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Survivor Curve Shape and Internet Revenue: A Laboratory Experiment

When should a necessary inconvenience be introduced gradually, and when should it be imposed all at once? The question is crucial to web content providers, who in order to generate revenue must sooner or later introduce advertisements, subscription fees, or other inconveniences. In a setting where people eventually fully adapt to changes, the answer depends on the shape of the 'survivor curve' S(x), which represents the fraction of a user population willing to tolerate inconveniences of size x (Aperjis and Huberman 2011).

 

We report a new laboratory experiment that, for the first time, estimates the shape of survivor curves in several different settings. We engage laboratory subjects in a series of six desirable activities, e.g., playing a video game, viewing a chosen video clip, or earning money by answering questions. For each activity we introduce a chosen level x ∈ [xmin,xmax] of a particular inconvenience, and each subject chooses whether to tolerate the inconvenience or to switch to a bland activity for the remaining time. 

 

Our key finding is that the survivor curve is log-concave in all six activities. Theory suggests that web content providers therefore will generally find it profitable to introduce inconveniences gradually over time, with the timing chosen to balance the number of long-term users against more rapid revenue acquisition.

 

Survivor Curve Shape and Internet Revenue: A Laboratory Experiment

Christina Aperjis, Ciril Bosch-Rosa, Daniel Friedman, Bernardo Huberman

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2215403

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Jam avoidance with autonomous systems

Many car-following models are developed for jam avoidance in highways. Two mechanisms are used to improve the stability: feedback control with autonomous models and increasing of the interaction within cooperative ones. In this paper, we compare the linear autonomous and collective optimal velocity (OV) models. We observe that the stability is significantly increased by adding predecessors in interaction with collective models. Yet autonomous and collective approaches are close when the speed difference term is taking into account. Within the linear OV models tested, the autonomous models including speed difference are sufficient to maximise the stability.


Jam avoidance with autonomous systems
Antoine Tordeux, Sylvain Lassarre

http://arxiv.org/abs/1601.07713

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Complex Contagion of Campaign Donations

Money is central in US politics, and most campaign contributions stem from a tiny, wealthy elite. Like other political acts, campaign donations are known to be socially contagious. We study how campaign donations diffuse through a network of more than 50 000 elites and examine how connectivity among previous donors reinforces contagion. We find the diffusion of donations to be driven by independent reinforcement contagion: people are more likely to donate when exposed to donors from different social groups than when they are exposed to equally many donors from the same group. Counter-intuitively, being exposed to one side may increase donations to the other side. Although the effect is weak, simultaneous cross-cutting exposure makes donation somewhat less likely. Finally, the independence of donors in the beginning of a campaign predicts the amount of money that is raised throughout a campaign. We theorize that people infer population-wide estimates from their local observations, with elites assessing the viability of candidates, possibly opposing candidates in response to local support. Our findings suggest that theories of complex contagions need refinement and that political campaigns should target multiple communities.


Complex Contagion of Campaign Donations
V.A. Traag

http://arxiv.org/abs/1601.07679

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Who Benefits from the "Sharing" Economy of Airbnb?

Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of "algorithmic regulation".


Who Benefits from the "Sharing" Economy of Airbnb?
Giovanni Quattrone, Davide Proserpio, Daniele Quercia, Licia Capra, Mirco Musolesi

http://arxiv.org/abs/1602.02238

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A Non-Newtonian Fluid Robot

New types of robots inspired by biological principles of assembly, locomotion, and behavior have been recently described. In this work we explored the concept of robots that are based on more fundamental physical phenomena, such as fluid dynamics, and their potential capabilities. We report a robot made entirely of non-Newtonian fluid, driven by shear strains created by spatial patterns of audio waves. We demonstrate various robotic primitives such as locomotion and transport of metallic loads—up to 6-fold heavier than the robot itself—between points on a surface, splitting and merging, shapeshifting, percolation through gratings, and counting to 3. We also utilized interactions between multiple robots carrying chemical loads to drive a bulk chemical synthesis reaction. Free of constraints such as skin or obligatory structural integrity, fluid robots represent a radically different design that could adapt more easily to unfamiliar, hostile, or chaotic environments and carry out tasks that neither living organisms nor conventional machines are capable of.


A Non-Newtonian Fluid Robot
Guy Hachmon, et al.

Artificial Life

http://dx.doi.org/10.1162/ARTL_a_00194

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What is Information?

Information is a precise concept that can be defined mathematically, but its relationship to what we call "knowledge" is not always made clear. Furthermore, the concepts "entropy" and "information", while deeply related, are distinct and must be used with care, something that is not always achieved in the literature. In this elementary introduction, the concepts of entropy and information are laid out one by one, explained intuitively, but defined rigorously. I argue that a proper understanding of information in terms of prediction is key to a number of disciplines beyond engineering, such as physics and biology.


What is Information?
Christoph Adami

http://arxiv.org/abs/1601.06176


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How ecosystems change

How ecosystems change | Papers | Scoop.it

Human impacts on the planet, including anthropogenic climate change, are reshaping ecosystems in unprecedented ways. To meet the challenge of conserving biodiversity in this rapidly changing world, we must understand how ecological assemblages respond to novel conditions (1). However, species in ecosystems are not fixed entities, even without human-induced change. All ecosystems experience natural turnover in species presence and abundance. Taking account of this baseline turnover in conservation planning could play an important role in protecting biodiversity.


How ecosystems change
Anne E. Magurran

Science  29 Jan 2016:
Vol. 351, Issue 6272, pp. 448-449
http://dx.doi.org/10.1126/science.aad6758

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Disentangling the Effects of Social Signals

In peer recommendation systems, social signals affect item popularity about half as much as position and content do, and further create a "herding" effect that biases people's judgments about the content.


Disentangling the Effects of Social Signals

Tad Hogg & Kristina Lerman ·

Human Computation 2(2)

http://hcjournal.org/ojs/index.php?journal=jhc&page=article&op=view&path%5B%5D=59&path%5B%5D=59 

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Innovation diffusion on time-varying activity driven networks

Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass’ model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.


Innovation diffusion on time-varying activity driven networks
Alessandro Rizzo , Maurizio Porfiri

The European Physical Journal B
January 2016, 89:20

http://dx.doi.org/10.1140/epjb/e2015-60933-3

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Crucial steps to life: From chemical reactions to code using agents

The concepts of the origin of the genetic code and the definitions of life changed dramatically after the RNA world hypothesis.

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Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.


Ranco G, Bordino I, Bormetti G, Caldarelli G, Lillo F, Treccani M (2016) Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics. PLoS ONE 11(1): e0146576. http://dx.doi.org/10.1371/journal.pone.0146576


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Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks

Women are dramatically underrepresented in computer science at all levels in academia and account for just 15% of tenure-track faculty. Understanding the causes of this gender imbalance would inform both policies intended to rectify it and employment decisions by departments and individuals. Progress in this direction, however, is complicated by the complexity and decentralized nature of faculty hiring and the non-independence of hires. Using comprehensive data on both hiring outcomes and scholarly productivity for 2659 tenure-track faculty across 205 Ph.D.-granting departments in North America, we investigate the multi-dimensional nature of gender inequality in computer science faculty hiring through a network model of the hiring process. Overall, we find that hiring outcomes are most directly affected by (i) the relative prestige between hiring and placing institutions and (ii) the scholarly productivity of the candidates. After including these, and other features, the addition of gender did not significantly reduce modeling error. However, gender differences do exist, e.g., in scholarly productivity, postdoctoral training rates, and in career movements up the rankings of universities, suggesting that the effects of gender are indirectly incorporated into hiring decisions through gender's covariates. Furthermore, we find evidence that more highly ranked departments recruit female faculty at higher than expected rates, which appears to inhibit similar efforts by lower ranked departments. These findings illustrate the subtle nature of gender inequality in faculty hiring networks and provide new insights to the underrepresentation of women in computer science.


Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks
Samuel F. Way, Daniel B. Larremore, Aaron Clauset

http://arxiv.org/abs/1602.00795

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Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics

Loss of cortical integration and changes in the dynamics of electrophysiological brain signals characterize the transition from wakefulness towards unconsciousness. In this study, we arrive at a basic model explaining these observations based on the theory of phase transitions in complex systems. We studied the link between spatial and temporal correlations of large-scale brain activity recorded with functional magnetic resonance imaging during wakefulness, propofol-induced sedation and loss of consciousness and during the subsequent recovery. We observed that during unconsciousness activity in frontothalamic regions exhibited a reduction of long-range temporal correlations and a departure of functional connectivity from anatomical constraints. A model of a system exhibiting a phase transition reproduced our findings, as well as the diminished sensitivity of the cortex to external perturbations during unconsciousness. This framework unifies different observations about brain activity during unconsciousness and predicts that the principles we identified are universal and independent from its causes.


Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics
Enzo Tagliazucchi, Dante R. Chialvo, Michael Siniatchkin, Enrico Amico, Jean-Francois Brichant, Vincent Bonhomme, Quentin Noirhomme, Helmut Laufs, Steven Laureys

Interface

http://dx.doi.org/10.1098/rsif.2015.1027

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Global multi-layer network of human mobility

Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different but equally important insights on the global mobility: while the first two highlight short-term visits of people from one country to another, the last one - migration - shows the long-term mobility perspective, when people relocate for good. And the main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. So we start from a comparative study of the network layers, comparing short- and long- term mobility through the statistical properties of the corresponding networks, such as the parameters of their degree centrality distributions or parameters of the corresponding gravity model being fit to the network. We also focus on the differences in country ranking by their short- and long-term attractiveness, discussing the most noticeable outliers. Finally, we apply this multi-layered human mobility network to infer the structure of the global society through a community detection approach and demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.


Global multi-layer network of human mobility
Alexander Belyi, Iva Bojic, Stanislav Sobolevsky, Izabela Sitko, Bartosz Hawelka, Lada Rudikova, Alexander Kurbatski, Carlo Ratti

http://arxiv.org/abs/1601.05532

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A mutual information approach to calculating nonlinearity

A new technique outlined in the paper below allows one to use mutual information not just to measure overall dependence, but the exact fraction of that dependence which is linear (or any other fitted function). Therefore, rather than describing dependence as 'linear' or 'nonlinear', the relatively strength of each in dependence can be measured.


A mutual information approach to calculating nonlinearity
Reginald D. Smith

Stat
Volume 4, Issue 1, pages 291–303, 2015

http://dx.doi.org/10.1002/sta4.96

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How visas shape and make visible the geopolitical architecture of the planet

The aim of the present study is to provide a picture for geopolitical globalization: the role of all world countries together with their contribution towards globalization is highlighted. In the context of the present study, every country owes its efficiency and therefore its contribution towards structuring the world by the position it holds in a complex global network. The location in which a country is positioned on the network is shown to provide a measure of its "contribution" and "importance". As a matter of fact, the visa status conditions between countries reflect their contribution towards geopolitical globalization. Based on the visa status of all countries, community detection reveals the existence of 4+1 main communities. The community constituted by the developed countries has the highest clustering coefficient equal to 0.9. In contrast, the community constituted by the old eastern European blocks, the middle eastern countries, and the old Soviet Union has the lowest clustering coefficient approximately equal to 0.65. PR China is the exceptional case. Thus, the picture of the globe issued in this study contributes towards understanding "how the world works".


How visas shape and make visible the geopolitical architecture of the planet
Meghdad Saeedian, Tayeb Jamali, S. Vasheghani Farahani, G. R. Jafari, Marcel Ausloos

http://arxiv.org/abs/1601.06314

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Scientists make first direct detection of gravitational waves

Scientists make first direct detection of gravitational waves | Papers | Scoop.it
Almost 100 years ago today, Albert Einstein predicted the existence of gravitational waves — ripples in the fabric of space-time that are set off by extremely violent, cosmic cataclysms in the early universe. With his knowledge of the universe and the technology available in 1916, Einstein assumed that such ripples would be “vanishingly small” and nearly impossible to detect. The astronomical discoveries and technological advances over the past century have changed those prospects.
Now for the first time, scientists in the LIGO Scientific Collaboration — with a prominent role played by researchers at MIT and Caltech — have directly observed the ripples of gravitational waves in an instrument on Earth. In so doing, they have again dramatically confirmed Einstein’s theory of general relativity and opened up a new way in which to view the universe.
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Marcelo Errera's curator insight, Today, 12:08 PM
It's a good example of the leaps theorists take when they make claims that makes sense in theory, but it is hard to be proven or accepted at first. Likewise there are theories that seem obvious, but only after someone properly stated it.
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Introduction to Focus Issue: The 25th Anniversary of Chaos: Perspectives on Nonlinear Science—Past, Present, and Future

The first issue of Chaos, published in July of 1991, comprised a selection of 14 now-classic papers authored by leading researchers in nonlinear dynamics.1–14 While some of their distinguished authors—including Vladimir Arnold, Boris Chirikov, and George Zaslavsky—are no longer with us, many of the contributors to the first issue remain active in research and some—Irving Epstein and Leon Glass—are in fact authors of papers in this 25th anniversary issue.


Introduction to Focus Issue: The 25th Anniversary of Chaos: Perspectives on Nonlinear Science—Past, Present, and Future
Elizabeth Bradley, Adilson E. Motter and Louis M. Pecora

Chaos 25, 097501 (2015); http://dx.doi.org/10.1063/1.4931448

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P-values: misunderstood and misused

P-values are widely used in both the social and natural sciences to quantify the statistical significance of observed results. The recent surge of big data research has made p-value an even more popular tool to test the significance of a study. However, substantial literature has been produced critiquing how p-values are used and understood. In this paper we review this recent critical literature, much of which is routed in the life sciences, and consider its implications for social scientific research. We provide a coherent picture of what the main criticisms are, and draw together and disambiguate common themes. In particular, we explain how the False Discovery Rate is calculated, and how this differs from a p-value. We also make explicit the Bayesian nature of many recent criticisms, a dimension that is often underplayed or ignored. We also identify practical steps to help remediate some of the concerns identified, and argue that p-values need to be contextualised within (i) the specific study, and (ii) the broader field of inquiry.


P-values: misunderstood and misused
Bertie Vidgen, Taha Yasseri

http://arxiv.org/abs/1601.06805

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Sentiment analysis and the complex natural language

There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.


Sentiment analysis and the complex natural language
Muhammad Taimoor Khan, Mehr Durrani, Armughan Ali, Irum Inayat, Shehzad Khalid and Kamran Habib Khan

Complex Adaptive Systems Modeling20164:2
http://dx.doi.org/10.1186/s40294-016-0016-9

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System under large stress: Prediction and management of catastrophic failures

The tensile strength of a chain is determined by its weakest link. Does this idea apply to more complex systems too? For instance, does the weakest thread of a spider web initiate cascading failure, when a strong wind gust is stretching the web to its limit? What happens to a computer when both the supply voltage and the ambient temperature are more than 20% outside its normal range of operations?
Climate change, an increasingly more densely populated world and the rapid change of technology seem to put more systems under large stress. Engineering sustainable systems with a more favorable response to large stress appears to be an urgent societal need. Emergency evacuations of hospitals after hurricane Katharina and Sandy, and the May 22, 2011 tornado in Joplin illustrate the urgent need for modeling the adaptive capacity of hospitals during an extended loss of infrastructure [1]. Presidential Policy Directive 21 [2] and the U.S. Department of Homeland Security National Infrastructure Protection Plan (NIPP) [3] call for increasing resilience of the nation’s critical infrastructure.


System under large stress: Prediction and management of catastrophic failures
Alfred Hübler

Complexity
Volume 21, Issue 3, pages 9–12, January/February 2016

http://dx.doi.org/10.1002/cplx.21753

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Google AI algorithm masters ancient game of Go

Google AI algorithm masters ancient game of Go | Papers | Scoop.it

A computer has beaten a human professional for the first time at Go — an ancient board game that has long been viewed as one of the greatest challenges for artificial intelligence (AI).
The best human players of chess, draughts and backgammon have all been outplayed by computers. But a hefty handicap was needed for computers to win at Go. Now Google’s London-based AI company, DeepMind, claims that its machine has mastered the game.

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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre+ et al.

http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html

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A Biologically Inspired Model of Distributed Online Communication Supporting Efficient Search and Diffusion of Innovation

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Critical fluctuations in proteins native states

We study a large data set of protein structure ensembles of very diverse sizes determined by nuclear magnetic resonance. By examining the distance-dependent correlations in the displacement of residues pairs and conducting finite size scaling analysis it was found that the correlations and susceptibility behave as in systems near a critical point implying that, at the native state, the motion of each amino acid residue is felt by every other residue up to the size of the protein molecule. Furthermore certain protein's shapes corresponding to maximum susceptibility were found to be more probable than others. Overall the results suggest that the protein's native state is critical, implying that despite being posed near the minimum of the energy landscape, they still preserve their dynamic flexibility.


Critical fluctuations in proteins native states
Qian-Yuan Tang, Yang-Yang Zhang, Jun Wang, Wei Wang, Dante R. Chialvo

http://arxiv.org/abs/1601.03420

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Scaling and universality in urban economic diversification

Understanding cities is central to addressing major global challenges from climate change to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only recently accessible to comprehensive analyses with the availability of large datasets. Here, we study abundances of business categories across US metropolitan statistical areas, and provide a framework for measuring the intrinsic diversity of economic activities that transcends scales of the classification scheme. A universal structure common to all cities is revealed, manifesting self-similarity in internal economic structure as well as aggregated metrics (GDP, patents, crime). We present a simple mathematical derivation of the universality, and provide a model, together with its economic implications of open-ended diversity created by urbanization, for understanding the observed empirical distribution. Given the universal distribution, scaling analyses for individual business categories enable us to determine their relative abundances as a function of city size. These results shed light on the processes of economic differentiation with scale, suggesting a general structure for the growth of national economies as integrated urban systems.


Scaling and universality in urban economic diversification
Hyejin Youn, Luís M. A. Bettencourt, José Lobo, Deborah Strumsky, Horacio Samaniego, Geoffrey B. West

Interface

http://dx.doi.org/10.1098/rsif.2015.0937

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Energy and resilience: The effects of endogenous interdependencies on trade network formation across space among major Japanese firms

The dynamic drivers of interfirm interactions across space have rarely been explored in the context of disaster recovery; therefore, the mechanism through which shocks propagate is unclear. This paper uses stochastic actor-oriented modeling to examine how trade networks among the 500 largest Japanese companies evolved during 2010 and 2011, i.e. before and after the Great East Japan Earthquake to identify sources of vulnerability in the system. In contrast to previous reports on broken supply chains, the network displayed only modest change even in the directly affected areas. Controlling for distance and for firm size, we find that when firms changed their partners, they preferred firms that were popular among other firms, that had partners in common with them and that also bought some products or services from them. These findings concur with a criticism that Japanese firms avoid external actors and exhibit inflexibility in reorganizing their networks in times of need, which contrasts with the non-cliquish network structures observed in high-performing economic sectors. The results also highlight the role of energy firms in disaster resilience. Unlike other large Japanese companies that cluster in major urban centers, energy firms are distributed across Japan. However, despite their peripheral physical locations, energy firms are centrally located in trade networks. Thus, while a disaster in any region may affect some energy firms and lead to large-scale temporary shocks, the entire network is unlikely to be disconnected by any region-specific disaster because of the spatial distribution of the topological network core formed by energy companies.


Energy and resilience: The effects of endogenous interdependencies on trade network formation across space among major Japanese firms
PETR MATOUS and YASUYUKI TODO

Network Science

http://dx.doi.org/10.1017/nws.2015.37


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