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Rescooped by
Eric L Berlow
from Papers
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Consensus in multi-agents systems can be efficiently used for large-scale optimization problems. Connectivity structure of the consensus network is effective in the convergence to the optimum solution where random structures show better performance as compared to heterogeneous networks. Large-scale global optimization through consensus of opinions over complex networks
Omid Askari Sichani and Mahdi Jalili Complex Adaptive Systems Modeling 2013, 1:11 http://dx.doi.org/10.1186/2194-3206-1-11
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market. Guerrero OA, Axtell RL (2013) Employment Growth through Labor Flow Networks. PLoS ONE 8(5): e60808. http://dx.doi.org/10.1371/journal.pone.0060808
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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A large-scale residential-location model of the Greater London region is being developed in which all stages of the model-building process—from data input, analysis through calibration to prediction—are rapid to execute and accessible in a visual and immediate fashion. The model is structured to distribute trips across competing modes of transport from employment to population locations. It is cast in an entropy-maximising framework which has been extended to measure actual components of energy—travel costs, free energy, and unusable energy (entropy itself)—and these provide indicators for examining future scenarios based on changing the costs of travel in the metro region. Although the model is comparatively static, we interpret its predictions in terms of fast and slow processes—‘fast’ relating to changes in transport modes, and ‘slow’ relating to changes in location. After developing and explaining the model using appropriate visual analytics, a scenario in which road-travel costs double is tested: this shows that mode switching is considerably more significant than shifts in location—which are minimal. Batty M, 2013, "Visually-Driven Urban Simulation: exploring fast and slow change in residential location" Environment and Planning A 45(3) 532 – 552 http://www.envplan.com/abstract.cgi?id=a44153
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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Rescooped by
Eric L Berlow
from Papers
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We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. Noise enhances information transfer in hierarchical networks Agnieszka Czaplicka, Janusz A. Holyst & Peter M. A. Sloot Scientific Reports 3, Article number: 1223 http://dx.doi.org/10.1038/srep01223
Via Complexity Digest
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Suggested by
Anna B. Scott
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Rapid shifts are the hallmark of climate change, epileptic seizures, financial crises, and fishery collapses. Deep mathematical principles tie these events together.
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Rescooped by
Eric L Berlow
from Papers
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Cities are perhaps the ultimate expression of human sociality displaying at once humanity’s greatest achievements and some of its most difficult challenges. Despite the increasing importance of cities in human societies our ability to understand them scientifically, and manage them in practice, has remained unsatisfactorily limited. The greatest difficulties to any scientific approach to cities have resulted from their many interdependent facets, as social, economic, infrastructural and spatial complex systems, which exist in similar but changing forms over a huge range of scales. Here, I show how cities may evolve following a small set of basic principles that operate locally and can explain how cities change gradually from the bottom-up. As a result I obtain a theoretical framework that derives the general open-ended properties of cities through the optimization of a set of local conditions. This framework is used to predict, in a unified and quantitative way, the average social, spatial and infrastructural properties of cities as a set of scaling relations that apply to all urban systems, many of which have been observed in nations around the world. Finally, I compare and contrast the structure and dynamics of cities to those of other complex systems that share some analogous properties. The Origins of Scaling in Cities
Lúis M. A. Bettencourt SFI-WP 12-09-014
Via Complexity Digest
Two bands, Los Angeles-based A House For Lions and Maine's The Mallett Brothers, add up what they've spent while asking you for money. Beware the sunk costs of crowdfunding! -ABS
Via Anna B. Scott
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Rescooped by
Eric L Berlow
from Papers
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Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this paper we demonstrate that universities show a similar behavior as cities in the distribution of the gross university income in terms of total number of citations over size in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling. Universities Scale Like Cities Anthony F. J. van Raan http://arxiv.org/abs/1211.5124
Via Complexity Digest
In spatial games players typically alter their strategy by imitating the most successful or one randomly selected neighbor. Since a single neighbor is taken as reference, the information stemming from other neighbors is neglected, which begets the consideration of alternative, possibly more realistic approaches. Here we show that strategy changes inspired not only by the performance of individual neighbors but rather by entire neighborhoods introduce a qualitatively different evolutionary dynamics that is able to support the stable existence of very small cooperative clusters. This leads to phase diagrams that differ significantly from those obtained by means of pairwise strategy updating. In particular, the survivability of cooperators is possible even by high temptations to defect and over a much wider uncertainty range. We support the simulation results by means of pair approximations and analysis of spatial patterns, which jointly highlight the importance of local information for the resolution of social dilemmas.
Via Shaolin Tan
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Rescooped by
Eric L Berlow
from Papers
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The pace of life accelerates with city size, manifested in a per capita increase of almost all socioeconomic rates such as GDP, wages, violent crime or the transmission of certain contagious diseases. Here, we show that the structure and dynamics of the underlying network of human interactions provides a possible unifying mechanism for the origin of these pervasive regularities. By analyzing billions of anonymized call records from two European countries we find that human social interactions follow a superlinear scale-invariant relationship with city population size. This systematic acceleration of the interaction intensity takes place within specific constraints of social grouping. Together, these results provide a general microscopic basis for a deeper understanding of cities as co-located social networks in space and time, and of the emergent urban socioeconomic processes that characterize complex human societies. The Scaling of Human Interactions with City Size Markus Schläpfer, Luis M. A. Bettencourt, Mathias Raschke, Rob Claxton, Zbigniew Smoreda, Geoffrey B. West, Carlo Ratti http://arxiv.org/abs/1210.5215
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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When networks depend on other networks, such as a communications network that relies on a power grid, failure can cascade back and forth between the two. This behavior may explain sudden breakdowns in interacting systems. Thus, the effects of an attack on a single node can reduce an übernetwork that starts with 12 operating nodes to just four. Once studied solo, systems display surprising behavior when they interact.
Via FuturICT, Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios. Quantifying the Behavior of Stock Correlations Under Market Stress Tobias Preis, Dror Y. Kenett, H. Eugene Stanley, Dirk Helbing & Eshel Ben-Jacob Scientific Reports 2, Article number: 752 http://dx.doi.org/10.1038/srep00752
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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Today’s strongly connected, global networks have produced highly interdependent systems that we do not understand and cannot control well. These systems are vulnerable to failure at all scales, posing serious threats to society, even when external shocks are absent. As the complexity and interaction strengths in our networked world increase, man-made systems can become unstable, creating uncontrollable situations even when decision-makers are well-skilled, have all data and technology at their disposal, and do their best. To make these systems manageable, a fundamental redesign is needed. A ‘Global Systems Science’ might create the required knowledge and paradigm shift in thinking. Globally networked risks and how to respond Dirk Helbing
Nature 497, 51–59 (02 May 2013) http://dx.doi.org/10.1038/nature12047
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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A few years ago, Hawking was asked what he thought of the common opinion that the twentieth century was that of biology and the twenty-first century would be that of physics. Hawking replied that in his opinion the twenty-first century would be the “century of complexity”. That remark probably holds more useful advice for contemporary students than they realize since it points to at least two skills which are going to be essential for new college grads in the age of complexity: statistics and data visualization.
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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We analyze the entire publication database of the American Physical Society generating longitudinal (50 years) citation networks geolocalized at the level of single urban areas. We define the knowledge diffusion proxy, and scientific production ranking algorithms to capture the spatio-temporal dynamics of Physics knowledge worldwide. By using the knowledge diffusion proxy we identify the key cities in the production and consumption of knowledge in Physics as a function of time. The results from the scientific production ranking algorithm allow us to characterize the top cities for scholarly research in Physics. Although we focus on a single dataset concerning a specific field, the methodology presented here opens the path to comparative studies of the dynamics of knowledge across disciplines and research areas. Characterizing scientific production and consumption in Physics Qian Zhang, Nicola Perra, Bruno Gonçalves, Fabio Ciulla & Alessandro Vespignani Scientific Reports 3, Article number: 1640 http://dx.doi.org/10.1038/srep01640
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low. Chavalarias D, Cointet J-P (2013) Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields. PLoS ONE 8(2): e54847. http://dx.doi.org/10.1371/journal.pone.0054847
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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Application of social network analysis to education has revealed how social network positions of K-12 students correlate with their behavior and academic achievements. However, no study has been conducted on how their social network influences their academic progress over time. Here we investigated correlations between high school students’ academic progress over one year and the social environment that surrounds them in their friendship network. We found that students whose friends’ average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network. Blansky D, Kavanaugh C, Boothroyd C, Benson B, Gallagher J, et al. (2013) Spread of Academic Success in a High School Social Network. PLoS ONE 8(2): e55944. http://dx.doi.org/10.1371/journal.pone.0055944
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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Just a decade ago, 'adaptation' was something of a dirty word in the climate arena — an insinuation that nations could continue with business as usual and deal with the mess later. But greenhouse-gas emissions are increasing at an unprecedented rate and countries have failed to negotiate a successor to the Kyoto Protocol climate treaty. That stark reality has forced climate researchers and policy-makers to explore ways to weather some of the inevitable changes.
Via Complexity Digest
Over at the Edge there's a fascinating article by Thomas W. Malone about the work he and others are doing to understand the rise of collective human intelligence — an emergent phenomenon that's being primarily driven by our information technologies. We may be on an evolutionary trajectory, he argues, that could someday give rise to the global brain. And amazingly, he's developing an entirely new scientific discipline to back his case.
Via Viktor Markowski
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Rescooped by
Eric L Berlow
from Papers
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In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries in international and domestic economies and show that these networks are significantly nested. Bustos S, Gomez C, Hausmann R, Hidalgo CA (2012) The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems. PLoS One 7(11): e49393. http://dx.doi.org/10.1371/journal.pone.0049393
Via Complexity Digest
Readers will remember when I announced Ethan Perlstein's plan to crowdfund his scientific research. Crowdfunded research on crowdfunding!
Via Anna B. Scott
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Rescooped by
Eric L Berlow
from Papers
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Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem. Detecting Causality in Complex Ecosystems
George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, Stephan Munch Science 26 October 2012:
Vol. 338 no. 6106 pp. 496-500
http://dx.doi.org/10.1126/science.1227079
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios. Quantifying the Behavior of Stock Correlations Under Market Stress Tobias Preis, Dror Y. Kenett, H. Eugene Stanley, Dirk Helbing & Eshel Ben-Jacob Scientific Reports 2, Article number: 752 http://dx.doi.org/10.1038/srep00752
Via Complexity Digest
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Rescooped by
Eric L Berlow
from Papers
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Tipping points in complex systems may imply risks of unwanted collapse, but also opportunities for positive change. Our capacity to navigate such risks and opportunities can be boosted by combining emerging insights from two unconnected fields of research. One line of work is revealing fundamental architectural features that may cause ecological networks, financial markets, and other complex systems to have tipping points. Another field of research is uncovering generic empirical indicators of the proximity to such critical thresholds. Although sudden shifts in complex systems will inevitably continue to surprise us, work at the crossroads of these emerging fields offers new approaches for anticipating critical transitions. Anticipating Critical Transitions
Marten Scheffer et al. Science 19 October 2012:
Vol. 338 no. 6105 pp. 344-348
http://dx.doi.org/10.1126/science.1225244
Via Complexity Digest
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This research was made mainly by high school students.