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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, 2014 1:41 PM

John Holland's new book!

june holley's curator insight, March 23, 2014 7:43 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 ;


Via Complexity Digest, Roger D. Jones, PhD
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Eli Levine's curator insight, February 28, 2014 12:12 AM

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, 2014 1:05 PM

This is just plain interesting.

 

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

 

Think about it.

Rescooped by António F Fonseca from Complexity & Systems
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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...

Via NESS
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IEEE - 2014 Second World Conference on Complex Systems (WCCS)


Via Bryan A. Knowles
António F Fonseca's insight:

Complex Systems are going west.

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Rescooped by António F Fonseca from Papers
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Zipf's Law for All the Natural Cities around the World

Two fundamental issues surrounding research on Zipf's law regarding city sizes are whether and why Zipf's law holds. This paper does not deal with the latter issue with respect to why, and instead investigates whether Zipf's law holds in a global setting, thus involving all cities around the world. Unlike previous studies, which have mainly relied on conventional census data, and census- bureau-imposed definitions of cities, we adopt naturally and objectively delineated cities, or natural cities, to be more precise, in order to examine Zipf's law. We find that Zipf's law holds remarkably well for all natural cities at the global level, and remains almost valid at the continental level except for Africa at certain time instants. We further examine the law at the country level, and note that Zipf's law is violated from country to country or from time to time. This violation is mainly due to our limitations; we are limited to individual countries, and to a static view on city-size distributions. The central argument of this paper is that Zipf's law is universal, and we therefore must use the correct scope in order to observe it. We further find that this law is reflected in the distribution of cities: the number of cities in individual countries follows an inverse power relationship; the number of cities in the first largest country is twice as many as that in the second largest country, three times as many as that in the third largest country, and so on. 

 

Zipf's Law for All the Natural Cities around the World
Bin Jiang, Junjun Yin, Qingling Liu

http://arxiv.org/abs/1402.2965


Via Complexity Digest
António F Fonseca's insight:

This is a problem almost a century old, Zip's law was formulated in the 40's with English words.

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Epidemiological modeling of online social network dynamics

António F Fonseca's insight:

Very good idea: validating epidemic models with Google Trends. It seems Facebook is declining. But Facebook replied: https://www.facebook.com/notes/mike-develin/debunking-princeton/10151947421191849

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The Future Is Cities

The Future Is Cities | Aggregate Intelligence | Scoop.it

Cities around the world are growing faster than you can say megalopolis. More than half the world lives in cities, and by 2050, it will be two-thirds. In China alone, 300 million people will move to the city within the next 15 years, and to serve them, China must build the equivalent of the entire built infrastructure of the United States by 2028.
At the same time, 250 million new urban dwellers are expected in India and 380 million in Africa. Even though cities will soon account for 90 percent of population growth, 80 percent of global CO2, and 75 percent of energy consumption, more and more, it’s where people want to live.
Why? Because it’s where 80 percent of the wealth is created, and it’s where people find opportunities, especially women in the developing world. But beyond basic needs from housing to jobs, how do we enjoy the benefits of the city—like cafes, art galleries, restaurants, cultural facilities—without the traffic, crowding, crime, pollution, and disease?

 

http://spectrum.mit.edu/articles/the-future-is-cities/ ;


Via Complexity Digest
António F Fonseca's insight:

Living in cities is efficient and less costly to the natural environment.

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Eli Levine's curator insight, February 8, 2014 2:47 PM

Personally, I'd rather get us off the notion in our highest levels of policy making that money-making and monetary gain is the pinnacle of achievement for the individual in a given society.

 

But this appears to be a new front that's forming for our governments (not just the Federal, in the US) to tackle.

 

And it's going to, unfortunately, take us a relatively long time to figure this stuff out in our usual incomplete and sub-optimal manner.

 

I've got no evidence to suggest that we're going to do it otherwise.

 

Wish it wasn't the case.  But there you go.

 

Think about it.

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Complex Systems in the Arts & Humanities

Collective Dynamics of Complex Systems Research Group Seminar Series October 23, 2013 Thomas Lombardi (Computing and Information Studies, Washington and Jefferson…

Via Hiroki Sayama
António F Fonseca's insight:

Very interesting presentation of the scientific analysis of art and literature.

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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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Rescooped by António F Fonseca from Dynamics on complex networks
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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, 2014 5:16 PM

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


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

 

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

 

Silly people....

 

Think about it.

Rescooped by António F Fonseca from Exploring complexity
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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, 2014 4:55 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. 

Rescooped by António F Fonseca from Nice and Complex
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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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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, 2014 7:39 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, 2014 4:24 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 3, 2014 2:44 AM

"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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When You Fall in Love, This Is What Facebook Sees

When You Fall in Love, This Is What Facebook Sees | Aggregate Intelligence | Scoop.it
“During the 100 days before the relationship starts, we observe a slow but steady increase in the number of timeline posts shared between the future couple.”
António F Fonseca's insight:

We will assist new times in social science from now, we will see from now how people function in society just from social network data. This is just a breakthrough study.

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Connecting Dream Networks Across Cultures

Many species dream, yet there remain many open research questions in the study of dreams. The symbolism of dreams and their interpretation is present in cultures throughout history. Analysis of online data sources for dream interpretation using network science leads to understanding symbolism in dreams and their associated meaning. In this study, we introduce dream interpretation networks for English, Chinese and Arabic that represent different cultures from various parts of the world. We analyze communities in these networks, finding that symbols within a community are semantically related. The central nodes in communities give insight about cultures and symbols in dreams. The community structure of different networks highlights cultural similarities and differences. Interconnections between different networks are also identified by translating symbols from different languages into English. Structural correlations across networks point out relationships between cultures. Similarities between network communities are also investigated by analysis of sentiment in symbol interpretations. We find that interpretations within a community tend to have similar sentiment. Furthermore, we cluster communities based on their sentiment, yielding three main categories of positive, negative, and neutral dream symbols.

António F Fonseca's insight:

Very interesting research work based on web content.

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Eli Levine's curator insight, February 12, 2014 1:22 PM

Indeed, this does appear to, once again, support the notion that we are of one species with the same basic cultural roots at the core of each of our socieites.

 

Just look at the similarities of religious schools of thought.  Everything from Pagan beliefs in Europe, to the Abrahamic religions of Judaism, Christianity and Islam, to the Eastern religions of Hinduism, Buddhism, Sikhism, Jainism and the Chinese schools of thought: Confucism and Daoism.  They all center around, once again, to the notion that we ought to be treating everybody well and with a basic level of respect. 

 

This appears to be an almost universal phenomenon within our species, across cultures and within them.

 

However, it's a shame that there are so many who are caught in the conservative way of thinking that is more like our chimp ancestors than as actual human beings.

 

What is the purpose of being so tribal and petty about your interactions with other human beings, when it costs you so much on the material and non-material levels?  And, why do they persist in these ways of thinking which cause damage to them, in light of the constant evidence that they are wrong, both on a perceptive level and on a behavioral level?

 

Think about it.

 

 

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Power, Privacy, and the Internet

Power, Privacy, and the Internet | Aggregate Intelligence | Scoop.it
On October 30–31, 2013, The New York Review of Books held a conference at Scandinavia House in New York City on the internet's transformative effect on our lives.
António F Fonseca's insight:

Is The Cloud a 'Black Cloud' or will there be some silver linings?

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Quantifying Information Flow During Emergencies

Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.

 

Quantifying Information Flow During Emergencies
Liang Gao, Chaoming Song, Ziyou Gao, Albert-László Barabási, James P. Bagrow & Dashun Wang

Scientific Reports 4, Article number: 3997 http://dx.doi.org/10.1038/srep03997


Via Complexity Digest
António F Fonseca's insight:

Extensive study of information bursts in emergency situations, comparative analysis against other high arousal events like a rock concert is very instructive.

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