Information, Complexity, Computation | Scoop.it
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All the new curated posts for the topic: Information, Complexity, ComputationMon, 28 Jul 2014 16:09:37 GMTEugene Ch'ng2014-07-28T16:09:37ZInformation, Complexity, Computation | Scoop.ithttp://img.scoop.it/2m8ctkyR7_tP-D_1Hk3qnX96MkXN2Bo-CBIMTdOCamM=
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▶ Jeffrey Johnson: From networks to hypernetworks in complex systems science
http://www.scoop.it/t/information-complexity-computation/p/4024867449/2014/07/19/jeffrey-johnson-from-networks-to-hypernetworks-in-complex-systems-science
<img src='http://img.scoop.it/2m8ctkyR7_tP-D_1Hk3qnTl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>Complex systems have multilevel dynamics emerging from interactions between their parts. Networks have provided deep insights into those dynamics, but only represent relations between two things while the generality is relations between many things. Hypergraphs and their related Galois connections have long been used to model such relations, but their set theoretic nature has inadequate and inappropriate structure. Simplicial complexes can better represent relations between many things but they too have limitations. Hypersimplices, which are defined as simplices in which the relational structure is explicit, overcome these limitations. Hypernetworks, which in the simplest cases are sets of hypersimplices, have a multidimensional connectivity structure which constrains those dynamics represented by patterns of numbers over the hypersimplices and their vertices. The dynamics of hypernetwork also involve the formation and disintegration of hypersimplices, which are seen as structural events related to system time. Hypernetworks provide algebraic structure able to represent multilevel systems and combine their top-down and bottom-up micro, meso and macro-dynamics. Hypernetworks naturally generalise graphs, hypergraphs and networks. These ideas will be presented in a graphical way through examples which also show the relevance of hypernetworks to policy. It will be argued that hypernetworks are necessary if not sufficient for a science of complex systems and its applications. The talk will be aimed at a general audience and no prior knowledge will be assumed.</p><p><br></p><p>10th ECCO / GBI seminar series. Spring 2014 </p><p>From networks to hypernetworks in complex systems science </p><p> April 18, 2014, Brussels </p><p>Jeffrey Johnson Open University, UK </p><p>Slides, references and more: <a href="http://ecco.vub.ac.be/?q=node/231&nbsp" rel="nofollow">http://ecco.vub.ac.be/?q=node/231&nbsp</a>;</p><img src='http://www.scoop.it/rv?p=4024867449&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024867449/2014/07/19/jeffrey-johnson-from-networks-to-hypernetworks-in-complex-systems-science'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>▶ Towards a Self-Regulating Society
http://www.scoop.it/t/information-complexity-computation/p/4024867441/2014/07/19/towards-a-self-regulating-society
<img src='http://img.scoop.it/HJyyz6Tbw52DMuwfzkPc-Dl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> Towards a Self-Regulating Society. Dirk Helbing, ETH Zurich. 2014/05/20</blockquote><img src='http://www.scoop.it/rv?p=4024867441&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024867441/2014/07/19/towards-a-self-regulating-society'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>What humans can learn from semi-intelligent slime
http://www.scoop.it/t/information-complexity-computation/p/4024867438/2014/07/19/what-humans-can-learn-from-semi-intelligent-slime
<img src='http://img.scoop.it/Ewp4vmvdNZv2M8LONge3ljl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>Inspired by biological design and self-organizing systems, artist Heather Barnett co-creates with physarum polycephalum, a eukaryotic microorganism that lives in cool, moist areas. What can people learn from the semi-intelligent slime mold? Watch this talk to find out.</p><p><br></p><p><a href="http://on.ted.com/sz7m" rel="nofollow">http://on.ted.com/sz7m</a></p><img src='http://www.scoop.it/rv?p=4024867438&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024867438/2014/07/19/what-humans-can-learn-from-semi-intelligent-slime'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>▶ Global Brain: Web as Self-organizing Distributed Intelligence - Francis Heylighen
http://www.scoop.it/t/information-complexity-computation/p/4024867435/2014/07/19/global-brain-web-as-self-organizing-distributed-intelligence-francis-heylighen
<img src='http://img.scoop.it/XhROkUWGZ61PH0KID3vMfzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>Distributed intelligence is an ability to solve problems and process information that is not localized inside a single person or computer, but that emerges from the coordinated interactions between a large number of people and their technological extensions. The Internet and in particular the World-Wide Web form a nearly ideal substrate for the emergence of a distributed intelligence that spans the planet, integrating the knowledge, skills and intuitions of billions of people supported by billions of information-processing devices. This intelligence becomes increasingly powerful through a process of self-organization in which people and devices selectively reinforce useful links, while rejecting useless ones. This process can be modeled mathematically and computationally by representing individuals and devices as agents, connected by a weighted directed network along which "challenges" propagate. Challenges represent problems, opportunities or questions that must be processed by the agents to extract benefits and avoid penalties. Link weights are increased whenever agents extract benefit from the challenges propagated along it. My research group is developing such a large-scale simulation environment in order to better understand how the web may boost our collective intelligence. The anticipated outcome of that process is a "global brain", i.e. a nervous system for the planet that would be able to tackle both global and personal problems.</p><p><br></p><p>Summer School in cognitive Science: Web Science and the Mind Institut des sciences cognitives, UQAM, Montréal, Canada <a href="http://www.summer14.isc.uqam.ca" rel="nofollow">http://www.summer14.isc.uqam.ca</a>/</p><p> <a href="http://www.isc.uqam.ca" rel="nofollow">http://www.isc.uqam.ca</a>/ ;</p><p>FRANCIS HEYLIGHEN, Vrije Universiteit Brussel, ECCO - Evolution, Complexity and Cognition research group </p><p>Towards a Global Brain: the Web as a Self-organizing, Distributed Intelligence</p><p><a href="http://youtu.be/w2sznrVtiLg" rel="nofollow">http://youtu.be/w2sznrVtiLg</a></p><img src='http://www.scoop.it/rv?p=4024867435&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024867435/2014/07/19/global-brain-web-as-self-organizing-distributed-intelligence-francis-heylighen'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>The scaling of human interactions with city size
http://www.scoop.it/t/information-complexity-computation/p/4024637557/2014/07/15/the-scaling-of-human-interactions-with-city-size
<p>The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.</p><p> </p><p>Markus Schläpfer, Luís M. A. Bettencourt, Sébastian Grauwin, Mathias Raschke, Rob Claxton, Zbigniew Smoreda, Geoffrey B. West, and Carlo Ratti<br>The scaling of human interactions with city size<br>J. R. Soc. Interface. 2014 11 20130789; <a href="http://dx.doi.org/10.1098/rsif.2013.0789" rel="nofollow">http://dx.doi.org/10.1098/rsif.2013.0789</a></p><img src='http://www.scoop.it/rv?p=4024637557&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024637557/2014/07/15/the-scaling-of-human-interactions-with-city-size'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Co-Following on Twitter
http://www.scoop.it/t/information-complexity-computation/p/4024637543/2014/07/15/co-following-on-twitter
<p>We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. </p><p> </p><p>Co-Following on Twitter<br>Venkata Rama Kiran Garimella, Ingmar Weber</p><p><a href="http://arxiv.org/abs/1407.0791" rel="nofollow">http://arxiv.org/abs/1407.0791</a></p><img src='http://www.scoop.it/rv?p=4024637543&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024637543/2014/07/15/co-following-on-twitter'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Connecting Core Percolation and Controllability of Complex Networks
http://www.scoop.it/t/information-complexity-computation/p/4024636804/2014/07/15/connecting-core-percolation-and-controllability-of-complex-networks
<img src='http://img.scoop.it/zSULZGM2dXvmbsTu5bV3IDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.</p><p> </p><p>Connecting Core Percolation and Controllability of Complex Networks<br> • Tao Jia & Márton Pósfai</p><p>Scientific Reports 4, Article number: 5379 <a href="http://dx.doi.org/10.1038/srep05379" rel="nofollow">http://dx.doi.org/10.1038/srep05379</a></p><img src='http://www.scoop.it/rv?p=4024636804&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024636804/2014/07/15/connecting-core-percolation-and-controllability-of-complex-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Your Brain Is On the Brink of Chaos
http://www.scoop.it/t/information-complexity-computation/p/4024637522/2014/07/15/your-brain-is-on-the-brink-of-chaos
<img src='http://img.scoop.it/q3weiPAKCsk62LXStNa8CDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>In one important way, the recipient of a heart transplant ignores its new organ: Its nervous system usually doesn’t rewire to communicate with it. The 40,000 neurons controlling a heart operate so perfectly, and are so self-contained, that a heart can be cut out of one body, placed into another, and continue to function perfectly, even in the absence of external control, for a decade or more. This seems necessary: The parts of our nervous system managing our most essential functions behave like a Swiss watch, precisely timed and impervious to perturbations. Chaotic behavior has been throttled out.</p><p>Or has it? Two simple pendulums that swing with perfect regularity can, when yoked together, move in a chaotic trajectory. Given that the billions of neurons in our brain are each like a pendulum, oscillating back and forth between resting and firing, and connected to 10,000 other neurons, isn’t chaos in our nervous system unavoidable?</p><p> </p><p><a href="http://nautil.us/issue/15/turbulence/your-brain-is-on-the-brink-of-chaos" rel="nofollow">http://nautil.us/issue/15/turbulence/your-brain-is-on-the-brink-of-chaos</a></p><img src='http://www.scoop.it/rv?p=4024637522&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024637522/2014/07/15/your-brain-is-on-the-brink-of-chaos'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Spatial maximum entropy modeling from presence/absence tropical forest data
http://www.scoop.it/t/information-complexity-computation/p/4024635815/2014/07/15/spatial-maximum-entropy-modeling-from-presence-absence-tropical-forest-data
<p>Understanding the assembly of ecosystems to estimate the number of species at different spatial scales is a challenging problem. Until now, maximum entropy approaches have lacked the important feature of considering space in an explicit manner. We propose a spatially explicit maximum entropy model suitable to describe spatial patterns such as the species area relationship and the endemic area relationship. Starting from the minimal information extracted from presence/absence data, we compare the behavior of two models considering the occurrence or lack thereof of each species and information on spatial correlations. Our approach uses the information at shorter spatial scales to infer the spatial organization at larger ones. We also hypothesize a possible ecological interpretation of the effective interaction we use to characterize spatial clustering. (<a href="http://arxiv.org/abs/1407.2425" rel="nofollow">http://arxiv.org/abs/1407.2425</a>)</p><img src='http://www.scoop.it/rv?p=4024635815&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4024635815/2014/07/15/spatial-maximum-entropy-modeling-from-presence-absence-tropical-forest-data'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>[1405.6707] Understanding the spreading power of all nodes in a network: a continuous-time perspective
http://www.scoop.it/t/information-complexity-computation/p/4022418103/2014/06/03/1405-6707-understanding-the-spreading-power-of-all-nodes-in-a-network-a-continuous-time-perspective
<img src='http://www.scoop.it/rv?p=4022418103&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4022418103/2014/06/03/1405-6707-understanding-the-spreading-power-of-all-nodes-in-a-network-a-continuous-time-perspective'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Polar Swarms
http://www.scoop.it/t/information-complexity-computation/p/4019530785/2014/04/13/polar-swarms
<img src='http://img.scoop.it/yntRk-6s3aEuCQcqQsHbdDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> A new theory can explain the formation of swarming patterns observed in ensembles of self-propelled polar particles.</blockquote><br/>Eugene Ch'ng's insight:<br/>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.”<br/>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...<img src='http://www.scoop.it/rv?p=4019530785&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4019530785/2014/04/13/polar-swarms'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>How Complex Contagions Spread and Spread Quickly
http://www.scoop.it/t/information-complexity-computation/p/4019458818/2014/04/11/how-complex-contagions-spread-and-spread-quickly
<br/>Eugene Ch'ng's insight:<br/>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.<img src='http://www.scoop.it/rv?p=4019458818&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4019458818/2014/04/11/how-complex-contagions-spread-and-spread-quickly'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Network communities within and across borders
http://www.scoop.it/t/information-complexity-computation/p/4019320157/2014/04/10/network-communities-within-and-across-borders
<img src='http://img.scoop.it/SnoIgBneZ2RifZNhtZfDsTl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> 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.</blockquote><img src='http://www.scoop.it/rv?p=4019320157&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4019320157/2014/04/10/network-communities-within-and-across-borders'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>In the Company of Wealth | Dragons and Pandas | Big Think
http://www.scoop.it/t/information-complexity-computation/p/4019321054/2014/04/10/in-the-company-of-wealth-dragons-and-pandas-big-think
<img src='http://img.scoop.it/_Gt1oIDBa08P23AwfQy-6zl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> “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...</blockquote><img src='http://www.scoop.it/rv?p=4019321054&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4019321054/2014/04/10/in-the-company-of-wealth-dragons-and-pandas-big-think'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Topics in social network analysis and network science
http://www.scoop.it/t/information-complexity-computation/p/4019321051/2014/04/10/topics-in-social-network-analysis-and-network-science
<p>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.</p><p>by A. James O'Malley, Jukka-Pekka Onnela</p><p>arXiv:1404.0067 [physics.soc-ph]</p><img src='http://www.scoop.it/rv?p=4019321051&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4019321051/2014/04/10/topics-in-social-network-analysis-and-network-science'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Predicting Successful Memes using Network and Community Structure
http://www.scoop.it/t/information-complexity-computation/p/4019319955/2014/04/10/predicting-successful-memes-using-network-and-community-structure
<img src='http://img.scoop.it/gWyH9FCnrR0Icf_dudq0jDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><img src='http://www.scoop.it/rv?p=4019319955&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4019319955/2014/04/10/predicting-successful-memes-using-network-and-community-structure'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Financial Brownian particle in the layered order-book fluid and fluctuation-dissipation relations
http://www.scoop.it/t/information-complexity-computation/p/4017182006/2014/03/07/financial-brownian-particle-in-the-layered-order-book-fluid-and-fluctuation-dissipation-relations
<br/>Eugene Ch'ng's insight:<br/>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.<img src='http://www.scoop.it/rv?p=4017182006&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017182006/2014/03/07/financial-brownian-particle-in-the-layered-order-book-fluid-and-fluctuation-dissipation-relations'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Twitter 'big data' can be used to monitor HIV, drug-related behavior
http://www.scoop.it/t/information-complexity-computation/p/4017167262/2014/03/07/twitter-big-data-can-be-used-to-monitor-hiv-drug-related-behavior
<img src='http://img.scoop.it/hwXZoY_NNKPQiKtzxtkuSTl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> 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.</blockquote><img src='http://www.scoop.it/rv?p=4017167262&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017167262/2014/03/07/twitter-big-data-can-be-used-to-monitor-hiv-drug-related-behavior'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Damage spreading in spatial and small-world random Boolean networks
http://www.scoop.it/t/information-complexity-computation/p/4017165430/2014/03/07/damage-spreading-in-spatial-and-small-world-random-boolean-networks
<p>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.</p><p> </p><p>Qiming Lu and Christof Teuscher<br>Damage spreading in spatial and small-world random Boolean networks<br>Phys. Rev. E 89, 022806 (2014)</p><p><a href="http://pre.aps.org/abstract/PRE/v89/i2/e022806" rel="nofollow">http://pre.aps.org/abstract/PRE/v89/i2/e022806</a></p><img src='http://www.scoop.it/rv?p=4017165430&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017165430/2014/03/07/damage-spreading-in-spatial-and-small-world-random-boolean-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>How to Save Human Lives with Complexity Science
http://www.scoop.it/t/information-complexity-computation/p/4017167196/2014/03/07/how-to-save-human-lives-with-complexity-science
<br/>Eugene Ch'ng's insight:<br/>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.<img src='http://www.scoop.it/rv?p=4017167196&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017167196/2014/03/07/how-to-save-human-lives-with-complexity-science'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>The strength of ‘weak signals’
http://www.scoop.it/t/information-complexity-computation/p/4017165414/2014/03/07/the-strength-of-weak-signals
<img src='http://img.scoop.it/xYBas7yTcfo9peF4dgbpojl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>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.</p><img src='http://www.scoop.it/rv?p=4017165414&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017165414/2014/03/07/the-strength-of-weak-signals'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>The Relative Ineffectiveness of Criminal Network Disruption
http://www.scoop.it/t/information-complexity-computation/p/4017164465/2014/03/07/the-relative-ineffectiveness-of-criminal-network-disruption
<p>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.</p><p> </p><p>The Relative Ineffectiveness of Criminal Network Disruption<br>Paul A. C. Duijn, Victor Kashirin & Peter M. A. Sloot</p><p>Scientific Reports 4, Article number: 4238 http://dx.doi.org/10.1038/srep04238 ;</p><p> </p><p>See also documentary at <a href="http://www.youtube.com/watch?v=Qhk9ciHlzzo&nbsp" rel="nofollow">http://www.youtube.com/watch?v=Qhk9ciHlzzo&nbsp</a>;</p><img src='http://www.scoop.it/rv?p=4017164465&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017164465/2014/03/07/the-relative-ineffectiveness-of-criminal-network-disruption'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Guided Self-Organisation
http://www.scoop.it/t/information-complexity-computation/p/4017165409/2014/03/07/guided-self-organisation
<p>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.<br>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.</p><p> </p><p><a href="http://guided-self.org" rel="nofollow">http://guided-self.org</a></p><img src='http://www.scoop.it/rv?p=4017165409&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017165409/2014/03/07/guided-self-organisation'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Crime rates could rise as climate change bites - environment - 28 February 2014 - New Scientist
http://www.scoop.it/t/information-complexity-computation/p/4017165325/2014/03/07/crime-rates-could-rise-as-climate-change-bites-environment-28-february-2014-new-scientist
<img src='http://img.scoop.it/GzV5enNqM1VckzC_CQ5Tqzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> As temperatures soar, so do crime rates – suggesting climate change will lead to millions of extra offences in the coming decades</blockquote><img src='http://www.scoop.it/rv?p=4017165325&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4017165325/2014/03/07/crime-rates-could-rise-as-climate-change-bites-environment-28-february-2014-new-scientist'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Crowd-sourcing: Strength in numbers
http://www.scoop.it/t/information-complexity-computation/p/4016873935/2014/03/02/crowd-sourcing-strength-in-numbers
<img src='http://img.scoop.it/Sz_9zBGCmvJpanOPyhLlQDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> Researchers are finding that online, crowd-sourced collaboration can speed up their work — if they choose the right problem.</blockquote><img src='http://www.scoop.it/rv?p=4016873935&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4016873935/2014/03/02/crowd-sourcing-strength-in-numbers'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Information Evolution in Social Networks
http://www.scoop.it/t/information-complexity-computation/p/4016873932/2014/03/02/information-evolution-in-social-networks
<p>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.</p><p><br></p><p>Information Evolution in Social Networks<br>Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng</p><p><a href="http://arxiv.org/abs/1402.6792" rel="nofollow">http://arxiv.org/abs/1402.6792</a></p><img src='http://www.scoop.it/rv?p=4016873932&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4016873932/2014/03/02/information-evolution-in-social-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Predicting Crowd Behavior with Big Public Data
http://www.scoop.it/t/information-complexity-computation/p/4016873929/2014/03/02/predicting-crowd-behavior-with-big-public-data
<p>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.</p><p><br></p><p>Predicting Crowd Behavior with Big Public Data<br>Nathan Kallus</p><p><a href="http://arxiv.org/abs/1402.2308" rel="nofollow">http://arxiv.org/abs/1402.2308</a></p><img src='http://www.scoop.it/rv?p=4016873929&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4016873929/2014/03/02/predicting-crowd-behavior-with-big-public-data'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>The Math That Predicted the Revolutions Sweeping the Globe Right Now
http://www.scoop.it/t/information-complexity-computation/p/4016398173/2014/02/22/the-math-that-predicted-the-revolutions-sweeping-the-globe-right-now
<img src='http://img.scoop.it/4NkOBPU-uhXypbuKEnzc3Dl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> The complex systems theorists who predicted the Arab Spring built a model that predicted the unrest in Ukraine, Venezuela, and Thailand too.</blockquote><img src='http://www.scoop.it/rv?p=4016398173&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4016398173/2014/02/22/the-math-that-predicted-the-revolutions-sweeping-the-globe-right-now'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>SFI: notes for the history of Complex Systems Science
http://www.scoop.it/t/information-complexity-computation/p/4015400807/2014/02/05/sfi-notes-for-the-history-of-complex-systems-science
<p>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. </p><p>By John German</p><p> </p><p>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. (...)</p><img src='http://www.scoop.it/rv?p=4015400807&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4015400807/2014/02/05/sfi-notes-for-the-history-of-complex-systems-science'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>Simulating the past to understand human history: satellite in 10th conference of the European Social Simulation Association (ESSA)
http://www.scoop.it/t/information-complexity-computation/p/4015399904/2014/02/05/simulating-the-past-to-understand-human-history-satellite-in-10th-conference-of-the-european-social-simulation-association-essa
<p>SIMULATING THE PAST TO UNDERSTAND HUMAN HISTORY</p><p> </p><p>The conference is organized with the contribution of the SimulPast project(<a href="http://www.simulpast.es" rel="nofollow">www.simulpast.es</a>), 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.</p><p> </p><p>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</p><img src='http://www.scoop.it/rv?p=4015399904&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/information-complexity-computation/p/4015399904/2014/02/05/simulating-the-past-to-understand-human-history-satellite-in-10th-conference-of-the-european-social-simulation-association-essa'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/information-complexity-computation'>Information, Complexity, Computation</a></div><div style='clear: both'></div>