复杂文摘翻译第五期-(摘自Complex Digest 2016.2月文章)

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复杂文摘翻译第五期-(摘自Complex Digest 2016.2月文章)


Scaling and universality in urban economic diversification

Network Science / Volume 4 / Issue 02 / June 2016, pp 141-163 DOI: 10.1098/rsif.2015.0937

by Hyejin Youn, Luís M. A. Bettencourt, José Lobo, Deborah Strumsky, Horacio Samaniego, Geoffrey B. West

(Translated by -)

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.



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

Journal of The Royal Society Interface January 2016 Volume 13, issue 114 DOI: 10.1098/rsif.2015.1027

by Enzo Tagliazucchi, Dante R. Chialvo, Michael Siniatchkin, Enrico Amico, Jean-Francois Brichant, Vincent Bonhomme, Quentin Noirhomme, Helmut Laufs, Steven Laureys

(Translated by -)

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.



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

Proc. 2016 World Wide Web Conference (WWW), 1169-1179 (2016) arXiv:1602.00795v1 [cs.SI] (Submitted on 2 Feb 2016)

by Samuel F. Way, Daniel B. Larremore, Aaron Clauset

(Translated by -)

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.



Critical fluctuations in proteins native states

arXiv:1601.03420v1 [physics.bio-ph] (Submitted on 13 Jan 2016)

by Qian-Yuan Tang, Yang-Yang Zhang, Jun Wang, Wei Wang, Dante R. Chialvo

(Translated by -)

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.

这篇文章是我写的文章,如有必要,到时候我自己来翻译。(by @傅渥成)


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

Interdisciplinary Description of Complex Systems 14(1), 10-22, 2016

by Soumya Banerjee

(Translated by -)

We inhabit a world that is not only “small” but supports efficient decentralized search – an individual using local information can establish a line of communication with another completely unknown individual. Here we augment a hierarchical social network model with communication between and within communities. We argue that organization into communities would decrease overall decentralized search times. We take inspiration from the biological immune system which organizes search for pathogens in a hybrid modular strategy. Our strategy has relevance in search for rare amounts of information in online social networks and could have implications for massively distributed search challenges. Our work also has implications for design of efficient online networks that could have an impact on networks of human collaboration, scientific collaboration and networks used in targeted manhunts. Real world systems, like online social networks, have high associated delays for long-distance links, since they are built on top of physical networks. Such systems have been shown to densify i.e. the average number of neighbours that an individual has increases with time. Hence such networks will have a communication cost due to space and the requirement of building and maintaining and increasing number of connections. We have incorporated such a non-spatial cost to communication in order to introduce the realism of individuals communicating within communities, which we call participation cost. We introduce the notion of a community size that increases with the size of the system, which is shown to reduce the time to search for information in networks. Our final strategy balances search times and participation costs and is shown to decrease time to find information in decentralized search in online social networks. Our strategy also balances strong-ties (within communities) and weak-ties over long distances (between communities that bring in diverse ideas) and may ultimately lead to more productive and innovative networks of human communication and enterprise. We hope that this work will lay the foundation for strategies aimed at producing global scale human interaction networks that are sustainable and lead to a more networked, diverse and prosperous society.



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

PLoS ONE 11(1): e0146576. DOI: 10.1371/journal.pone.0146576

by Gabriele Ranco , Ilaria Bordino, Giacomo Bormetti, Guido Caldarelli, Fabrizio Lillo, Michele Treccani

(Translated by -)

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.



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

Biosystems Volume 140, February 2016, Pages 49–57 DOI: 10.1016/j.biosystems.2015.12.007

by Guenther Witzany

(Translated by -)

The concepts of the origin of the genetic code and the definitions of life changed dramatically after the RNA world hypothesis. Main narratives in molecular biology and genetics such as the “central dogma,” “one gene one protein” and “non-coding DNA is junk” were falsified meanwhile. RNA moved from the transition intermediate molecule into centre stage. Additionally the abundance of empirical data concerning non-random genetic change operators such as the variety of mobile genetic elements, persistent viruses and defectives do not fit with the dominant narrative of error replication events (mutations) as being the main driving forces creating genetic novelty and diversity. The reductionistic and mechanistic views on physico-chemical properties of the genetic code are no longer convincing as appropriate descriptions of the abundance of non-random genetic content operators which are active in natural genetic engineering and natural genome editing.



Google AI algorithm masters ancient game of Go

Nature 529, 445–446 (28 January 2016) DOI: 10.1038/529445a

by Reginald Smith

(Translated by -)

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.



Innovation diffusion on time-varying activity driven networks

The European Physical Journal B, 2016, Volume 89, Number 1, Page 1

by Alessandro Rizzo, Maurizio Porfiri

(Translated by -)

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.



Disentangling the Effects of Social Signals

Human Computation (2015) 2:2:189-208 DOI: 10.15346/hc.v2i2.4

by Tad Hogg, Kristina Lerman

(Translated by -)

Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify inter- esting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presenta- tion order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the “rich get richer” phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a “herding” effect that biases people’s judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.



Sentiment analysis and the complex natural language

Complex Adaptive Systems Modeling 20164:2 DOI: 10.1186/s40294-016-0016-9

by Muhammad Taimoor KhanEmail author, Mehr Durrani, Armughan Ali, Irum Inayat, Shehzad Khalid and Kamran Habib Khan

(Translated by -)

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.



System under large stress: Prediction and management of catastrophic failures

Complexity Volume 21, Issue 3, pages 9–12, January/February 2016 DOI: 10.1002/cplx.21753

by Alfred Hübler

(Translated by -)

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.



How ecosystems change

Science 29 Jan 2016:Vol. 351, Issue 6272, pp. 448-449 DOI: 10.1126/science.aad6758

by Anne E. Magurran

(Translated by -)

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.



P-values: misunderstood and misused

Frontiers in Physics: Vidgen B and Yasseri T (2016) P-Values: Misunderstood and Misused. Front. Phys. 4:6 DOI: 10.3389/fphy.2016.00006

by Bertie Vidgen, Taha Yasseri

(Translated by -)

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 the 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 conclude by identifying practical steps to help remediate some of the concerns identified. We recommend that (i) far lower significance levels are used, such as 0.01 or 0.001, and (ii) p-values are interpreted contextually, and situated within both the findings of the individual study and the broader field of inquiry (through, for example, meta-analyses).



What is Information?

Philosophical Transaction of the Royal Society A 374 (2016) 20150230 DOI: 10.1098/rsta.2015.0230

by Christoph Adami

(Translated by -)

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.



Introduction to Focus Issue: The 25th Anniversary of Chaos: Perspectives on Nonlinear Science—Past, Present, and Future

Chaos 25, 097501 (2015)

by Elizabeth Bradley, Adilson E. Motter and Louis M. Pecora

(Translated by -)

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.



A Non-Newtonian Fluid Robot

Artificial Life Winter 2016, Vol. 22, No. 1, Pages 1-22 DOI: 10.1162/ARTL_a_00194

by Guy Hachmon, et al.

(Translated by -)

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.



Scientists make first direct detection of gravitational waves

MIT News February 11, 2016

by Jennifer Chu

(Translated by -)

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.



Who Benefits from the "Sharing" Economy of Airbnb?

arXiv:1602.02238v1 [cs.SI] (Submitted on 6 Feb 2016)

by Giovanni Quattrone, Davide Proserpio, Daniele Quercia, Licia Capra, Mirco Musolesi

(Translated by -)

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".



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

arXiv:1601.06314v1 [physics.soc-ph] (Submitted on 23 Jan 2016)

by Meghdad Saeedian, Tayeb Jamali, S. Vasheghani Farahani, G. R. Jafari, Marcel Ausloos

(Translated by -)

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".



Complex Contagion of Campaign Donations

PLoS ONE 2016, 11(4): e0153539 DOI: 10.1371/journal.pone.0153539

by V.A. Traag

(Translated by -)

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 that the diffusion of donations is 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.



A mutual information approach to calculating nonlinearity

Stat Volume 4, Issue 1, pages 291–303, 2015 DOI: 10.1002/sta4.96

by Reginald Smith

(Translated by -)

A new method to measure nonlinear dependence between two variables is described using mutual information to analyse the separate linear and nonlinear components of dependence. This technique, which gives an exact value for the proportion of linear dependence, is then compared with another common test for linearity, the Brock, Dechert and Scheinkman test.



Jam avoidance with autonomous systems

Physics and Society,arXiv:1601.07713 [physics.soc-ph]

by Antoine Tordeux, Sylvain Lassarre

(Translated by -)

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.




The chips are down for Moore’s law

NATURE,News Feature,Volume 530,Issue 7589,144-147,11 February 2016

by M. Mitchell Waldrop

(Translated by -)

Next month, the worldwide semiconductor industry will formally acknowledge what has become increasingly obvious to everyone involved: Moore's law, the principle that has powered the information-technology revolution since the 1960s, is nearing its end. A rule of thumb that has come to dominate computing, Moore's law states that the number of transistors on a microprocessor chip will double every two years or so — which has generally meant that the chip's performance will, too. The exponential improvement that the law describes transformed the first crude home computers of the 1970s into the sophisticated machines of the 1980s and 1990s, and from there gave rise to high-speed Internet, smartphones and the wired-up cars, refrigerators and thermostats that are becoming prevalent today.




Evaluating the impact of interdisciplinary research: a multilayer network approach

Physics and Society,arXiv:1601.06075 [physics.soc-ph]

by Elisa Omodei, Manlio De Domenico, Alex Arenas

(Translated by -)

Nowadays, scientific challenges usually require approaches that cross traditional boundaries between academic disciplines, driving many researchers towards interdisciplinarity. Despite its obvious importance, there is a lack of studies on how to quantify the influence of interdisciplinarity on the research impact, posing uncertainty in a proper evaluation for hiring and funding purposes. Here we propose a method based on the analysis of bipartite interconnected multilayer networks of citations and disciplines, to assess scholars, institutions and countries interdisciplinary importance. Using data about physics publications and US patents, we show that our method allows to reveal, using a quantitative approach, that being more interdisciplinary causes -- in the Granger sense -- benefits in scientific productivity and impact. The proposed method could be used by funding agencies, universities and scientific policy decision makers for hiring and funding purposes, and to complement existing methods to rank universities and countries.




The ecological and evolutionary energetics of hunter-gatherer residential mobility

Physics and Society,arXiv:1602.00631 [physics.soc-ph]

by Marcus J. Hamilton, Jose Lobo, Eric Rupley, Hyejin Youn, Geoffrey B. West

(Translated by -)

Residential mobility is deeply entangled with all aspects of hunter-gatherer life ways, and is therefore an issue of central importance in hunter-gatherer studies. Hunter-gatherers vary widely in annual rates of residential mobility, and understanding the sources of this variation has long been of interest to anthropologists and archaeologists. Since mobility is, to a large extent, driven by the need for a continuous supply of food, a natural framework for addressing this question is provided by the metabolic theory of ecology. This provides a powerful framework for formulating formal testable hypotheses concerning evolutionary and ecological constraints on the scale and variation of hunter-gatherer residential mobility. We evaluate these predictions using extant data and show strong support for the hypotheses. We show that the overall scale of hunter-gatherer residential mobility is predicted by average human body size, and the limited capacity of mobile hunter-gatherers to store energy internally. We then show that the majority of variation in residential mobility observed across cultures is predicted by energy availability in local ecosystems. Our results demonstrate that large-scale evolutionary and ecological processes, common to all plants and animals, constrain hunter-gatherers in predictable ways as they move through territories to effectively exploit resources over the course of a year. Moreover, our results extend the scope of the metabolic theory of ecology by showing how it successfully predicts variation in the behavioral ecology of populations within a species.




Describing People as Particles Isn’t Always a Bad Idea



(Translated by -)

Infomercialist and pop psychologist Barbara De Angelis puts it this way: “Love is a force more formidable than any other.” Whether you agree with her or not, De Angelis is doing something we do all the time—she is using the language of physics to describe social phenomena.

“I was irresistibly attracted to him”; “You can’t force me”; “We recognize the force of public opinion”; “I’m repelled by these policies.” We can’t measure any of these “social forces” in the way that we can measure gravity or magnetic force. But not only has physics-based thinking entered our language, it is also at the heart of many of our most important models of social behavior, from economics to psychology. The question is, do we want it there?



Bendy bugs inspire roboticists

Science 12 Feb 2016:Vol. 351, Issue 6274, pp. 647 DOI: 10.1126/science.351.6274.647

by Elizabeth Pennisi

(Translated by -)

Insects, whether they creep or fly, live in a world of hard knocks. Who has not stepped on a cockroach, then raised her shoe to watch the creature get up and scoot under a door? Bees and wasps, for their part, face a never-ending obstacle course of leaves, stems, and petals—bumblebees crash their wings into obstacles as often as once a second. Now, researchers are learning how these creatures bend but don't break. The results do more than explain why cockroaches are so hard to kill. By mimicking the combination of rigid and flexible parts that gives insect exoskeletons and wings their resilience, biomechanicists are making robots tougher. It's quite the contrast from the way engineers have designed most of their machines, but may lead to better robots for search and rescue.



Social Norms of Cooperation in Small-Scale Societies

PLoS Comput Biol 12(1): e1004709

by Fernando P. Santos, Francisco C. Santos, Jorge M. Pacheco

(Translated by -)

Indirect reciprocity, besides providing a convenient framework to address the evolution of moral systems, offers a simple and plausible explanation for the prevalence of cooperation among unrelated individuals. By helping someone, an individual may increase her/his reputation, which may change the pre-disposition of others to help her/him in the future. This, however, depends on what is reckoned as a good or a bad action, i.e., on the adopted social norm responsible for raising or damaging a reputation. In particular, it remains an open question which social norms are able to foster cooperation in small-scale societies, while enduring the wide plethora of stochastic affects inherent to finite populations. Here we address this problem by studying the stochastic dynamics of cooperation under distinct social norms, showing that the leading norms capable of promoting cooperation depend on the community size. However, only a single norm systematically leads to the highest cooperative standards in small communities. That simple norm dictates that only whoever cooperates with good individuals, and defects against bad ones, deserves a good reputation, a pattern that proves robust to errors, mutations and variations in the intensity of selection.



Triumph for gravitational wave hunt

Science 12 Feb 2016:Vol. 351, Issue 6274, pp. 645-646 DOI: 10.1126/science.351.6274.645

by Adrian Cho

(Translated by -)

More than a billion years ago, two black holes—the gravitational ghosts of gigantic stars—spiraled together and collided in space. Ripples in spacetime swept through the universe. Five months ago, they washed past Earth, and physicists detected gravitational waves for the first time. The long-awaited discovery—announced this week—marks a triumph for the Laser Interferometer Gravitational-Wave Observatory (LIGO), a pair of huge instruments in Washington state and Louisiana. It also promises to give researchers a whole new set of eyes on the universe. Until now, astronomers have probed it mainly through electromagnetic radiation such as light. Now, gravitational waves will enable them to detect astrophysical objects that they can't see. And physicists will be able to study realms of extreme gravity that until now only theorists could explore. Other gravitational-wave detections may come soon, both from LIGO and from VIRGO, a freshly upgraded Italian detector scheduled to be switched on later this year.



The happiness paradox: your friends are happier than you

Social and Information Networks,arXiv:1602.02665 [cs.SI]

by Johan Bollen, Bruno Gonçalves, Ingrid van de Leemput, Guangchen Ruan

(Translated by -)

Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of $39,110$ Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users cluster assortatively by level of happiness. Our results indicate that the topology of online social networks and the distribution of happiness in some populations can cause widespread psycho-social effects that affect the well-being of billions of individuals.




Extreme robustness of scaling in sample space reducing processes explains Zipf's law in diffusion on directed networks

Physics and Society,arXiv:1602.05530 [physics.soc-ph]

by Bernat Corominas-Murtra, Rudolf Hanel, Stefan Thurner

(Translated by -)

It has been shown recently that a specific class of path-dependent stochastic processes, which reduce their sample space as they unfold, lead to exact scaling laws in frequency and rank distributions. Such Sample Space Reducing processes (SSRP) offer an alternative new mechanism to understand the emergence of scaling in countless processes. The corresponding power law exponents were shown to be related to noise levels in the process. Here we show that the emergence of scaling is not limited to the simplest SSRPs, but holds for a huge domain of stochastic processes that are characterized by non-uniform prior distributions. We demonstrate mathematically that in the absence of noise the scaling exponents converge to $-1$ (Zipf's law) for almost all prior distributions. As a consequence it becomes possible to fully understand targeted diffusion on weighted directed networks and its associated scaling laws law in node visit distributions. The presence of cycles can be properly interpreted as playing the same role as noise in SSRPs and, accordingly, determine the scaling exponents. The result that Zipf's law emerges as a generic feature of diffusion on networks, regardless of its details, and that the exponent of visiting times is related to the amount of cycles in a network could be relevant for a series of applications in traffic-, transport- and supply chain management.




Zika Virus Community Response

by Yaneer Bar-Yam,New England Complex Systems Institute and Rebecca Menapace, Brandeis University.

(Translated by -)

Here we propose a set of community-level strategies for reducing mosquito reproduction, reducing exposure to the virus, and constraining its geographical spread. The benefits of collective effects lead to the importance of strategies in which multiple individuals perform actions which mutually reinforce each other. The rapid two to four week generation time of the primary mosquito species carrying the virus, Aedes aegypti, means that reducing its reproduction rate may confine it to smaller areas, halting its spread and subsequently enabling more targeted efforts to eliminate the virus in those areas.




Four billion people facing severe water scarcity

Science Advances 12 Feb 2016:Vol. 2, no. 2, e1500323 DOI: 10.1126/sciadv.1500323

by Mesfin M. Mekonnen* and Arjen Y. Hoekstra

(Translated by -)

Freshwater scarcity is increasingly perceived as a global systemic risk. Previous global water scarcity assessments, measuring water scarcity annually, have underestimated experienced water scarcity by failing to capture the seasonal fluctuations in water consumption and availability. We assess blue water scarcity globally at a high spatial resolution on a monthly basis. We find that two-thirds of the global population (4.0 billion people) live under conditions of severe water scarcity at least 1 month of the year. Nearly half of those people live in India and China. Half a billion people in the world face severe water scarcity all year round. Putting caps to water consumption by river basin, increasing water-use efficiencies, and better sharing of the limited freshwater resources will be key in reducing the threat posed by water scarcity on biodiversity and human welfare.



Human Atlas: A Tool for Mapping Social Networks

Social and Information Networks ,arXiv:1602.02426 [cs.SI]

by Martin Saveski, Eric Chu, Soroush Vosoughi, Deb Roy

(Translated by -)

Most social network analyses focus on online social networks. While these networks encode important aspects of our lives they fail to capture many real-world connections. Most of these connections are, in fact, public and known to the members of the community. Mapping them is a task very suitable for crowdsourcing: it is easily broken down in many simple and independent subtasks. Due to the nature of social networks -- presence of highly connected nodes and tightly knit groups -- if we allow users to map their immediate connections and the connections between them, we will need few participants to map most connections within a community. To this end, we built the Human Atlas, a web-based tool for mapping social networks. To test it, we partially mapped the social network of the MIT Media Lab. We ran a user study and invited members of the community to use the tool. In 4.6 man-hours, 22 participants mapped 984 connections within the lab, demonstrating the potential of the tool.




Beyond Ebola

Science 19 Feb 2016:Vol. 351, Issue 6275, pp. 815-816 DOI: 10.1126/science.aad8521

by Janet Currie, Bryan Grenfell, Jeremy Farrar

(Translated by -)

On 14 January 2016, Liberia was declared Ebola-free. A new case was identified shortly after the announcement, but it is nevertheless clear that the West African epidemic has moved on to a more hopeful phase. What lessons can be drawn from the Ebola crisis to help the international community to prepare for and respond to the next global epidemic? This question is particularly pertinent given the recent declaration of the Zika virus as a public health emergency.



What sparked the Cambrian explosion?

NATURE,News Feature,Volume 530,Issue 7590, 268–270 (18 February 2016)

by Douglas Fox

(Translated by -)

An evolutionary burst 540 million years ago filled the seas with an astonishing diversity of animals. The trigger behind that revolution is finally coming into focus.

A series of dark, craggy pinnacles rises 80 metres above the grassy plains of Namibia. The peaks call to mind something ancient — the burial mounds of past civilizations or the tips of vast pyramids buried by the ages.



Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children

Science 19 Feb 2016:Vol. 351, Issue 6275, DOI: 10.1126/science.aad3311


(Translated by -)

Malnutrition in children is a persistent challenge that is not always remedied by improvements in nutrition. This is because a characteristic community of gut microbes seems to mediate some of the pathology. Human gut microbes can be transplanted effectively into germ-free mice to recapitulate their associated phenotypes. Using this model, Blanton et al. found that the microbiota of healthy children relieved the harmful effects on growth caused by the microbiota of malnourished children. In infant mammals, chronic undernutrition results in growth hormone resistance and stunting. In mice, Schwarzer et al. showed that strains of Lactobacillus plantarum in the gut microbiota sustained growth hormone activity via signaling pathways in the liver, thus overcoming growth hormone resistance. Together these studies reveal that specific beneficial microbes could potentially be exploited to resolve undernutrition syndromes.



The scope and limits of simulation in automated reasoning

Artificial Intelligence,Volume 233, April 2016, Pages 60–72

by Ernest Davis, Gary Marcus

(Translated by -)

In scientific computing and in realistic graphic animation, simulation – that is, step-by-step calculation of the complete trajectory of a physical system – is one of the most common and important modes of calculation. In this article, we address the scope and limits of the use of simulation, with respect to AI tasks that involve high-level physical reasoning. We argue that, in many cases, simulation can play at most a limited role. Simulation is most effective when the task is prediction, when complete information is available, when a reasonably high quality theory is available, and when the range of scales involved, both temporal and spatial, is not extreme. When these conditions do not hold, simulation is less effective or entirely inappropriate. We discuss twelve features of physical reasoning problems that pose challenges for simulation-based reasoning. We briefly survey alternative techniques for physical reasoning that do not rely on simulation.



Tensegrity, Dynamic Networks, and Complex Systems Biology: Emergence in Structural and Information Networks Within Living Cells

Complex Systems Science in Biomedicine,Part of the series Topics in Biomedical Engineering International Book Series pp 283-310

by Sui Huang, Cornel Sultan, Donald E. Ingber

(Translated by -)

The genomic revolution has led to the systematic characterization of all the genes of the genome and the proteins they encode. But we still do not fully understand how many cell behaviors are controlled, because many important biological properties of cells emerge at the whole-system level from the collective action of thousands of molecular components, which is orchestrated through specific regulatory interactions. In this chapter we present two distinct approaches based on the concept of molecular networks to understand two fundamental system properties of living cells: their ability to maintain their shape and mechanical stability, and their ability to express stable, discrete cell phenotypes and switch between them. We first describe how structural networks built using the principles of tensegrity architecture and computational models that incorporate these features can predict many of the complex mechanical behaviors that are exhibited by living mammalian cells. We then discuss how genome-wide biochemical signaling networks produce “attractor” states that may represent the stable cell phenotypes, such as growth, differentiation, and apoptosis, and which explain how cells can make discrete cell fate decisions in the presence of multiple conflicting signals. These network-based concepts help to bridge the apparent gap between emergent system features characteristic of living cells and the underlying molecular processes.



Step by Step to Stability and Peace in Syria

New England Complex Systems Institute,210 Broadway Suite 101 Cambridge MA 02139, USA,(Dated February 9, 2016)

by Raphael Parens, Yaneer Bar-Yam

(Translated by -)

The revolution and Civil War in Syria has led to substantial death and suffering, a massive refugee crisis, and growth of ISIS extremism and its terror attacks globally. Conflict between disparate groups is ongoing. Here we propose that interventions should be pursued to stop specific local conflicts, creating safe zones, that can be expanded gradually and serve as examples for achieving a comprehensive solution for safety, peace and stable local governance in Syria.




The likely determines the unlikely

Physics and Society,arXiv:1602.05272 [physics.soc-ph]

by Xiaoyong Yan, Petter Minnhagen, Henrik Jeldtoft Jensen

(Translated by -)

We point out that the functional form describing the frequency of sizes of events in complex systems (e.g. earthquakes, forest fires, bursts of neuronal activity) can be obtained from maximal likelihood inference, which, remarkably, only involve a few available observed measures such as number of events, total event size and extremes. Most importantly, the method is able to predict with high accuracy the frequency of the rare extreme events. To be able to predict the few, often big impact events, from the frequent small events is of course of great general importance. For a data set of wind speed we are able to predict the frequency of gales with good precision. We analyse several examples ranging from the shortest length of a recruit to the number of Chinese characters which occur only once in a text.




The International Postal Network and Other Global Flows As Proxies for National Wellbeing

Computers and Society,arXiv:1601.06028 [cs.CY]

by Desislava Hristova, Alex Rutherford, Jose Anson, Miguel Luengo-Oroz, Cecilia Mascolo

(Translated by -)

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national wellbeing by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude with a multiplex community analysis of the global flow networks, showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources into global multiplex networks can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.



Universal resilience patterns in complex networks

Nature 530, 307–312 (18 February 2016) doi:10.1038/nature16948

by Jianxi Gao,Baruch Barzel& Albert-László Barabási

(Translated by -)

Resilience, a system’s ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems1. Despite widespread consequences for human health2, the economy3 and the environment4, events leading to loss of resilience—from cascading failures in technological systems5 to mass extinctions in ecological networks6—are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components7, and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system’s resilience. The proposed analytical framework allows us systematically to separate the roles of the system’s dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes.



Complexity theory and financial regulation

Science 19 Feb 2016:Vol. 351, Issue 6275, pp. 818-819 DOI: 10.1126/science.aad0299


(Translated by -)

Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.



Networks of plants: how to measure similarity in vegetable species

Populations and Evolution ,arXiv:1602.05887 [q-bio.PE]

by Gianna Vivaldo, Elisa Masi, Camilla Pandolfi, Stefano Mancuso, Guido Caldarelli

(Translated by -)

Despite the common misconception of nearly static organisms, plants do interact continuously with the environment and with each other. It is fair to assume that during their evolution they developed particular features to overcome problems and to exploit possibilities from environment. In this paper we introduce various quantitative measures based on recent advancements in complex network theory that allow to measure the effective similarities of various species. By using this approach on the similarity in fruit-typology ecological traits we obtain a clear plant classification in a way similar to traditional taxonomic classification. This result is not trivial, since a similar analysis done on the basis of diaspore morphological properties do not provide any clear parameter to classify plants species. Complex network theory can then be used in order to determine which feature amongst many can be used to distinguish scope and possibly evolution of plants. Future uses of this approach range from functional classification to quantitative determination of plant communities in nature.




The Mobile Territorial Lab: a multilayered and dynamic view on parents’ daily lives

EPJ Data Science20165:3 DOI: 10.1140/epjds/s13688-016-0064-6© Centellegher et al. 2016

by Simone Centellegher, Marco De Nadai, Michele Caraviello, Chiara Leonardi, Michele Vescovi, Yusi Ramadian, Nuria Oliver, Fabio Pianesi, Alex Pentland, Fabrizio Antonelli and Bruno Lepri

(Translated by -)

The exploration of people’s everyday life has long been of interest to social scientists. Recent years have witnessed a growing interest in analyzing human behavioral data generated by technology (e.g. mobile phones). To date, a few large-scale studies have been designed to measure human behaviors and interactions using multiple sources of data. A common characteristic of these studies is the population under investigation: students having similar daily routines and needs. This choice constraints the range of behaviors, of places and the generalization of the results. In order to widen this line of studies, we focus on a different target group: parents with young children aged 0 through 10 years. Children influence multiple aspects of their parents’ lives, from the satisfaction of basic human needs and the fulfillment of social roles to their financial status and sleep quality.

In this paper, we describe the Mobile Territorial Lab (MTL) project, a longitudinal living lab which has been sensing by means of technology (mobile phones) the lives of more than 100 parents in different areas of the Trentino region in Northern Italy. We present the preliminary results after two years of experimentation of, to the best of our knowledge, the most complete picture of parents’ daily lives. Through the collection and analysis of the collected data, we created a multi-layered view of the participants’ lives, tracking social interactions, mobility routines, spending patterns, and personality characteristics.

Overall, our results prove the relevance of living lab approaches to measure human behaviors and interactions, which can pave the way to new studies exploiting a richer number of behavioral indicators. Moreover, we believe that the proposed methodology and the collected data could be very valuable for researchers from different disciplines such as social psychology, sociology, computer science, economy, etc., which are interested in understanding human behaviour.




Measuring the Complexity of Continuous Distributions

Entropy 2016, 18(3), 72; doi:10.3390/e18030072

by Guillermo Santamaría-Bonfil, Nelson Fernández, and Carlos Gershenson

(Translated by -)

We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon’s information, the novel continuous complexity measures describe how a system’s predictability changes in terms of the probability distribution parameters. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.




Modern Milgram experiment sheds light on power of authority

Nature,Volume 530,Issue 7591,News

by Alison Abbott

(Translated by -)

More than 50 years after a controversial psychologist shocked the world with studies that revealed people’s willingness to harm others on order, a team of cognitive scientists has carried out an updated version of the iconic ‘Milgram experiments’. Their findings may offer some explanation for Stanley Milgram's uncomfortable revelations: when following commands, they say, people genuinely feel less responsibility for their actions — whether they are told to do something evil or benign.




Evolution in the Anthropocene

Science 26 Feb 2016:Vol. 351, Issue 6276, pp. 922-923 DOI: 10.1126/science.aad6756

by François Sarrazin, Jane Lecomte

(Translated by -)

Most current conservation strategies focus on the immediate social, cultural, and economic values of ecological diversity, functions, and services (1). For example, the Intergovernmental Platform on Biodiversity and Ecosystem Services (2) mostly addresses the utilitarian management of biodiversity from local to global scales. However, besides urgent diagnosis and actions (3, 4), processes that occur over evolutionary time scales are equally important for biodiversity conservation. Strategizing for conservation of nature at such long time scales will help to preserve the function—and associated services—of the natural world, as well as providing opportunities for it to evolve. This approach will foster a long-term, sustainable interaction that promotes both the persistence of nature and the wellbeing of humans.



Global Patterns of Human Synchronization

Physics and Society ,arXiv:1602.06219 [physics.soc-ph]

by Alfredo J. Morales, Vaibhav Vavilala, Rosa M. Benito, Yaneer Bar-Yam

(Translated by -)

Social media are transforming global communication and coordination and provide unprecedented opportunities for studying socio-technical domains. Here we study global dynamical patterns of communication on Twitter across many scales. Underlying the observed patterns is both the diurnal rotation of the earth, day and night, and the synchrony required for contingency of actions between individuals. We find that urban areas show a cyclic contraction and expansion that resembles heartbeats linked to social rather than natural cycles. Different urban areas have characteristic signatures of daily collective activities. We show that the differences detected are consistent with a new emergent global synchrony that couples behavior in distant regions across the world. Although local synchrony is the major force that shapes the collective behavior in cities, a larger-scale synchronization is beginning to occur.