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Be nice. Evolution will punish you if you’re not, says study

Be nice. Evolution will punish you if you’re not, says study | Information, Complexity, Computation | Scoop.it
New research has challenged the notion that evolution favours self-interest above co-operation, suggesting instead that selfish individuals eventually ‘compete each other out of existence’. 
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[1405.6707] Understanding the spreading power of all nodes in a network: a continuous-time perspective

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How Complex Contagions Spread and Spread Quickly

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In this paper we study the spreading speed of complex contagions in a social network. A k-complex contagion starts from a set of initially infected seeds such that any node with at least k infected neighbors gets infected. Simple contagions, i.e., k=1, spreads to the entire network quickly in any small world graph. However, the spreading of complex contagions appears to be less likely and more delicate; the successful cases depend crucially on the network structure~\cite{Ghasemiesfeh:2013:CCW}. 
The main result in this paper is to show that complex contagions can spread fast in the preferential attachment model, covering the entire network of nnodes in O(logn) steps, if the initial seeds are the oldest nodes in the network. We show that the choice of the initial seeds is crucial. If the initial seeds are uniformly randomly chosen and even if we have polynomial number of them, it is not enough to spread a complex contagion. The oldest nodes in a preferential attachment model are likely to have high degrees in the network. However, we remark that it is actually not the power law degree distribution per se that supports complex contagion, but rather the evolutionary graph structure of such models. The proof generalizes to a bigger family of time evolving graphs where some of the members do not have a power-law distribution. The core of the proof relies on the analysis of a multitype branching process, which may be of independent interest. 
We also present lower bounds for the cases of Kleinberg's small world model that were not analyzed in prior work. When the clustering coefficient γ is anything other than 2, a complex contagion necessarily takes polynomial number of rounds to spread to the entire network.

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In the Company of Wealth | Dragons and Pandas | Big Think

In the Company of Wealth | Dragons and Pandas | Big Think | Information, Complexity, Computation | Scoop.it
“Now, working elbow to elbow with billionaires, I was a giant fireball of greed.” –Sam Polk BREAK it down: Obscenely rich people from 2013 meet in Davos, Switzerland, to discuss how to get ludicrously rich in 2014; Japan’s Abe reminds Western...
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Predicting Successful Memes using Network and Community Structure

Predicting Successful Memes using Network and Community Structure | Information, Complexity, Computation | Scoop.it

Via luiy, Shaolin Tan, António F Fonseca, NESS, Eugene Ch'ng
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luiy's curator insight, March 27, 1:44 PM

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

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

Another paper about popularity prediction.

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Twitter 'big data' can be used to monitor HIV, drug-related behavior

Twitter 'big data' can be used to monitor HIV, drug-related behavior | Information, Complexity, Computation | Scoop.it
Real-time social media like Twitter could be used to track HIV incidence and drug-related behaviors with the aim of detecting and potentially preventing outbreaks. The study suggests it may be possible to predict sexual risk and drug use behaviors by monitoring tweets, mapping where those messages come from and linking them with data on the geographical distribution of HIV cases. The use of various drugs had been associated in previous studies with HIV sexual risk behaviors and transmission of infectious disease.
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How to Save Human Lives with Complexity Science

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We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are not effective and sufficient to contain them. The failure of many conventional approaches results from their neglection of feedback loops, instabilities and/or cascade effects, due to which equilibrium models do often not provide a good picture of the actual system behavior. However, the complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be understood by means of complexity science, which enables one to address the aforementioned problems more successfully. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.
  
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The Relative Ineffectiveness of Criminal Network Disruption

Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, data-driven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become ‘stronger’, after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re-)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a long-term effort.

 

The Relative Ineffectiveness of Criminal Network Disruption
Paul A. C. Duijn, Victor Kashirin & Peter M. A. Sloot

Scientific Reports 4, Article number: 4238 http://dx.doi.org/10.1038/srep04238 ;

 

See also documentary at http://www.youtube.com/watch?v=Qhk9ciHlzzo 


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Eli Levine's curator insight, March 6, 1:34 PM

My only critique of this, is that even by successfully disrupting the social networks, you will ont get rid of the foundations of crime within a society.

 

Greed, lust, violence, all of these things come from the brain and can be seen as mental health problems, rather than necessarily just societal problems.  I think we've got to begin ori sorting th the convected and post conicted crowd, such tht we can understand how their brains work and then, how to help heal them, such that we eliminate criminality and crime inspited lifestyles.  I understand there are dozens of easy ways to be opposed to this and that there are dozes more ways th work (especially here, in america, where we are soc focused on our small "selves" to forget that there is a much much much much larger world out thre, and that of ourselves as well.  We are connected to everyone and everything.  That's science.  To deny that it is otherwise is to invite delusion and hallucinations about reality and to invite other problems into your life and the rest of ours for your deliberate ignorance and unwillingness to escape to where reality simply is unoffensive and not politically motivated other than to help other people.

 

Therefore, let's overcome this monkey need to punish people for crimes they really didn't have much say in (thankst o the primacy of the brain) and start doing some research on these people (even though they should be confined from the rest of the population until treatments and diagnoses have been developed and concluded upon).

 

Think about it.

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Crime rates could rise as climate change bites - environment - 28 February 2014 - New Scientist

Crime rates could rise as climate change bites - environment - 28 February 2014 - New Scientist | Information, Complexity, Computation | Scoop.it
As temperatures soar, so do crime rates – suggesting climate change will lead to millions of extra offences in the coming decades
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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


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António F Fonseca's curator insight, March 1, 2:00 PM

Memes are the information science counterpath of particles to physics.

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The Math That Predicted the Revolutions Sweeping the Globe Right Now

The Math That Predicted the Revolutions Sweeping the Globe Right Now | Information, Complexity, Computation | Scoop.it
The complex systems theorists who predicted the Arab Spring built a model that predicted the unrest in Ukraine, Venezuela, and Thailand too.
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Simulating the past to understand human history: satellite in 10th conference of the European Social Simulation Association (ESSA)

SIMULATING THE PAST TO UNDERSTAND HUMAN HISTORY

 

The conference is organized with the contribution of the SimulPast project(www.simulpast.es), a 5-year exploratory research project funded by the SpanishGovernment (MICINN CSD2010-00034) that aims at developing an innovative andinterdisciplinary methodological framework to model and simulate ancient societies andtheir relationship with environmental transformations. To achieve these aims, SimulPastintegrates knowledge from diverse fields covering humanities, social, computationaland ecological sciences within a national and international network.

 

The conference intention is to showcase the result of the SimulPast project together withcurrent international research on the methodological and theoretical aspects of computersimulation in archaeological and historical contexts. The conference will bring togetherscholars from different disciplinary backgrounds (history, ecology, archaeology,anthropology, sociology, computer science and complex systems) in order to promotedeeper understanding and collaboration in the study of past human behavior and history


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Introduction to Complex Systems: Patterns in Nature

This video provides a basic introduction to the science of complex systems, focusing on patterns in nature. (For more information on agent-based modeling, vi...

Via Lorien Pratt, António F Fonseca, NESS
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António F Fonseca's curator insight, February 1, 4:50 AM

Agent based modeling still is the best tool to understand complex systems when mathematical modeling gets very complicated.

Liz Rykert's curator insight, February 10, 7:25 PM

Always looking for good resources to introduce complexity science to others. This looks great. 

Ian Biggs, MAIPM, CPPD's curator insight, April 16, 8:08 PM

I recently conducted a series of workshops on the subject of 'Complex Project Management - Navigating through the unknown'. This clip provides a great introduction to complex systems and for those interested in Complexity Science, this clip is worth 7:52 of your time.

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Biology's 'dark matter' hints at fourth domain of life - life - 18 March 2011 - New Scientist

Biology's 'dark matter' hints at fourth domain of life - life - 18 March 2011 - New Scientist | Information, Complexity, Computation | Scoop.it
Over 99 per cent of organisms remain unknown to science – so could some of them sit outside the classic three domains of cellular life?
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Polar Swarms

Polar Swarms | Information, Complexity, Computation | Scoop.it
A new theory can explain the formation of swarming patterns observed in ensembles of self-propelled polar particles.
Eugene Ch'ng's insight:

How do individual animals form swarms, schools, and flocks? In the 1990s, physicists modeled collections of self-propelled particles (so-called “active matter”) and could simulate the ordering that occurs in animal flocks.  Theoretical models have reproduced many aspects of this collective behavior, but a number of questions have persisted. One concerns the observation that in polar, active matter—think of a collection of small, mutually interacting swimming arrows—the particles organize themselves into three possible pattern classes: density waves, solitary waves (solitons), and traveling “droplets.”

No single theory has been able to explain the formation and diversity of these patterns. However, in a paper in Physical Review Letters, Jean-Baptiste Caussin and collaborators from institutes in France, Germany, and the Netherlands, have solved a hydrodynamic model of polar active particles and have accounted for the origin and variety of these propagating swarm structures...

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Network communities within and across borders

Network communities within and across borders | Information, Complexity, Computation | Scoop.it
We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index.

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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]


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Eli Levine's curator insight, April 16, 6:08 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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Financial Brownian particle in the layered order-book fluid and fluctuation-dissipation relations

Eugene Ch'ng's insight:

We introduce a novel description of the dynamics of the order book of financial markets as that of an effective colloidal Brownian particle embedded in fluid particles. The analysis of a comprehensive market data enables us to identify all motions of the fluid particles. Correlations between the motions of the Brownian particle and its surrounding fluid particles reflect specific layering interactions; in the inner-layer, the correlation is strong and with short memory while, in the outer-layer, it is weaker and with long memory. By interpreting and estimating the contribution from the outer-layer as a drag resistance, we demonstrate the validity of the fluctuation-dissipation relation (FDR) in this non-material Brownian motion process.

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Damage spreading in spatial and small-world random Boolean networks

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities ($\bar{K} << 1$) and that the critical connectivity of stability $\bar{K}$ changes compared to random networks. At higher $\bar{K}$, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size $N$ increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

 

Qiming Lu and Christof Teuscher
Damage spreading in spatial and small-world random Boolean networks
Phys. Rev. E 89, 022806 (2014)

http://pre.aps.org/abstract/PRE/v89/i2/e022806


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The strength of ‘weak signals’

The strength of ‘weak signals’ | Information, Complexity, Computation | Scoop.it

As information thunders through the digital economy, it’s easy to miss valuable “weak signals” often hidden amid the noise. Arising primarily from social media, they represent snippets—not streams—of information and can help companies to figure out what customers want and to spot looming industry and market disruptions before competitors do. Sometimes, companies notice them during data-analytics number-crunching exercises. Or employees who apply methods more akin to art than to science might spot them and then do some further number crunching to test anomalies they’re seeing or hypotheses the signals suggest. In any case, companies are just beginning to recognize and capture their value. Here are a few principles that companies can follow to grasp and harness the power of weak signals.


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Eli Levine's curator insight, March 6, 12:34 PM

The same can be said for governing, although the end goal is, when it's actually working for the sake of the governing, how to better serve people according to their needs and expressed desires.  The reward for good governance is continued time in office.  The way you actually get to that end is through a combination of listening for NEEDS (which aren't the same as wants) within the general public and then actively teasing those needs out so that you can understand them.

 

It's a pro-active dialogue, especially on the part of the governing, if it is being done in a way that is actually beneficial for the governing and the governed alike.  The former depends on the latter more than the latter depends on the former, because it is the governed which gives authority to the governing, while the governed can exist (if sub-optimally) without the governing group's present.  It doesn't even matter which specific group is in power, since they're all going to be bound to do the same basic stuff in the same basic ways, if they're going to produce optimal results for themselves and other people living in the society as a whole.  The only question that matters is "how well does the present governing group do at governing?"  Society is constantly open to shopping for other options; constantly playing the field if things become sub-optimal for society in some way, shape or form.

 

That is why a good government is proactive when working with its citizens and listening for these "weak signals", because those are what reveals the subtle workings of the group's psychology and what the group actually is needing/wanting versus what they explicitly express.

 

Think about it.

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Guided Self-Organisation

Typically, self-organisation (SO) is defined as the evolution of a system into an organised form in the absence of external pressures. SO within a system brings about several attractive properties, in particular, robustness, adaptability and scalability. In the face of perturbations caused by adverse external factors or internal component failures, a robust self-organising system continues to function. Moreover, an adaptive system may re-configure when required, degrading in performance “gracefully” rather than catastrophically. In certain circumstances, a system may need to be extended with new components and/or new connections among existing modules — without SO such scaling must be preoptimised in advance, overloading the traditional design process.
In general, SO is a not a force that can be applied very naturally during a design process. In fact, one may argue that the notions of design and SO are contradictory: the former approach often assumes a methodical step-by-step planning process with predictable outcomes, while the latter involves non-deterministic spontaneous dynamics with emergent features. Thus, the main challenge faced by designers of self-organising systems is how to achieve and control the desired dynamics. Erring on the one side may result in over-engineering the system, completely eliminating emergent patterns and suppressing an increase in internal organisation with outside influence. Strongly favouring the other side may leave too much non-determinism in the system’s behaviour, making its verification and validation almost impossible. The balance between design and SO is the main theme of guided self-organisation (GSO). In short, GSO combines both task-independent objectives (e.g., information-theoretic and graph-theoretic utility functions) with task-dependent constraints.

 

http://guided-self.org


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Liz Rykert's curator insight, March 3, 5:17 PM

Love the paradox in this. Worth digging into. 

Eli Levine's curator insight, March 3, 9:24 PM

Indeed, you must obey the laws of society and those of economics, environmental science and psychology in order to govern a world.

 

The simple rules are already in place.  The question is how do we conform to those rules so that we realize the most optimal social function for everybody?

 

This is where lawyering and traditional politiking takes a back seat, and the technical world of governing begins.

Think about it.

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Crowd-sourcing: Strength in numbers

Crowd-sourcing: Strength in numbers | Information, Complexity, Computation | Scoop.it
Researchers are finding that online, crowd-sourced collaboration can speed up their work — if they choose the right problem.
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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


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António F Fonseca's curator insight, March 1, 7:53 AM

Its becoming standard practice.

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SFI: notes for the history of Complex Systems Science

This is the first in a series of articles recounting the history of the Santa Fe Institute drawn from primary and, in a few cases, secondary sources. 

By John German

 

In George Cowan's telling, the notion for a Santa Fe Institute began to form in the summer of 1956. He had been invited to the Aspen Institute, where prominent intellectuals from the arts, science, and culture gathered for free-form philosophical exchanges. He had just participated as the lone scientist in a discussion of literature. (...)


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SocInfo 2014 | 6th international conference on Social Informatics

We are delighted to welcome the 6th International Conference on Social Informatics (SocInfo 2014) to Barcelona, Spain, from November 10th to November 13th.SocInfo is an interdisciplinary venue for researchers from Computer Science, Informatics, Social Sciences and Management Sciences to share ideas and opinions, and present original research work on studying the interplay between socially-centric platforms and social phenomena. The ultimate goal of Social Informatics is to create better understanding of socially-centric platforms not just as a technology, but also as a set of social phenomena. To that end, we are inviting interdisciplinary papers, on applying information technology in the study of social phenomena, on applying social concepts in the design of information systems, on applying methods from the social sciences in the study of social computing and information systems, on applying computational algorithms to facilitate the study of social systems and human social dynamics, and on designing information and communication technologies that consider social context.

 

http://socinfo2014.org


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Video Reveals the Molecular Basis of Memory | Big Think TV | Big Think

Video Reveals the Molecular Basis of Memory | Big Think TV | Big Think | Information, Complexity, Computation | Scoop.it
A major advance in the use of microscopes for scientific investigation allows scientists to watch the brain create memories. 
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