insight, March 25, 2014 10:59 PM

I've said it several times before.

 

It's going to take a change in the logic of politics, a different program, as it were, to operate and produce a new base level of hardware.  We are bound by some of the lower levels of physics, biology and psychology and the realities of the economic market.  These are the lower level, mechanistic laws that have to be obeyed first, in order to realize what ought to be a common goal of leading relatively happy, prosperous, sustainable and resilient lives.

 

But the politics, by engaging in a different logic that's not meant to benefit only the well to do, will ultimately save itself from collapse and destruction (ironically, the big goal for conservatives, who are so keen on implementing these boot-licking, elite worshipping and poor-punishing programs and policies) and produce a new effect from the established lower level laws that could, potentially, mitigate against major economic and social collapses that, ultimately, ruins the politics as well.

 

Way cool stuff here.  Very relevant for government and governing policy.

 

Think about it.

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The Metastable Brain

Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or “bound” together when people attend to a stimulus, perceive, think, and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales, suggesting that metastable dynamics underlie the real-time coordination necessary for the brain’s dynamic cognitive, behavioral, and social functions.


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