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The Learning Environment as a Chaotic and Complex Adaptive System

Thrivability is a novel concept describing the intention to go beyond sustainability, allowing a system to flourish. For a society or organization to be thrivable, educated, responsible acting agents are needed. Traditional education focuses on (efficient) reproduction of existing organised bodies of information. We argue that complex adaptive systems theory and chaos theory provide concepts well suited to inform the design of learning environments, in order to facilitate a thrivable organization. This learning is not linear and externally controlled, but happens in a chaotic, yet guided manner. After discussing the suitability of the theoretical body of these general approaches, we show how a concrete progressive education approach, called the Dalton-Plan pedagogy, implements and supports these elements. By doing so, we show that the Dalton-Plan pedagogy is well suited for education of agents working in and for thrivable organizations. Support for teachers as part of this evolving learning system is provided by an e-learning environment.

Bernard Ryefield's insight:

from http://gfbertini.wordpress.com/2013/09/03/the-learning-environment-as-a-chaotic-and-complex-adaptive-system/

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

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided.

by A. James O'Malley, Jukka-Pekka Onnela

arXiv:1404.0067 [physics.soc-ph]


Via NESS, Complejidady Economía
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Eli Levine's curator insight, Today, 3:08 PM

A very cool and comprehensive look at how networks can be analyzed, studied and examined.

 

Way cool science!

 

Think about it.

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Pattern and Process | Spatial Simulation: Exploring Pattern and Process

Pattern and Process | Spatial Simulation: Exploring Pattern and Process | Complexity - Complex Systems Theory | Scoop.it

Across broad areas of the environmental and social sciences, simulation models are an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement of those more conventional approaches.  The contemporary research literature is teeming with abstract simulation models whose presentation is mathematically demanding and requires a high level of knowledge of quantitative and computational methods and approaches.  Furthermore, simulation models designed to represent specific systems and phenomena are often complicated, and, as a result, difficult to reconstruct from their descriptions in the literature.  Spatial Simulation: Exploring Pattern and Process aims to provide a practical and accessible account of dynamic spatial modelling, while also equipping readers with a sound conceptual foundation in the subject, and a useful introduction to the wide-ranging literature.

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A signature of power law network dynamics

Can one hear the 'sound' of a growing network? We address the problem of recognizing the topology of evolving biological or social networks. Starting from percolation theory, we analytically prove a linear inverse relationship between two simple graph parameters--the logarithm of the average cluster size and logarithm of the ratio of the edges of the graph to the theoretically maximum number of edges for that graph--that holds for all growing power law graphs. The result establishes a novel property of evolving power-law networks in the asymptotic limit of network size. Numerical simulations as well as fitting to real-world citation co-authorship networks demonstrate that the result holds for networks of finite sizes, and provides a convenient measure of the extent to which an evolving family of networks belongs to the same power-law class.

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Center for Complex Networks and Systems Research

Center for Complex Networks and Systems Research | Complexity - Complex Systems Theory | Scoop.it

Via Bryan Knowles
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Swarm Intelligence Based Algorithms: A Critical Analysis

Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic systems, self-organization and Markov chain framework. Finally, we provide some discussions and topics for further research.

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The Strange New Science of Chaos - YouTube

A 1989 program, with Lorenz
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Eli Levine's curator insight, March 31, 12:08 PM

I <3 Science.

It just keeps learning more and more about the universe, ourselves and ourselves within the universe.

It doesn't stop, until we stop.

 

The lessons that are discussed here are applicable to our social sciences and questions of governance, especially the non-linear nature of society, economy and social psychology and the importance of initial conditions.

 

It's not a stable universe.

 

And we're living and apart of the instability!

 

Think about it.

Vasileios Basios's curator insight, April 1, 6:43 AM

Wow! such a rare delightful material .... Ralph Abraham and Lorenz who could imagine!

Luciano Lampi's curator insight, Today, 5:31 AM

to be watched by the new generations!  old certitudes and new doubts?

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Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory

Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory | Complexity - Complex Systems Theory | Scoop.it

Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.

 

 

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The story of the Gömböc | plus.maths.org

The story of the Gömböc | plus.maths.org | Complexity - Complex Systems Theory | Scoop.it

A Gömböc is a strange thing. It looks like an egg with sharp edges, and when you put it down it starts wriggling and rolling around with an apparent will of its own. Until quite recently, no-one knew whether Gömböcs even existed. Even now, Gábor Domokos, one of their discoverers, reckons that in some sense they barely exists at all. So what are Gömböcs and what makes them special?

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Why Model? Joshua M. Epstein

Why Model? Joshua M. Epstein | Complexity - Complex Systems Theory | Scoop.it

This lecture treats some enduring misconceptions about modeling. One of these is that the goal is always prediction. The lecture distinguishes between explanation and prediction as modeling goals, and offers sixteen reasons other than prediction to build a model. It also challenges the common assumption that scientific theories arise from and 'summarize' data, when often, theories precede and guide data collection; without theory, in other words, it is not clear what data to collect. Among other things, it also argues that the modeling enterprise enforces habits of mind essential to freedom. It is based on the author's 2008 Bastille Day keynote address to the Second World Congress on Social Simulation, George Mason University, and earlier addresses at the Institute of Medicine, the University of Michigan, and the Santa Fe Institute.

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António F Fonseca's curator insight, March 23, 2:20 AM

The classical paper about modelling and simulation. Very clear.

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Power-law distributions in binned empirical data

Many man-made and natural phenomena, including the intensity of earthquakes, population of cities, and size of international wars, are believed to follow power-law distributions. The accurate identification of power-law patterns has significant consequences for developing an understanding of complex systems. However, statistical evidence for or against the power-law hypothesis is complicated by large fluctuations in the empirical distribution's tail, and these are worsened when information is lost from binning the data. We adapt the statistically principled framework for testing the power-law hypothesis, developed by Clauset, Shalizi and Newman, to the case of binned data. This approach includes maximum-likelihood fitting, a hypothesis test based on the Kolmogorov-Smirnov goodness-of-fit statistic and likelihood ratio tests for comparing against alternative explanations. We evaluate the effectiveness of these methods on synthetic binned data with known structure and apply them to twelve real-world binned data sets with heavy-tailed patterns.
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Scaling and Predictability in Stock Markets: A Comparative Study

Scaling and Predictability in Stock Markets: A Comparative Study | Complexity - Complex Systems Theory | Scoop.it

Most people who invest in stock markets want to be rich, thus, many technical methods have been created to beat the market. If one knows the predictability of the price series in different markets, it would be easier for him/her to make the technical analysis, at least to some extent. Here we use one of the most basic sold-and-bought trading strategies to establish the profit landscape, and then calculate the parameters to characterize the strength of predictability. According to the analysis of scaling of the profit landscape, we find that the Chinese individual stocks are harder to predict than US ones, and the individual stocks are harder to predict than indexes in both Chinese stock market and US stock market. Since the Chinese (US) stock market is a representative of emerging (developed) markets, our comparative study on the markets of these two countries is of potential value not only for conducting technical analysis, but also for understanding physical mechanisms of different kinds of markets in terms of scaling.

 

 

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Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System

Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System | Complexity - Complex Systems Theory | Scoop.it

Social systems have recently attracted much attention, with attempts to understand social behavior with the aid of statistical mechanics applied to complex systems. Collective properties of such systems emerge from couplings between components, for example, individual persons, transportation nodes such as airports or subway stations, and administrative districts. Among various collective properties, criticality is known as a characteristic property of a complex system, which helps the systems to respond flexibly to external perturbations. This work considers the criticality of the urban transportation system entailed in the massive smart card data on the Seoul transportation network. Analyzing the passenger flow on the Seoul bus system during one week, we find explicit power-law correlations in the system, that is, power-law behavior of the strength correlation function of bus stops and verify scale invariance of the strength fluctuations. Such criticality is probed by means of the scaling and renormalization analysis of the modified gravity model applied to the system. Here a group of nearby (bare) bus stops are transformed into a (renormalized) “block stop” and the scaling relations of the network density turn out to be closely related to the fractal dimensions of the system, revealing the underlying structure. Specifically, the resulting renormalized values of the gravity exponent and of the Hill coefficient give a good description of the Seoul bus system: The former measures the characteristic dimensionality of the network whereas the latter reflects the coupling between distinct transportation modes. It is thus demonstrated that such ideas of physics as scaling and renormalization can be applied successfully to social phenomena exemplified by the passenger flow.

 

 

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PLOS ONE Nonlinear dynamics articles

PLOS ONE Nonlinear dynamics articles | Complexity - Complex Systems Theory | Scoop.it

PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.


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Complex Thinking for a Complex World – About Reductionism, Disjunction and Systemism | Morin | Systema: connecting matter, life, culture and technology

Complex Thinking for a Complex World – About Reductionism, Disjunction and Systemism | Morin | Systema: connecting matter, life, culture and technology | Complexity - Complex Systems Theory | Scoop.it

This article is based on the keynote address presented to the European Meetings on Cybernetics and Systems Research (EMCSR) in 2012, on the occasion of Edgar Morin receiving the Bertalanffy Prize in Complexity Thinking, awarded by the Bertalanffy Centre for the Study of Systems Science(BCSSS).

The following theses will be elaborated on: (a) The whole is at the same time more and less than its parts; (b) We must abandon the term "object" for systems because all the objects are systems and parts of systems; (c) System and organization are the two faces of the same reality; (d) Eco-systems illustrate self-organization.

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Arrogant physicists — do they think economics is easy?

Arrogant physicists — do they think economics is easy? | Complexity - Complex Systems Theory | Scoop.it
Arrogant physicists — do they think economics is easy?No. But their ideas can help improve economics, and here’s why

 

 

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Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World

Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player's wealth at every point in time, but also all actions of every player over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in western countries. In particular we find an approximate exponential for low wealth and a power-law tail. The Gini index is found to be 0.65, which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degree in the trade network, relatively low nearest-neighbor degree and a low clustering coefficient. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. We find that players that are not organized within social groups with at least three members are significantly poorer on average. We observe that high `political' status and high wealth go hand in hand. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies.

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Eli Levine's curator insight, April 5, 7:53 AM

When you let laissez-faire take its course, only a few individuals really end up on top.  That's not to say that markets shouldn't be allowed and enabled to exist, for the sake of the free exchange of goods, services, knowledge, wealth, etc.  It is saying that we need non-intrusive mechanisms to help make sure that the wealth that is produced is enjoyed by everyone who produced it.

 

Some people will always have more than others, for behavioral reasons and for circumstantial reasons.  That is not a problem, in my own view.  The problem comes, for me, when their focus on wealth becomes so great that they lose sight of their human needs on the individual as well as social and environmental levels, such that they choose wealth that they will not use over that which they need for survival and physical/psychological well being.

 

It's a form of being disconnected with the real world, kind of like schizophrenia.  The brain isn't functioning properly when  greed is and has taken over, for one reason or another.  It should be considered a mental illness that we could, potentially in time, treat, such that these individuals who are not aware and do not care to be aware of their actual place in the universe can lead normal, happy, healthy and appropriately placed lives in our societies.

 

So, we're left with the present situation in which work is undervalued, relative to what it produces, while executive management is way overvalued relative to its healthy role in the economy and society.  I'm not saying that pure equality is desirable, because sometimes people do work harder than others and deserve a greater share of wealth than someone who didn't work when they honestly could have.  What I'm saying, is that indulging the elite's fantasy of the ego is detrimental to themselves and to others, and that I don't think it should be accepted or tolerated within our social world.

 

If you want equality of opportunities, you must have more equality of outcomes.  That is yet another fact about our world that conservatives fail to accept and appreciate, if they're attempting to realize a world in which we are all together as one, rather than a world where we are heavily stratified according to an artificial hierarchy.  That is the difference between a conservative and a progressive.  One wants us all to be living together in peace, harmony, stability and, for want of a better word, love, while the other just wants everyone in a specific place according to birth.  One promotes democracy and inclusivity, the other, discourages it.  One works better for humanity on the tangible level, the other, does not.

 

And it's just a difference in brain type/values that makes them be something so antithetical to what Western civilization has stood for.

 

Think about it.

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Dynamical Systems on Networks: A Tutorial

We give a tutorial for the study of dynamical systems on networks, and we focus in particular on ``simple" situations that are tractable analytically. We briefly motivate why examining dynamical systems on networks is interesting and important. We then give several fascinating examples and discuss some theoretical results. We also discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give our outlook on the field.

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Generalized friendship paradox in complex networks: The case of scientific collaboration

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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Using Complex Networks to Characterize International Business Cycles

Using Complex Networks to Characterize International Business Cycles | Complexity - Complex Systems Theory | Scoop.it
Background


There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles.

Methodology/Principal Findings


We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries’ GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples.

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Eli Levine's curator insight, March 29, 12:13 PM

These are the natural laws and connections which exist amongst various economies and within each economy.  This shows the interconnectedness of the whole planet's economy and can give predictions as to what could happen if one particular economy were to crash and fall into valuelessness for humanity.

 

It's interesting that this research comes at a time in our history when the natural laws of social interactions are being violated by governments and elite groups everywhere.  What will happen if discontent turns into unrest and rebellions in the United States?  What happens if the authority of governments ceases to be legitimate, to the point where violence and anarchy take their place.  What will happen to the economy if the rule of law is no longer abided, and the mob takes over to deal with the perceived injustices that the elite groups have committed against the general public?

 

What happens when the environment gives way and our societies are no longer able to support the populations that are present?  What happens when people are forced to either starve or fight?

 

That's the direction that we're headed towards, I'm afraid. 

Funny how it is that the conservatives from all parties who enacted these policies, are leading to the very destruction of society that they're so afraid of.  Funny how it is that things get more delicate and likely to change significantly as they cling to their image of how the past was (and it is just an image of the past, not the real world as it was, is or will be).

 

Silly brains.

 

Think about it.

António F Fonseca's curator insight, March 31, 3:34 AM

Crisis transmission, lookout for USA, Ireland and Spain!

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Complex networks analysis in socioeconomic models (v1) UPDATED (see link)

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.

Bernard Ryefield's insight:

updated  to v2 : http://arxiv.org/pdf/1403.6767v2

 

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Signals and Boundaries: Building Blocks for Complex Adaptive Systems (by John H. Holland)

Signals and Boundaries: Building Blocks for Complex Adaptive Systems

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Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other cas. Despite a wealth of data and descriptions concerning different cas, there remain many unanswered questions about "steering" these systems. In Signals and Boundaries, John Holland argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering cas through the mechanisms that generate their signal/boundary hierarchies.

Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.


Via Complexity Digest, António F Fonseca
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Costas Bouyioukos's curator insight, March 18, 10:41 AM

John Holland's new book!

António F Fonseca's curator insight, March 23, 2:23 AM

Why communicate, why not, for example, just command?

june holley's curator insight, March 23, 4:43 AM

Just got this. His stuff is usually excellent so I have high hopes.

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Nasa-funded study: industrial civilisation headed for 'irreversible collapse'?

Nasa-funded study: industrial civilisation headed for 'irreversible collapse'? | Complexity - Complex Systems Theory | Scoop.it

Nafeez Ahmed: Natural and social scientists develop new model of how 'perfect storm' of crises could unravel global system

 

 

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Predictability of extreme events in social media

It is part of our daily social-media experience that seemingly ordinary items (videos, news, publications, etc.) unexpectedly gain an enormous amount of attention. Here we investigate how unexpected these events are. We propose a method that, given some information on the items, quantifies the predictability of events, i.e., the potential of identifying in advance the most successful items defined as the upper bound for the quality of any prediction based on the same information. Applying this method to different data, ranging from views in YouTube videos to posts in Usenet discussion groups, we invariantly find that the predictability increases for the most extreme events. This indicates that, despite the inherently stochastic collective dynamics of users, efficient prediction is possible for the most extreme events.
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Complexity Science and the concept of "Social Cognition"

Complexity Science and the concept of "Social Cognition" | Complexity - Complex Systems Theory | Scoop.it
Activities such as distributed collaboration are becoming more common as organizations become geographically diverse and they have important consequences when the collective group makes important decisions.

Via Roger D. Jones, PhD
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Kaya, l'équation qui calcule l'avenir de l'humanité

Kaya, l'équation qui calcule l'avenir de l'humanité | Complexity - Complex Systems Theory | Scoop.it
Comprendre l'équation de Kaya, qui permet de dynamiser les rapports entre les composantes d'un écosystème, est primordial pour penser l'avenir de l'espèce.
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