Social Simulation
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 Scooped by Frédéric Amblard onto Social Simulation

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to changing data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative networks. This article provides an overview of diffusion strategies for adaptation and learning over networks.

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# Social Simulation

News about social simulation, social networks dynamics and complex systems
 Rescooped by Frédéric Amblard from Papers

## Subliminal Influence or Plagiarism by Negligence? The Slodderwetenschap of Ignoring the Internet

Does the availability of instant reference checking and “find more like this” research on the Internet change the standards by which academics should feel “obligated” to cite the work of others? Is the deliberate refusal to look for the existence of parallel work by others an ethical lapse or merely negligence? At a minimum, the Dutch standard of Slodderwetenschap (sloppy science) is clearly at work. At a maximum so is plagiarism. In between sits the process to be labeled as ‘plagiarism by negligence’. This article seeks to expose the intellectual folly of allowing such a plagiarism to be tolerated by the academy through a discussion of the cases of Terrence Deacon and Stephen Wolfram.

Subliminal Influence or Plagiarism by Negligence? The Slodderwetenschap of Ignoring the Internet

Michael Lissack

http://isce.edu/Subliminal.pdf

Via Complexity Digest
Arjen ten Have's comment, December 4, 2:01 PM
The Dutch standard of Slodderwetenschap? Bit sloppy, it is a recent Dutch word, hope not ths standard.
Ellie Kesselman Wells's comment, December 5, 4:43 PM
Excellent subject matter! Thank you!
 Rescooped by Frédéric Amblard from Papers

## Complex Systems Science as a New Transdisciplinary Science, by Paul Bourgine

The new science of complex systems will be at the heart of the future of the Worldwide Knowledge Society. It is providing radical new ways of understanding the physical, biological, ecological, and techno-social universe. Complex Systems are open, value-laden, multi-level, multi-component, reconfigurable systems of systems, situated in turbulent, unstable, and changing environments. They evolve, adapt and transform through internal and external dynamic interactions. They are the source of very difficult scientific challenges for observing, understanding, reconstructing and predicting their multi-scale dynamics. The challenges posed by the multi-scale modelling of both natural and artificial adaptive complex systems can only be met with radically new collective strategies for research and teaching (...)

Via NESS, Complexity Digest
june holley's curator insight,

The study of complex systems adds a lot of depth to understanding networks.

Complexity Institute's curator insight,

Are we ready to recognize a Science as a "Transdisciplinary Science?
Complex systems science is not a science in itself, but it may be considered as a 'Science of Sciences'.
I think this is the most challenging issue to face for a Worldwide Knowledge Society, as Paul Bourgine states.

 Rescooped by Frédéric Amblard from CoCo: Collective Dynamics of Complex Systems Research Group

## [1311.3674] Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations

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 Rescooped by Frédéric Amblard from CxAnnouncements

## Innovation Accelerator

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## Social Network Size Linked to Brain Size

How and why the volume of the orbital prefrontal cortex is related to the size of social networks...

Via Spaceweaver, Complexity Digest
Viktoras Veitas's comment, September 1, 2012 12:47 PM
The idea came across my mind wile reading this. Global Brain can be compared to a global social network (= giant global graph). Humans are not able to form a meaningfull social network with more than 150 members. Global Brain should encompass the whole humanity, i.e. in the order of billions. So, in order for the Global Brain to emerge, we need (1) to either enhance humans to be able to form "theories of mind" of this magnitude, or, alternatively, (2) to create artificial agents, capable of doing this and connecting humans. Oh, and there is a third way - doing both in parallel...
 Scooped by Frédéric Amblard

## Tracking Down an Epidemic’s Source

Researchers find the source of an epidemic using relatively little information. Their technique could also help authorities track down contamination in water systems or locate problems in electrical grids.
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## Opinions, Conflicts and Consensus: Modeling Social Dynamics in a Collaborative Environment

János Török, Gerardo Iñiguez, Taha Yasseri, Maxi San Miguel, Kimmo Kaski, János Kertész
(Submitted on 20 Jul 2012)
Information-communication technology promotes collaborative environments like Wikipedia where, however, controversiality and conflicts can appear. To describe the rise, persistence, and resolution of such conflicts we devise an extended opinion dynamics model where agents with different opinions perform a single task to make a consensual product. As a function of the convergence parameter describing the influence of the product on the agents, the model shows spontaneous symmetry breaking of the final consensus opinion represented by the medium. For the case when agents are replaced with new ones at a certain rate, a transition from mainly consensus to a perpetual conflict occurs, which is in qualitative agreement with the scenarios observed in Wikipedia.

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## Peer-to-Peer and Mass Communication Effect on Revolution Dynamics

Alex Kindler, Sorin Solomon, Dietrich Stauffer
(Submitted on 22 Jul 2012)
Revolution dynamics is studied through a minimal Ising model with three main influences (fields): personal conservatism (power-law distributed), inter-personal and group pressure, and a global field incorporating peer-to-peer and mass communications, which is generated bottom-up from the revolutionary faction. A rich phase diagram appears separating possible terminal stages of the revolution, characterizing failure phases by the features of the individuals who had joined the revolution. An exhaustive solution of the model is produced, allowing predictions to be made on the revolution's outcome.

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 Rescooped by Frédéric Amblard from Papers

## Identifying Influential and Susceptible Members of Social Networks

Identifying social influence in networks is critical to understanding how behaviors spread. We present a method for identifying influence and susceptibility in networks that avoids biases in traditional estimates of social contagion by leveraging in vivo randomized experimentation. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product we studied. Analysis of influence and susceptibility together with network structure reveals that influential individuals are less susceptible to influence than non-influential individuals and that they cluster in the network, which suggests that influential people with influential friends help spread this product.

Identifying Influential and Susceptible Members of Social Networks
Sinan Aral, Dylan Walker

Via Complexity Digest
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## Complex networks embedded in space: Dimension and scaling relations between mass, topological distance and Euclidean distance

Many real networks are embedded in space, where in some of them the links length decay as a power law distribution with distance. Indications that such systems can be characterized by the concept of dimension were found recently. Here, we present further support for this claim, based on extensive numerical simulations for model networks embedded on lattices of dimensions $d_e=1$ and $d_e=2$.
We evaluate the dimension $d$ from the power law scaling of (a) the mass of the network with the Euclidean radius $r$ and (b) the probability of return to the origin with the distance $r$ travelled by the random walker. Both approaches yield the same dimension. For networks with $\delta < d_e$, $d$ is infinity, while for $\delta > 2d_e$, $d$ obtains the value of the embedding dimension $d_e$. In the intermediate regime of interest $d_e \leq \delta < 2 d_e$, our numerical results suggest that $d$ decreases continously from $d = \infty$ to $d_e$, with $d - d_e \sim (\delta - d_e)^{-1}$ for $\delta$ close to $d_e$. Finally, we discuss the scaling of the mass $M$ and the Euclidean distance $r$ with the topological distance $\ell$. Our results suggest that in the intermediate regime $d_e \leq \delta < 2 d_e$, $M(\ell)$ and $r(\ell)$ do not increase with $\ell$ as a power law but with a stretched exponential, $M(\ell) \sim \exp [A \ell^{\delta' (2 - \delta')}]$ and $r(\ell) \sim \exp [B \ell^{\delta' (2 - \delta')}]$, where $\delta' = \delta/d_e$. The parameters $A$ and $B$ are related to $d$ by $d = A/B$, such that $M(\ell) \sim r(\ell)^d$. For $\delta < d_e$, $M$ increases exponentially with $\ell$, as known for $\delta=0$, while $r$ is constant and independent of $\ell$. For $\delta \geq 2d_e$, we find power law scaling, $M(\ell) \sim \ell^{d_\ell}$ and $r(\ell) \sim \ell^{1/d_{min}}$, with $d_\ell \cdot d_{min} = d$.

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## Temporal Heterogeneities Increase the Prevalence of Epidemics on Evolving Networks

Empirical studies suggest that contact patterns follow heterogeneous inter-event times, meaning that intervals of high activity are followed by periods of inactivity. Combined with birth and death of individuals, these temporal constraints affect the spread of infections in a non-trivial way and are dependent on the particular contact dynamics. We propose a stochastic model to generate temporal networks where vertices make instantaneous contacts following heterogeneous inter-event times, and leave and enter the system at fixed rates. We study how these temporal properties affect the prevalence of an infection and estimate R0, the number of secondary infections, by modeling simulated infections (SIR, SI and SIS) co-evolving with the network structure. We find that heterogeneous contact patterns cause earlier and larger epidemics on the SIR model in comparison to homogeneous scenarios. In case of SI and SIS, the epidemics is faster in the early stages (up to 90% of prevalence) followed by a slowdown in the asymptotic limit in case of heterogeneous patterns. In the presence of birth and death, heterogeneous patterns always cause higher prevalence in comparison to homogeneous scenarios with same average inter-event times. Our results suggest that R0 may be underestimated if temporal heterogeneities are not taken into account in the modeling of epidemics.

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## Using large-scale brain simulations for machine learning and A.I.

You probably use machine learning technology dozens of times a day without knowing it—it’s a way of training computers on real-world data, and it enables high-quality speech recognition, practical computer vision, email spam blocking and even self-driving cars. But it’s far from perfect—you’ve probably chuckled at poorly transcribed text, a bad translation or a misidentified image. We believe machine learning could be far more accurate, and that smarter computers could make everyday tasks much easier. So our research team has been working on some new approaches to large-scale machine learning.

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 Rescooped by Frédéric Amblard from Augmented Collective Intelligence

## Mob rule: Iceland crowdsources its next constitution

Public sphere meets collective intelligence. -- Howard

"It is not the way the scribes of yore would have done it but Iceland is tearing up the rulebook by drawing up its new constitution through crowdsourcing.

As the country recovers from the financial crisis that saw the collapse of its banks and government, it is using social media to get its citizens to share their ideas as to what the new document should contain.

"I believe this is the first time a constitution is being drafted basically on the internet," said Thorvaldur Gylfason, member of Iceland's constitutional council."

Via Howard Rheingold
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 Rescooped by Frédéric Amblard from Papers

## Early-warning signals of topological collapse in interbank networks

The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.

Via Claudia Mihai, Complexity Digest
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 Rescooped by Frédéric Amblard from Global Brain

## Big Data needs Big Theory

In this guest cross-post, Geoffrey West, former President of the Santa Fe Institute, argues that just as the industrial age produced the laws of thermodynamics, we need universal laws of complexity...

Via Spaceweaver
Jacek Bugajski's curator insight,

Big Data needs Big Theory

Ricardo Pimenta's curator insight,

Big theory is needed to Big Data issues...

 Rescooped by Frédéric Amblard from Social Foraging

## The Math of Segregation

In the 1960s Schelling devised a simple model in which a mixed group of people spontaneously segregates by race even though no one in the population desires that outcome. Initially, black and white families are randomly distributed. At each step in the modeling process the families examine their immediate neighborhood and either stay put or move elsewhere depending on whether the local racial composition suits their preferences. The procedure is repeated until everyone finds a satisfactory home (or until the simulator’s patience is exhausted).

Via Bernard Ryefield, Complexity Digest, Ashish Umre
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## An Introduction to Community Detection in Multi-layered Social Network

Piotr Bródka, Tomasz Filipowski, Przemysław Kazienko
(Submitted on 26 Sep 2012)
Social communities extraction and their dynamics are one of the most important problems in today's social network analysis. During last few years, many researchers have proposed their own methods for group discovery in social networks. However, almost none of them have noticed that modern social networks are much more complex than few years ago. Due to vast amount of different data about various user activities available in IT systems, it is possible to distinguish the new class of social networks called multi-layered social network. For that reason, the new approach to community detection in the multi-layered social network, which utilizes multi-layered edge clustering coefficient is proposed in the paper.

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 Rescooped by Frédéric Amblard from Papers

## How People Interact in Evolving Online Affiliation Networks

The concept of social networks, in the age of Twitter and Facebook, seems like a really banal one. Social networks, however, have turned out to be a fertile ground for scientific studies of human interactions by not only social scientists, but also by physicists, from which we gain illuminating insights about ourselves and our societies. For example, why, and how, do we make new friends or establish fresh social ties? In this paper, we show that meaningful answers to these questions can be learned, by bringing concepts and methods from statistical physics to bear in a new analysis of the detailed growth dynamics of two networks associated with two online social-networking sites.

How People Interact in Evolving Online Affiliation Networks

Lazaros K. Gallos, Diego Rybski, Fredrik Liljeros, Shlomo Havlin, and Hernán A. Makse

Via Complexity Digest
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 Rescooped by Frédéric Amblard from Papers

## Urban world: Cities and the rise of the consuming class

Cities have long been the world’s economic dynamos, but today the speed and scale of their expansion are unprecedented. Through a combination of consumption and investment in physical capital, growing cities could inject up to \$30 trillion a year into the world economy by 2025. Understanding cities and their shifting demographics is critical to reaching urban consumers and to preparing for the challenges that will arise from increasing demand for natural resources (such as water and energy) and for capital to invest in new housing, office buildings, and port capacity.

Report|McKinsey Global Institute
Urban world: Cities and the rise of the consuming class
June 2012 | by Richard Dobbs, Jaana Remes, James Manyika, Charles Roxburgh, Sven Smit and Fabian Schaer

Via Complexity Digest
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## Dynamic Network Centrality Summarizes Learning in the Human Brain

Alexander V. Mantzaris, Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, Desmond J. Higham
(Submitted on 20 Jul 2012)
We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of 'learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time progresses. However, the high dimensionality and time-dependent nature of the data makes it difficult to explain which brain regions are driving this distinction. Using network centrality measures that respect the arrow of time, we express the data in an extremely compact form that characterizes the aggregate activity of each brain region in each experiment using a single coefficient, while reproducing information about learning that was discovered using the full data set. This compact summary allows key brain regions contributing to centrality to be visualized and interpreted. We thereby provide a proof of principle for the use of recently proposed dynamic centrality measures on temporal network data in neuroscience.

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 Rescooped by Frédéric Amblard from Network Science

## Long-Range Navigation on Complex Networks using Lévy Random Walks

We introduce a new strategy of navigation in undirected networks, including regular, random and complex networks, that is inspired by L\'evy random walks, generalizing previous navigation rules. We obtained exact expressions for the stationary probability distribution, the occupation probability, the mean first passage time and the average time to reach a node on the network. We found that the long-range navigation using the L\'evy random walk strategy, in comparison with the normal random walk strategy, is more efficient to reduce the time to cover the network. The dynamical effect of using the L\'evy walk strategy is to transform a large-world network into a small world. Our exact results provide a general framework that connects two important fields: L\'evy navigation strategies and dynamics in complex

Via David Rodrigues
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## Activity driven modeling of time varying networks : Scientific Reports : Nature Publishing Group

Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.

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## A Tunable Mechanism for Identifying Trusted Nodes in Large Scale Distributed Networks

In this paper, we propose a simple randomized protocol for identifying trusted nodes based on personalized trust in large scale distributed networks. The problem of identifying trusted nodes, based on personalized trust, in a large network setting stems from the huge computation and message overhead involved in exhaustively calculating and propagating the trust estimates by the remote nodes. However, in any practical scenario, nodes generally communicate with a small subset of nodes and thus exhaustively estimating the trust of all the nodes can lead to huge resource consumption. In contrast, our mechanism can be tuned to locate a desired subset of trusted nodes, based on the allowable overhead, with respect to a particular user. The mechanism is based on a simple exchange of random walk messages and nodes counting the number of times they are being hit by random walkers of nodes in their neighborhood. Simulation results to analyze the effectiveness of the algorithm show that using the proposed algorithm, nodes identify the top trusted nodes in the network with a very high probability by exploring only around 45% of the total nodes, and in turn generates nearly 90% less overhead as compared to an exhaustive trust estimation mechanism, named TrustWebRank. Finally, we provide a measure of the global trustworthiness of a node; simulation results indicate that the measures generated using our mechanism differ by only around 0.6% as compared to TrustWebRank.

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## Predicting the behavior of interacting humans by fusing data from multiple sources

Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but high-fidelity simulations to produce an accurate model of a system of interest at minimal cost. They have proven useful in modeling physical systems and have been applied to engineering problems such as wing-design optimization. During human-in-the-loop experimentation, it has become increasingly common to use online platforms, like Mechanical Turk, to run low-fidelity experiments to gather human performance data in an efficient manner. One concern with these experiments is that the results obtained from the online environment generalize poorly to the actual domain of interest. To address this limitation, we extend traditional multi-fidelity approaches to allow us to combine fewer data points from high-fidelity human-in-the-loop experiments with plentiful but less accurate data from low-fidelity experiments to produce accurate models of how humans interact. We present both model-based and model-free methods, and summarize the predictive performance of each method under different conditions.

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 Rescooped by Frédéric Amblard from Papers

## Social Dynamics of Digg

Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user's friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users' friends from those of interest to the entire user community. We show how this model predicts a story's eventual popularity from users' early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site.

Social Dynamics of Digg