Complexity Sciences
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The Future's Arriving Sooner Than You Think. Here’s How to Get Ready.

The Future's Arriving Sooner Than You Think. Here’s How to Get Ready. | Complexity Sciences | Scoop.it
With the exception of mathematicians, we humans can’t handle big numbers. The blame falls squarely on our ancestors. To survive, our knuckle-dragging ancient forebears needed to count how many people ...
Yaser Helmy's insight:
The line on the logarithmic scale is actually curved. Of course I am talking about technological growth in the network economy.
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Hidden scaling patterns and universality in written communication

Hidden scaling patterns and universality in written communication | Complexity Sciences | Scoop.it
The temporal statistics exhibited by written correspondence appear to be media dependent, with features which have so far proven difficult to characterize. We explain the origin of these difficulties by disentangling the role of spontaneous activity from decision-based prioritizing processes in human dynamics, clocking all waiting times through each agent's ``proper time'' measured by activity. This unveils the same fundamental patterns in written communication across all media (letters, email, sms), with response times displaying truncated power-law behavior and average exponents near $$-${}\frac{3}{2}$. When standard time is used, the response time probabilities are theoretically predicted to exhibit a bimodal character, which is empirically borne out by our newly collected years-long data on email. These perspectives on the temporal dynamics of human correspondence should aid in the analysis of interaction phenomena in general, including resource management, optimal pricing and routing, information sharing, and emergency handling.

Via Claudia Mihai
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The Beautiful Phenomena Of Starling Flocks, Explained By Computers

The Beautiful Phenomena Of Starling Flocks, Explained By Computers | Complexity Sciences | Scoop.it

On its own, a single starling doesn't elicit much fuss. It's a tennis-ball-sized bird, glossy black in winter, purplish or green in summer, and in autumn, sometimes speckled with white spots. But when starlings congregate in flocks of hundreds of thousands over open fields (something scientists call a "murmuration") they pitch and arc and rush at one another in a bizarre choreography that's puzzled naturalists for hundreds of years. A whole catalog of YouTube videos has documented the black shapes in flight, a movement that looks like the birds are attached to a giant rhythmic gymnast's invisible ribbon.


Via Claudia Mihai
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A mathematical theory proposed by Alan Turing in 1952 can explain the formation of fingers

A mathematical theory proposed by Alan Turing in 1952 can explain the formation of fingers | Complexity Sciences | Scoop.it

Alan Turing, the British mathematician (1912-1954), is famous for a number of breakthroughs, which altered the course of the 20th century. In 1936 he published a paper, which laid the foundation of computer science, providing the first formal concept of a computer algorithm. He next played a pivotal role in the Second World War, designing the machines which cracked the German military codes, enabling the Allies to defeat the Nazis in several crucial battles. And in the late 1940's he turned his attention to artificial intelligence and proposed a challenge, now called the Turing test, which is still important to the field today.

His contribution to mathematical biology is less famous, but was no less profound. He published just one paper (1952), but it triggered a whole new field of mathematical enquiry into pattern formation. He discovered that a system with just 2 molecules could, at least in theory, create spotty or stripy patterns if they diffused and chemically interacted in just the right way.

His mathematical equations showed that starting from uniform condition (ie. a homogeneous distribution – no pattern) they could spontaneously self-organise their concentrations into a repetitive spatial pattern. This theory has come to be accepted as an explanation of fairly simple patterns such as zebra stripes and even the ridges on sand dunes, but in embryology it has been resisted for decades as an explanation of how structures such as fingers are formed.

Now a group of researchers from the Multicellular Systems Biology lab at the CRG, led by ICREA Research Professor James Sharpe, has provided the long sought-for data which confirms that the fingers and toes are patterned by a Turing mechanism. "It complements their recent paper (Science 338:1476, 2012), which provided evidence that Hox genes and FGF signaling modulated a hypothetical Turing system. However, at that point the Turing molecules themselves were still not identified, and so this remained as the critical unsolved piece of the puzzle. The new study completes the picture, by revealing which signaling molecules act as the Turing system" says James Sharpe, co-author of the study.


Via Claudia Mihai
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A simple generative model of collective online behavior

A simple generative model of collective online behavior | Complexity Sciences | Scoop.it

One of the most common strategies in studying complex systems is to investigate and interpret whether any “hidden order” is present by fitting observed statistical regularities via data analysis and then reproducing such regularities with long-time or equilibrium dynamics from some generative model. Unfortunately, many different models can possess indistinguishable long-time dynamics, so the above recipe is often insufficient to discern the relative quality of competing models. In this paper, we use the example of collective online behavior to illustrate that, by contrast, time-dependent modeling can be very effective at disentangling competing generative models of a complex system.


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Social Network Analysis of the Islamist Terrorist Network

Social Network Analysis of the Islamist Terrorist Network | Complexity Sciences | Scoop.it
Social Network Analysis of the Islamist Terrorist Network

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Forget The Wisdom of Crowds; Neurobiologists Re...

Forget The Wisdom of Crowds; Neurobiologists Re... | Complexity Sciences | Scoop.it
The wisdom of crowds breaks down when people are biased. Now researchers have discovered a simple method of removing this bias–just listen to the most confident.
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The Only Probability Cheatsheet You'll Ever Need

The Only Probability Cheatsheet You'll Ever Need | Complexity Sciences | Scoop.it
Handy resource for #datascience : a super-condensed probability cheat sheet http://t.co/BdcgkAdgpi

Via Claudia Mihai
Yaser Helmy's insight:

Although I have been a practicing data scientist for years now, I have actually understood some concepts from this sheet!

 

Loved it.

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The Curious Nature of Sharing Cascades on Facebook

The Curious Nature of Sharing Cascades on Facebook | Complexity Sciences | Scoop.it
“ Most content on Facebook is shared a few times but some can be shared millions of times. Now computer scientists are beginning to understand the difference.”
Via Claudia Mihai
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Ancient food webs developed modern structure soon after mass extinction

Ancient food webs developed modern structure soon after mass extinction | Complexity Sciences | Scoop.it
“ Researchers from the Santa Fe Institute and the Smithsonian Institution have pieced together a highly detailed picture of feeding relationships among 700 mammal, bird, reptile, fish, insect, and plant species from a 48 million year old lake and forest ecosystem.”Their analysis of fossilized remains from the Messel deposit near Frankfurt, Germany, provides the most compelling evidence to date that ancient food webs were organized much like modern food webs. Their paper describing the research appears online and open access this week in Proceedings of the Royal Society B: Biological Sciences.
Via Claudia Mihai
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Data Mining Reveals How Conspiracy Theories Emerge on Facebook

Data Mining Reveals How Conspiracy Theories Emerge on Facebook | Complexity Sciences | Scoop.it
“Some people are more susceptible to conspiracy theories than others, say computational social scientists who have studied how false ideas jump the “credulity barrier” on Facebook.”Conspiracy theories seem to come about by a process in which ordinary satirical commentary or obviously false content somehow jumps the credulity barrier. And that seems to happen through groups of people who deliberately expose themselves to alternative sources of news.
Via Claudia Mihai, Complexity Digest
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Resilience of Natural Gas Networks during Conflicts, Crises and Disruptions

Resilience of Natural Gas Networks during Conflicts, Crises and Disruptions | Complexity Sciences | Scoop.it

Human conflict, geopolitical crises, terrorist attacks, and natural disasters can turn large parts of energy distribution networks offline. Europe's current gas supply network is largely dependent on deliveries from Russia and North Africa, creating vulnerabilities to social and political instabilities. During crises, less delivery may mean greater congestion, as the pipeline network is used in ways it has not been designed for. Given the importance of the security of natural gas supply, we develop a model to handle network congestion on various geographical scales. We offer a resilient response strategy to energy shortages and quantify its effectiveness for a variety of relevant scenarios. In essence, Europe's gas supply can be made robust even to major supply disruptions, if a fair distribution strategy is applied.

 


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Netlytic - Making sense of online conversations

Netlytic - Making sense of online conversations | Complexity Sciences | Scoop.it
"Netlytic is a cloud-based text and social networks analyzer that can automatically summarize large volumes of text and discover social networks from online conversations on social media sites such as Twitter, Youtube, blogs, online forums and chats. "
Via João Greno Brogueira
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The dynamics of correlated novelties

The dynamics of correlated novelties | Complexity Sciences | Scoop.it
Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called “expanding the adjacent possible”. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.

Via Claudia Mihai
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Competitive Dynamics on Complex Networks

Competitive Dynamics on Complex Networks | Complexity Sciences | Scoop.it
We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.

Via Claudia Mihai
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How bird flocks are like liquid helium

How bird flocks are like liquid helium | Complexity Sciences | Scoop.it

Mathematical model shows how hundreds of starlings coordinate their movements in flight.

A flock of starlings flies as one, a spectacular display in which each bird flits about as if in a well-choreographed dance. Everyone seems to know exactly when and where to turn. Now, for the first time, researchers have measured how that knowledge moves through the flock—a behavior that mirrors certain quantum phenomena of liquid helium.


Via Claudia Mihai
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A network framework of cultural history

A network framework of cultural history | Complexity Sciences | Scoop.it

The emergent processes driving cultural history are a product of complex interactions among large numbers of individuals, determined by difficult-to-quantify historical conditions. To characterize these processes, we have reconstructed aggregate intellectual mobility over two millennia through the birth and death locations of more than 150,000 notable individuals. The tools of network and complexity theory were then used to identify characteristic statistical patterns and determine the cultural and historical relevance of deviations. The resulting network of locations provides a macroscopic perspective of cultural history, which helps us to retrace cultural narratives of Europe and North America using large-scale visualization and quantitative dynamical tools and to derive historical trends of cultural centers beyond the scope of specific events or narrow time intervals.


Via Claudia Mihai
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Exploring No Man’s Sky, A Computer Game Forged by Algorithms

Exploring No Man’s Sky, A Computer Game Forged by Algorithms | Complexity Sciences | Scoop.it
A new computer game, No Man’s Sky, demonstrates a new way to build computer games filled with diverse flora and fauna.

Sean Murray, one of the creators of the computer game No Man’s Sky, can’t guarantee that the virtual universe he is building is infinite, but he’s certain that, if it isn’t, nobody will ever find out. “If you were to visit one virtual planet every second,” he says, “then our own sun will have died before you’d have seen them all.”

No Man’s Sky is a video game quite unlike any other. Developed for Sony’s PlayStation 4 by an improbably small team (the original four-person crew has grown only to 10 in recent months) at Hello Games, an independent studio in the south of England, it’s a game that presents a traversable universe in which every rock, flower, tree, creature, and planet has been “procedurally generated” to create a vast and diverse play area.


Via Claudia Mihai
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▶ Jeffrey Johnson: From networks to hypernetworks in complex systems science

Complex systems have multilevel dynamics emerging from interactions between their parts. Networks have provided deep insights into those dynamics, but only represent relations between two things while the generality is relations between many things. Hypergraphs and their related Galois connections have long been used to model such relations, but their set theoretic nature has inadequate and inappropriate structure. Simplicial complexes can better represent relations between many things but they too have limitations. Hypersimplices, which are defined as simplices in which the relational structure is explicit, overcome these limitations. Hypernetworks, which in the simplest cases are sets of hypersimplices, have a multidimensional connectivity structure which constrains those dynamics represented by patterns of numbers over the hypersimplices and their vertices. The dynamics of hypernetwork also involve the formation and disintegration of hypersimplices, which are seen as structural events related to system time. Hypernetworks provide algebraic structure able to represent multilevel systems and combine their top-down and bottom-up micro, meso and macro-dynamics. Hypernetworks naturally generalise graphs, hypergraphs and networks. These ideas will be presented in a graphical way through examples which also show the relevance of hypernetworks to policy. It will be argued that hypernetworks are necessary if not sufficient for a science of complex systems and its applications. The talk will be aimed at a general audience and no prior knowledge will be assumed.

 

10th ECCO / GBI seminar series. Spring 2014

From networks to hypernetworks in complex systems science

April 18, 2014, Brussels

Jeffrey Johnson Open University, UK

Slides, references and more: http://ecco.vub.ac.be/?q=node/231 


Via Complexity Digest, Eugene Ch'ng
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Liz Rykert's curator insight, May 10, 2014 9:32 PM

I am fascinated with the role of networks in complex systems as the scaffolds that connect and conduct.  

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Detecting Communities Based on Network Topology

Detecting Communities Based on Network Topology | Complexity Sciences | Scoop.it

Network methods have had profound influence in many domains and disciplines in the past decade. Community structure is a very important property of complex networks, but the accurate definition of a community remains an open problem. Here we defined community based on three properties, and then propose a simple and novel framework to detect communities based on network topology. We analyzed 16 different types of networks, and compared our partitions with Infomap, LPA, Fastgreedy and Walktrap, which are popular algorithms for community detection. Most of the partitions generated using our approach compare favorably to those generated by these other algorithms. Furthermore, we define overlapping nodes that combine community structure with shortest paths. We also analyzed the E. Coli. transcriptional regulatory network in detail, and identified modules with strong functional coherence.

  
Via Ashish Umre
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Ivon Prefontaine's curator insight, July 26, 2014 6:54 PM

Community is a more complex and organic organizing than teams. Teams are inherently hierarchical with predetermined goals. Communities are fluid and the goals are continuously being negotiated. Schools and classrooms are better served to be thought of as communities with overlapping qualities and permeable boundaries with other communities.

Eli Levine's curator insight, July 29, 2014 6:42 PM

A useful tool for policy making, because it helps identify communities and how they interact to form super-communities.

 

The essence of mapping the polity and the public, socially, economically, technologically, and infrastrucutrally.

 

Think about it.

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Control Profiles of Complex Networks

Control Profiles of Complex Networks | Complexity Sciences | Scoop.it
Studying the control properties of complex networks provides insight into how designers and engineers can influence these systems to achieve a desired behavior. Topology of a network has been shown to strongly correlate with certain control properties; here we uncover the fundamental structures that explain the basis of this correlation. We develop the control profile, a statistic that quantifies the different proportions of control-inducing structures present in a network. We find that standard random network models do not reproduce the kinds of control profiles that are observed in real-world networks. The profiles of real networks form three well-defined clusters that provide insight into the high-level organization and function of complex systems.
Via Claudia Mihai
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Correlations and Scaling Laws in Human Mobility

Correlations and Scaling Laws in Human Mobility | Complexity Sciences | 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.
Via Bernard Ryefield, A. J. Alvarez-Socorro
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The Bursty Dynamics of the Twitter Information Network

In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure. These bursts transform users' networks of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.The Bursty Dynamics of the Twitter Information NetworkSeth A. Myers, Jure Leskovechttp://arxiv.org/abs/1403.2732
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Ancient food webs developed modern structure soon after mass extinction

Ancient food webs developed modern structure soon after mass extinction | Complexity Sciences | Scoop.it
“ Researchers from the Santa Fe Institute and the Smithsonian Institution have pieced together a highly detailed picture of feeding relationships among 700 mammal, bird, reptile, fish, insect, and plant species from a 48 million year old lake and forest ecosystem.”Their analysis of fossilized remains from the Messel deposit near Frankfurt, Germany, provides the most compelling evidence to date that ancient food webs were organized much like modern food webs. Their paper describing the research appears online and open access this week in Proceedings of the Royal Society B: Biological Sciences.
Via Claudia Mihai
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The Simple Rules of Social Contagion

The Simple Rules of Social Contagion | Complexity Sciences | 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.”
Via Claudia Mihai, ukituki
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