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Rescooped by António F Fonseca from Network and Graph Theory
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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.


Via Bernard Ryefield
António F Fonseca's insight:

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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Homo Narrativus and the Trouble with Fame

Homo Narrativus and the Trouble with Fame | Aggregate Intelligence | Scoop.it
Our understanding of fame is critical to how we see each other and our society. But it is also badly wrong. Let me tell you why. We…

Via Jorge Louçã, NESS
António F Fonseca's insight:

A very interesting article confirming my own PhD thesis - fame is more dependent on information propagation than on individual qualities.

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Rescooped by António F Fonseca from Papers
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Information: A Personal Synthesis

This article is an attempt to capture, in a reasonable space, some of the major developments and currents of thought in information theory and the relations between them. I have particularly tried to include changes in the views of key authors in the field. The domains addressed range from mathematical-categorial, philosophical and computational approaches to systems, causal-compositional, biological and religious approaches and messaging theory. I have related key concepts in each domain to my non-standard extension of logic to real processes that I call Logic in Reality (LIR). The result is not another attempt at a General Theory of Information such as that of Burgin, or a Unified Theory of Information like that of Hofkirchner. It is not a compendium of papers presented at a conference, more or less unified around a particular theme. It is rather a highly personal, limited synthesis which nonetheless may facilitate comparison of insights, including contradictory ones, from different lines of inquiry. As such, it may be an example of the concept proposed by Marijuan, still little developed, of the recombination of knowledge. Like the best of the work to which it refers, the finality of this synthesis is the possible contribution that an improved understanding of the nature and dynamics of information may make to the ethical development of the information society.

 

Information: A Personal Synthesis
by Joseph Brenner
Information 2014, 5(1), 134-170; doi:10.3390/info5010134
http://www.mdpi.com/2078-2489/5/1/134/ ;


Via Complexity Digest
António F Fonseca's insight:

Brenner and Daniel Cohnitz have a very good book about the subject "Information and Information Flow" that covers almost all aspects of Information Theory. Unfortunatelly the 'Matecmatical Information Theory' of Jan Kahre didn't have yet the same attention.

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Eli Levine's curator insight, April 11, 10:57 AM

All information that we receive from the universe that is around us is second hand.  It is possible to alter and shift them out of our own volition or of the volition of someone else, provided that we're either caught unawares or allowing it to happen just as it is theoretically possible to shift the universe around us, so that we experience something different than what would ordinarily happen (again, only theoretically, not necessarily in actuality).  The universe is out there, I think, just as we're most certainly apart of it.  There are laws to this place as well which influence and effect our abilities to act, our perception of the choices that we have and the choices that we actually are left with at the end of the day, when all's said and told.  We are just receptors, analyzers and synthesizers of information with our biological bodies.  We are all slaves, ultimately, to our biology, our circumstances and the consequences of our actions.

 

Just my two cents on information.

 

Think about it.

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The Informative Herd: why humans and other animals imitate more when conditions are adverse

Decisions in a group often result in imitation and aggregation, which are enhanced in panic, dangerous, stressful or negative situations. Current explanations of this enhancement are restricted to particular contexts, such as anti-predatory behavior, deflection of responsibility in humans, or cases in which the negative situation is associated with an increase in uncertainty. But this effect is observed across taxa and in very diverse conditions, suggesting that it may arise from a more general cause, such as a fundamental characteristic of social decision-making. Current decision-making theories do not explain it, but we noted that they concentrate on estimating which of the available options is the best one, implicitly neglecting the cases in which several options can be good at the same time. We explore a more general model of decision-making that instead estimates the probability that each option is good, allowing several options to be good simultaneously. This model predicts with great generality the enhanced imitation in negative situations. Fish and human behavioral data showing an increased imitation behavior in negative circumstances are well described by this type of decisions to choose a good option.

 

The Informative Herd: why humans and other animals imitate more when conditions are adverse
Alfonso Pérez-Escudero, Gonzalo G. de Polavieja

http://arxiv.org/abs/1403.7478


Via Complexity Digest
António F Fonseca's insight:

I believe logic emerges from imitation.

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Using Complex Networks to Characterize International Business Cycles

Using Complex Networks to Characterize International Business Cycles | Aggregate Intelligence | 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.


Via Bernard Ryefield
António F Fonseca's insight:

Crisis transmission, lookout for USA, Ireland and Spain!

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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.

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Hypernetworks in the Science of Complex Systems (by Jeffrey Johnson)

Hypernetworks in the Science of Complex Systems (Series on Complexity Science)

~ Jeffrey Johnson (author) More about this product
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The modern world is complex beyond human understanding and control. The science of complex systems aims to find new ways of thinking about the many interconnected networks of interaction that defy traditional approaches. Thus far, research into networks has largely been restricted to pairwise relationships represented by links between two nodes. This volume marks a major extension of networks to multidimensional hypernetworks for modeling multi-element relationships, such as companies making up the stock market, the neighborhoods forming a city, people making up committees, divisions making up companies, computers making up the internet, men and machines making up armies, or robots working as teams.

This volume makes an important contribution to the science of complex systems by:
(i) extending network theory to include dynamic relationships between many elements;
(ii) providing a mathematical theory able to integrate multilevel dynamics in a coherent way; (iii)
providing a new methodological approach to analyze complex systems; and
(iv) illustrating the theory with practical examples in the design, management and control of complex systems taken from many areas of application.


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june holley's curator insight, March 24, 5:36 AM

A little pricey but breakthrough stuff here...

Rescooped by António F Fonseca from Complexity - Complex Systems Theory
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Why Model? Joshua M. Epstein

Why Model? Joshua M. Epstein | Aggregate Intelligence | 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.


Via Bernard Ryefield
António F Fonseca's insight:

The classical paper about modelling and simulation. Very clear.

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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


Via Complexity Digest, Shaolin Tan
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Eli Levine's curator insight, March 10, 2:16 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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Twitter e la dinamica delle opinioni di maggioranza - Le Scienze

Twitter e la dinamica delle opinioni di maggioranza - Le Scienze | Aggregate Intelligence | Scoop.it
L'opinione predominante, condivisa dalla maggioranza delle persone, emerge rapidamente su Twitter, qualunque sia l'argomento, e una volta stabilizzata difficilmente può cambiare. Lo ha scoperto una nuova analisi automatizzata, che potrebbe essere utilizzata per prevedere - ma forse anche per influenzare - come si orienterà l'opinione pubblica

Via Marinella De Simone
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Marinella De Simone's curator insight, March 18, 1:55 PM

i dati mostrano che mentre all'inizio le opinioni su un argomento fluttuano notevolmente, questa variabilità si attenua molto in fretta, stabilizzandosi su un'opinione di maggioranza, largamente condivisa, che prevale nettamente sull'altra. 

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PLOS ONE Complex systems articles

PLOS ONE Complex systems articles | Aggregate Intelligence | 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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Rescooped by António F Fonseca from Papers
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Information Evolution in Social Networks

Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.

 

Information Evolution in Social Networks
Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng

http://arxiv.org/abs/1402.6792


Via Complexity Digest
António F Fonseca's insight:

Memes are the information science counterpath of particles to physics.

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Study maps Twitter’s information ecosystem

Study maps Twitter’s information ecosystem | Aggregate Intelligence | Scoop.it
New research outlines the six types of communities on the social network and what that means for communication

Via luiy, NESS
António F Fonseca's insight:

What community do you belong to?

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luiy's curator insight, February 22, 4:39 AM

Fil Menczer, a professor at the University of Indiana Bloomington School of Informatics and Computing, has researched the potential applications of this type of analysis for years. Menczer’s research touches on every aspect of Twitter’s role as a mirror for human communities, like examining the relationship between social data and the stock market, the spread of infectious diseases and how political campaigns manipulate data to spread misleading information. In a 2012 paper on the spread of memes on Twitter, Menczer and his team sought to demystify how information spreads on unrelated topics, yielding similar network structures to those uncovered by Pew.

 

------------------------------

 

One of the major lessons of network analysis, both Pew and Menczer emphasize, is that the Twitter commons hasn’t necessarily made society as democratic as techno-utopians would have you believe. Twitter isn’t a wide-open space, free of boundaries or obstacles: It’s a "mirror," as Menczer says, for the social structures of the real world.

“One of the presumptions about the rise of social media is that it’s changed everything,” says Himelboim. “In fact, if you look at the broadcast networks and brand clusters (two archetypes described by Pew), big, important and powerful institutions that wield tremendous influence offline still do on the Internet. This is really a reality check against those louder voices who claim the world has somehow been transformed."

 

“It makes you wonder about polarization in political discourse: Is this something that social media is responsible for?” asks Menczer. “Is more polarization easier because of social media, or are we observing what was already there with new technology? Or, even simpler: Would our discourse be better if Twitter and Facebook just didn’t exist?” 

 

 

Eli Levine's curator insight, March 1, 1:24 PM

Indeed, we each live in our own world, not in the real world per se.

 

Some, however, have a more accurate understanding of the real world and are willing to acknowledge their shortcomings.

 

The others, who are less inclined to explore and are more focused on their own self-production, just happen to be known as conservative in our culture.  Hence, they area always hindered from perceiving the real world in the strictest of senses, and are not likely to change in light of new information received from the outside world.

 

Non-adapting humans will equal a dead and dying species.  It's a shame, though, that we can be dragged down by them for our lack of effective effort and action.

 

Sad.

 

Think about it.

Fàtima Galan's curator insight, March 2, 11:44 PM

"The topographical "maps" of these communities, generated by Pew using the data visualization tool NodeXL, aren’t just maps of relationships. They represent the channels of information in Twitter’s vast ecosystem, the roads and throughways, stoops and street corners in each topical neighborhood where users congregate and swap news and anecdotes."

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Predicting Crowd Behavior with Big Public Data

With public information becoming widely accessible and shared on today's web, greater insights are possible into crowd actions by citizens and non-state actors such as large protests and cyber activism. We present efforts to predict the occurrence, specific timeframe, and location of such actions before they occur based on public data collected from over 300,000 open content web sources in 7 languages, from all over the world, ranging from mainstream news to government publications to blogs and social media. Using natural language processing, event information is extracted from content such as type of event, what entities are involved and in what role, sentiment and tone, and the occurrence time range of the event discussed. Statements made on Twitter about a future date from the time of posting prove particularly indicative. We consider in particular the case of the 2013 Egyptian coup d'etat. The study validates and quantifies the common intuition that data on social media (beyond mainstream news sources) are able to predict major events.

 

Predicting Crowd Behavior with Big Public Data
Nathan Kallus

http://arxiv.org/abs/1402.2308


Via Complexity Digest
António F Fonseca's insight:

Its becoming standard practice.

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▶ Alessandro Vespignani on theoretical developments for complex networks and systems - YouTube

This interview with Alessandro Vespignani is about the future of modelling and forecasting of epidemics and is part of the Futurium Talking Futures interview...

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Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Here we propose a simple formula for describing the characteristic properties of memes in the scientific literature, which is based on their frequency of occurrence and the degree to which they propagate along the citation graph. The product of the frequency and the propagation degree is the meme score, which accurately identifies important and interesting memes within a scientific field. We use data from close to 50 million publication records from the Web of Science, PubMed Central and the American Physical Society to demonstrate the effectiveness of our approach. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative metrics confirm that the meme score is highly effective, while requiring no external resources or arbitrary thresholds and filters.

António F Fonseca's insight:

A simple truth: memes are repetition and imitation. A nice paper based on this simple idea. 

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Network Weirdness: Exploring the Origins of Network Paradoxes

Social networks have many counter-intuitive properties, including the "friendship paradox" that states, on average, your friends have more friends than you do. Recently, a variety of other paradoxes were demonstrated in online social networks. This paper explores the origins of these network paradoxes. Specifically, we ask whether they arise from mathematical properties of the networks or whether they have a behavioral origin. We show that sampling from heavy-tailed distributions always gives rise to a paradox in the mean, but not the median. We propose a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks. Specifically, we show that for any user the majority of user's friends and followers have more friends, followers, etc. than the user, and that this cannot be explained by statistical properties of sampling. Next, we explore the behavioral origins of the paradoxes by using the shuffle test to remove correlations between node degrees and attributes. We find that paradoxes for the mean persist in the shuffled network, but not for the median. We demonstrate that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.

 

Network Weirdness: Exploring the Origins of Network Paradoxes
Farshad Kooti, Nathan O. Hodas, Kristina Lerman

http://arxiv.org/abs/1403.7242


Via Complexity Digest
António F Fonseca's insight:

Some network insights into the vague notion of popularity.

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

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Aggregate Intelligence | Scoop.it

Via luiy, Shaolin Tan
António F Fonseca's insight:

Another paper about popularity prediction.

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luiy's curator insight, March 27, 10:44 AM

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.

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Pinterest, Twitter, Facebook, Instagram, Google+, LinkedIn – Social Media Stats 2014 [INFOGRAPHIC] - AllTwitter

Pinterest, Twitter, Facebook, Instagram, Google+, LinkedIn – Social Media Stats 2014 [INFOGRAPHIC] - AllTwitter | Aggregate Intelligence | Scoop.it
Pinterest, Twitter, Facebook, Instagram, Google+, LinkedIn – Social Media Stats 2014 [INFOGRAPHIC]

Via Justin Menard
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ConfluenceSocialMarketing's curator insight, January 25, 7:34 AM

Great infographics on the various networks.

Ewa Sulima's curator insight, January 28, 2:34 AM

Brief and clear! Worth taking a glance!

Siri Anderson's curator insight, February 1, 6:14 AM

What if these were stats instead about "number of children immunized," "mentoring hours," "released prisoners education opportunities." sigh

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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

~ John H. Holland (author) More about this product
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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's insight:

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

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Costas Bouyioukos's curator insight, March 18, 10:41 AM

John Holland's new book!

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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Internet : outil de collaboration ou de domination ?

Internet : outil de collaboration ou de domination ? | Aggregate Intelligence | Scoop.it
Internet : outil de collaboration ou de domination ?
António F Fonseca's insight:

Excelent article about peer influence, power and the internet.

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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 | Aggregate Intelligence | 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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What are Complex Adaptive Systems?

What are Complex Adaptive Systems? | Aggregate Intelligence | Scoop.it

This site is about my business - trojanmice - which is dedicated to helping organisations understand the concept of complex adaptive systems and their application to organisational form.


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▶ Dirk Helbing on complexity in economic theory

This interview with Dirk Helbing on the Future of the economy is part of the Futurium Talking Futures interview series. More information is available here: https://ec.europa.eu/digital-agenda/futurium/en/interviews ;


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Eli Levine's curator insight, February 27, 9:12 PM

Indeed, it is when we shut the door and turn our backs on those and that which do us harm, that we'll actually realize some real benefits amongst this species.


Think about it.

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Study uncovers six basic types of Twitter conversations

Study uncovers six basic types of Twitter conversations | Aggregate Intelligence | Scoop.it
Researchers say there are six structures for most conversations on Twitter, ranging from polarized debates to community clusters.

Via NESS, Complexity Institute
António F Fonseca's insight:

I've already study this.

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Eli Levine's curator insight, February 23, 10:05 AM

This is just plain interesting.

 

How often we talk, and how little we actually have to say.

 

Think about it.

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Cybernetics and Information Theory in the United States, France and the Soviet Union

Mindell et al. (2003) «Cybernetics and Information Theory in the United States, France and the Soviet Union» http://t.co/WxxvghtyUz

Via Ben van Lier, Bernard Ryefield
António F Fonseca's insight:

Very interesting document!

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New Insights on How To Verify Social Media

New Insights on How To Verify Social Media | Aggregate Intelligence | Scoop.it
The "field" of information forensics has seen some interesting developments in recent weeks. Take the Verification Handbook or Twitter Lie-Detector project, for example. The Social Sensor project i...

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