Satellite imagery can serve as a time machine, revealing dramatic change in just a few seconds — but can you imagine documenting almost three decades' worth of all that change, across most of our planet's land mass?
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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 Delete the scoop?
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What is your favorite deep, elegant, or beautiful explanation? This is the question John Brockman, publisher of Edge.org, posed to the world's most influential minds. Flowing from the horizons of physics, economics, psychology, neuroscience, and more, This Explains Everything presents 150 of the most surprising and brilliant theories of the way of our minds, societies, and universe work.
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In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In the present paper, we review the literature related to the areas of agent-based traffic modelling and simulation, and agent-based traffic control and management. Later we discuss and summarize the main achievements and the challenges. Delete the scoop?
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Researchers have developed the first global model that analyses the routes taken by marine invasive species. Delete the scoop?
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Slime mould Physarum polycephalum is large single cell with intriguingly smart behaviour. The slime mould shows outstanding abilities to adapt its protoplasmic network to varying environmental conditions. The slime mould can solve tasks of computational geometry, image processing, logics and arithmetics when data are represented by configurations of attractants and repellents. We attempt to map behavioural patterns of slime onto the cognitive control versus schizotypy spectrum phase space and thus interpret slime mould's activity in terms of creativity. Delete the scoop?
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Dr Alex Penn speaks at the first TEDxSouthamptonUniversity event on the 17th March 2013. For details about the day including videos of other talks, please vi... Delete the scoop?
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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 
Murray McKercher's curator insight,
April 30, 7:39 AM
"century of complexity" sounds like we should therefore concentrate on simplicity in all things mobile... Delete the scoop?
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Transcript of Stephen Wolfram’s SXSW 2013 presentation. Discusses his vision of what computation will do for people in the future. Not only as it applies to science and knowledge discovery, but also as it pertains to personal well-being. Via Complexity Digest Delete the scoop?
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We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (“optimization variance”) and over randomizations of network structure (“randomization variance”). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.
Robust detection of dynamic community structure in networks Chaos 23, 013142 (2013); http://dx.doi.org/10.1063/1.4790830 ; Via Complexity Digest 
ComplexInsight's curator insight,
April 22, 4:50 PM
Good catch by Eugene and the Complexity Digest team. Ability to examine network structure communal and adhoc in time-dependent networks will become increasingly important with complex systems analysis. Interesting read. Delete the scoop?
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We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.
Neural Computation and the Computational Theory of Cognition Gualtiero Piccinini, Sonya Bahar Cognitive Science http://dx.doi.org/10.1111/cogs.12012 Via Complexity Digest
ComplexInsight's curator insight,
April 15, 2:53 AM
Re-reading some of John Holland's work on neural network simulation at present while looking into different models of computation and digital physics, so this is a timely paper. Looks to be an interesting read. Delete the scoop?
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Our species can’t seem to escape big data. We have more data inputs, storage, and computing resources than ever, so Homo sapiens naturally does what it has always done when given new tools: it goes even bigger, higher, and bolder. Delete the scoop?
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Social networking services (e.g., Twitter, Facebook) are now major sources of World Wide Web (called “Web”) dynamics, together with Web search services (e.g., Google). These two types of Web services mutually influence each other but generate different dynamics. In this paper, we distinguish two modes of Web dynamics: the reactive mode and the default mode. It is assumed that Twitter messages (called “tweets”) and Google search queries react to significant social movements and events, but they also demonstrate signs of becoming self-activated, thereby forming a baseline Web activity. We define the former as the reactive mode and the latter as the default mode of the Web. In this paper, we investigate these reactive and default modes of the Web's dynamics using transfer entropy (TE). The amount of information transferred between a time series of 1,000 frequent keywords in Twitter and the same keywords in Google queries is investigated across an 11-month time period.(...)
Oka M, Ikegami T (2013) Exploring Default Mode and Information Flow on the Web. PLoS ONE 8(4): e60398. http://dx.doi.org/10.1371/journal.pone.0060398 Via Complexity Digest 
luiy's curator insight,
May 10, 9:12 AM
Discussion
This paper explored how to define the Web's reactive and default modes by information transfer by computing TE to characterize the inherent structure of the Web dynamics. First, we defined whether a keyword is in default or reactive mode in terms of how burst events are caused internally or externally. There are reports on YouTube page views and Twitter hashtags, whereby internally and externally caused bursts are distinguished by certain criteria[17], [19]. Our analysis of the number of bursts in relation to keyword frequency revealed that while low-frequency keywords tend to burst more, keywords are more influenced by real-world events, when compared to high-frequency keywords. From this observation, we defined that high-frequency keywords form the Web's default mode network and low-frequency keywords constitute the Web's reactive mode. When analyzing the information transfer between Google and Twitter, we found that information is mostly transferred from Twitter to Google and that this tendency is more apparent for high-frequency keywords than for low-frequency keywords. We also studied the information flow network formed among Twitter keywords by taking the keywords as nodes and flow direction as the edges of a network. We found that high-frequency keywords tend to become information sources and low-frequency keywords tend to become information sinks. These findings suggest that we can use high-frequency keywords (or default mode of the Web) to reduce uncertainty with the externally driven low-frequency keywords (or reactive mode of the Web). However, it is fair to assume that frequently searched keywords in Google are different from the frequent keywords found on Twitter. Thus, if we investigated the high-frequency keywords found in Google queries, the results may be different. The concept of reactive and default modes originates from brain science [20]–[22]. A brain region responsible for a given task is identified by measuring the neural activity that is observably higher compared to the baseline activity. Raichle et al. [23] examined the baseline activity by analyzing the regions that become less active when a specific task is given. This successful approach uncovered some remarkable perspectives and characteristics of the default mode; based on Buckner's [22] and Raichle's [24] reviews, these are: i) the area associated with the default mode is found as the integration of various subsystems in the brain - the medial prefrontal cortex and posterior cingulate cortex subsystems seem to play central roles. ii) The neural activity of the aforementioned subsystems were observed as noisy fMRI signals at a low frequency of about 0.1 Hz or less, showing global synchronization. iii) The default mode is to do with spontaneous cognition e.g., day dreaming and internal thoughts such as future planning. iv) The activity of the default mode is anti-correlated with the other brain regions that are responsible for focusing attention on the external world; and v) the brain region associated with the default mode overlaps with those involved in the construction of episodic memory. This notion of the default mode can be generalized for any living systems with or without brain systems. In the case of the Web system, it can be said that 1) frequent keywords constitute the default mode (mostly everyday keywords), 2) these frequent keywords display less frequent bursting behaviors and are an information source for other keywords, 3) the default mode may help reduce uncertainty in the entire Web system, and 4) the default mode comprises quasi-periodic time series. From this comparison with the default mode network in brain systems, and in particular with the possibility that high-frequency keywords may help to predict essentially unpredictable events, it becomes apparent the Web's default mode may have the same property as the default modes in the brain. Differentiating between these two modes, the reactive and the default, provides a useful perspective for understanding Web dynamics and predicting the future of bursting behavior in the time series of keyword frequencies in tweets in Twitter, as well as in the time series of search queries in Google. With respect to the examples of complex networks in general, we believe that the default mode is key for understanding autonomy in complex systems in general. Any autonomous system (e.g., robots) possesses primitive forms of the default mode with different time scales [25]. Delete the scoop?
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The fixation of cooperation among unrelated individuals is one of the fundamental problems in biology and social sciences. It is investigated by means of public goods games, the generalization of the prisoner's dilemma to more than two players. In compulsory public goods games, defect is the dominant strategy, while voluntary participation overcomes the social dilemma by allowing a cyclic coexistence of cooperators, defectors, and non-participants. [...] a milder punishing mechanism, where defectors only risk a fixed penalty per round—as in many real situations—and the cost of punishment is shared among the punishers. The payoffs for the four strategies—cooperate, defect, abstain, and cooperate-&-punish—are derived and the corresponding replicator dynamics analyzed in full detail. Via Complexity Digest Delete the scoop?
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In Systematicity, Paul Hoyningen-Huene answers the question "What is science?" by proposing that scientific knowledge is primarily distinguished from other forms of knowledge, especially everyday knowledge, by being more systematic. "Science" is here understood in the broadest possible sense, encompassing not only the natural sciences but also mathematics, the social sciences, and the humanities. The author develops his thesis in nine dimensions in which it is claimed that science is more systematic than other forms of knowledge: regarding descriptions, explanations, predictions, the defense of knowledge claims, critical discourse, epistemic connectedness, an ideal of completeness, knowledge generation, and the representation of knowledge. He compares his view with positions on the question held by philosophers from Aristotle to Nicholas Rescher. The book concludes with an exploration of some consequences of Hoyningen-Huene's view concerning the genesis and dynamics of science, the relationship of science and common sense, normative implications of the thesis, and the demarcation criterion between science and pseudo-science.
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Throughout humankind's history, we've driven species after species extinct: the passenger pigeon, the Eastern cougar, the dodo ... But now, says Stewart Brand, we have the technology (and the biology) to bring back species that humanity wiped out. So -- should we? Which ones? He asks a big question whose answer is closer than you may think. Via Complexity Digest Delete the scoop?
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In classical physics, one is taught that given the initial state of a system, all of its future states can be calculated. In the celebrated words of Pierre Simon Laplace, “An intelligence which could comprehend all the forces by which nature is animated and the respective situation of the beings who compose it—an intelligence sufficiently vast to submit these data to analysis . . . for it, nothing would be uncertain and the future, as the past, would be present to its eyes.” Or, put another way, the clockwork universe holds true.... Delete the scoop?
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Networks are ubiquitous and the scientific discipline of network science has flourished in the last decade. As a means to study complex interactions, two particular application areas are social and information science. On the one hand side, the www as a pool of hyper-linked information can be represented with the help of networks (and this representation is the basis for google’s Page-rank algorithm), on the other hand side services like facebook, twitter or flickr provide the means for people to establish social networks of never-seen size – and hence provide the basis for what is now called computational sociology. The scope of this this summer school is to provide PhD students and early PostDocs with a comprehensive 1-week insight into the “power of networks” in an information science and social media setting. In Six to nine lectures, established and well-known researchers (from Europe, US and Asia) will present cutting-edge research as well as provide the participants with valuable insight into challenges and methods. The Summer School will take place from 10th to 15th June in Höllviken (south of Sweden, 1h by train from Copenhagen, Denmark), right after NetSci 2013.
http://networkfactory2013.wordpress.com Via Complexity Digest Delete the scoop?
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We study the asymptotic behaviour of symbolic computing systems, notably one-dimensional cellular automata (CA), in order to ascertain whether and at what rate the number of complex versus simple rules dominate the rule space for increasing neighbourhood range and number of symbols (or colours), and how different behaviour is distributed in the spaces of different cellular automata formalisms. Using two different measures, Shannon's block entropy and Kolmogorov complexity, the latter approximated by two different methods (lossless compressibility and block decomposition), we arrive at the same trend of larger complex behavioural fractions. We also advance a notion of asymptotic and limit behaviour for individual rules, both over initial conditions and runtimes, and we provide a formalisation of Wolfram's classification as a limit function in terms of Kolmogorov complexity. Delete the scoop?
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University of Georgia researchers used a Facebook page to determine the trajectories of tornado debris. Delete the scoop?
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Sporulation vs. competence provides a prototypic example of collective cell fate determination. The decision is performed by the action of three modules: 1) A stochastic competence switch whose transition probability is regulated by population density, population stress and cell stress. 2) A sporulation timer whose clock rate is regulated by cell stress and population stress. 3) A decision gate that is coupled to the timer via a special repressilator-like loop. We show that the distinct circuit architecture of this gate leads to special dynamics and noise management characteristics: The gate opens a time-window of opportunity for competence transitions during which it generates oscillations that are turned into a chain of transition opportunities – each oscillation opens a short interval with high transition probability. The special architecture of the gate also leads to filtering of external noise and robustness against internal noise and variations in the circuit parameters.
Turning Oscillations Into Opportunities: Lessons from a Bacterial Decision Gate Daniel Schultz, Mingyang Lu, Trevor Stavropoulos, Jose' Onuchic & Eshel Ben-Jacob Scientific Reports 3, Article number: 1668 http://dx.doi.org/10.1038/srep01668 Via Complexity Digest Delete the scoop?
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GigaOM contributor James Urquhart shares some of the best books, blogs and other information on the concepts of devops and complex IT systems. Delete the scoop?
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“FOUNDATION”, a novel by Isaac Asimov from the golden age of science fiction, imagines a science called psychohistory which enables its practitioners to predict... Via Complejidady Economía Delete the scoop?
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