In this study, the effect of assortativity on the robustness and efficiency of interconnected networks was investigated. This involved constructing a network that possessed the desired degree of assortativity. Additionally, an interconnected network was constructed wherein the assortativity between component networks possessed the desired value. With respect to single networks, the results indicated that a decrease in assortativity provided low hop length, high information diffusion efficiency, and distribution of communication load on edges. The study also revealed that excessive assortativity led to poor network performance. In the study, the assortativity between networks was defined and the following results were demonstrated: assortative connections between networks lowered the average hop length and enhanced information diffusion efficiency, whereas disassortative connections between networks distributed the communication loads of internetwork links and enhanced robustness. Furthermore, it is necessary to carefully adjust assortativity based on the node degree distribution of networks. Finally, the application of the results to the design of robust and efficient information networks was discussed.
Robustness and efficiency in interconnected networks with changes in network assortativity Masaya Murakami, Shu Ishikura, Daichi Kominami, Tetsuya Shimokawa and Masayuki Murata Applied Network Science 2017 2:6 DOI: 10.1007/s41109-017-0025-4
We leveraged exogenous variation in weather patterns across geographies to identify social contagion in exercise behaviours across a global social network. We estimated these contagion effects by combining daily global weather data, which creates exogenous variation in running among friends, with data on the network ties and daily exercise patterns of ∼1.1M individuals who ran over 350M km in a global social network over 5 years. Here we show that exercise is socially contagious and that its contagiousness varies with the relative activity of and gender relationships between friends. Less active runners influence more active runners, but not the reverse. Both men and women influence men, while only women influence other women. While the Embeddedness and Structural Diversity theories of social contagion explain the influence effects we observe, the Complex Contagion theory does not. These results suggest interventions that account for social contagion will spread behaviour change more effectively.
Exercise contagion in a global social network Sinan Aral & Christos Nicolaides Nature Communications 8, Article number: 14753 (2017) doi:10.1038/ncomms14753
Studies of collective intelligence in animal groups typically overlook potential improvement through learning. Although knowledge accumulation is recognized as a major advantage of group living within the framework of Cumulative Cultural Evolution (CCE), the interplay between CCE and collective intelligence has remained unexplored. Here, we use homing pigeons to investigate whether the repeated removal and replacement of individuals in experimental groups (a key method in testing for CCE) alters the groups’ solution efficiency over successive generations. Homing performance improves continuously over generations, and later-generation groups eventually outperform both solo individuals and fixed-membership groups. Homing routes are more similar in consecutive generations within the same chains than between chains, indicating cross-generational knowledge transfer. Our findings thus show that collective intelligence in animal groups can accumulate progressive modifications over time. Furthermore, our results satisfy the main criteria for CCE and suggest potential mechanisms for CCE that do not rely on complex cognition.
Cumulative culture can emerge from collective intelligence in animal groups Takao Sasaki & Dora Biro Nature Communications 8, Article number: 15049 (2017) doi:10.1038/ncomms15049
An ecosystem consists of communities of interacting species and the physical environment on which they depend. Although it is well accepted that Earth consists of many different ecosystems, human societies much less readily recognize that Earth itself is an ecosystem, dependent on interacting species and consisting of finite resources. As the human population has grown and increasingly dominated available resources, “ecosystem Earth” has begun to show increasing signs of stress. Loss of biodiversity, environmental degradation, and conflict over resources among the dominant species are typical signs that a biological system is nearing a state change, which could range from collapse of the dominant species, to development of alternative biological communities, to collapse of the entire system. In this special issue, we identify our impacts on ecosystem Earth, seek to understand the barriers to change, and explore potential solutions. Decades of research on ecosystem dynamics can help to guide our thinking about a sustainable future. Bottom-up reductions in human population growth and resource consumption, changes to how we think about our place in the system, and a willingness to prioritize persistence of the other species within our biological community will lead to a healthier planetary ecosystem. It is essential that humanity begins to better appreciate our role as just one part of a large and interdependent biological community. Our ability to dominate the planet's resources makes us directly responsible for determining the future of the ecosystem on which we, and all other forms of life, depend.
Ecosystem Earth Sacha Vignieri, Julia Fahrenkamp-Uppenbrink
In vertebrates, skin colour patterns emerge from nonlinear dynamical microscopic systems of cell interactions. Here we show that in ocellated lizards a quasi-hexagonal lattice of skin scales, rather than individual chromatophore cells, establishes a green and black labyrinthine pattern of skin colour. We analysed time series of lizard scale colour dynamics over four years of their development and demonstrate that this pattern is produced by a cellular automaton (a grid of elements whose states are iterated according to a set of rules based on the states of neighbouring elements) that dynamically computes the colour states of individual mesoscopic skin scales to produce the corresponding macroscopic colour pattern. Using numerical simulations and mathematical derivation, we identify how a discrete von Neumann cellular automaton emerges from a continuous Turing reaction–diffusion system. Skin thickness variation generated by three-dimensional morphogenesis of skin scales causes the underlying reaction–diffusion dynamics to separate into microscopic and mesoscopic spatial scales, the latter generating a cellular automaton. Our study indicates that cellular automata are not merely abstract computational systems, but can directly correspond to processes generated by biological evolution.
A living mesoscopic cellular automaton made of skin scales
Liana Manukyan, Sophie A. Montandon, Anamarija Fofonjka, Stanislav Smirnov & Michel C. Milinkovitch
Nature 544, 173–179 (13 April 2017) doi:10.1038/nature22031
Willingness to lay down one’s life for a group of non-kin, well documented historically and ethnographically, represents an evolutionary puzzle. Building on research in social psychology, we develop a mathematical model showing how conditioning cooperation on previous shared experience can allow individually costly pro-group behavior to evolve. The model generates a series of predictions that we then test empirically in a range of special sample populations (including military veterans, college fraternity/sorority members, football fans, martial arts practitioners, and twins). Our empirical results show that sharing painful experiences produces “identity fusion” – a visceral sense of oneness – which in turn can motivate self-sacrifice, including willingness to fight and die for the group. Practically, our account of how shared dysphoric experiences produce identity fusion helps us better understand such pressing social issues as suicide terrorism, holy wars, sectarian violence, gang-related violence, and other forms of intergroup conflict.
The evolution of extreme cooperation via shared dysphoric experiences Harvey Whitehouse, Jonathan Jong, Michael D. Buhrmester, Ángel Gómez, Brock Bastian, Christopher M. Kavanagh, Martha Newson, Miriam Matthews, Jonathan A. Lanman, Ryan McKay & Sergey Gavrilets
Random Item Generation tasks (RIG) are commonly used to assess high cognitive abilities such as inhibition or sustained attention. They also draw upon our approximate sense of complexity. A detrimental effect of ageing on pseudo-random productions has been demonstrated for some tasks, but little is as yet known about the developmental curve of cognitive complexity over the lifespan. We investigate the complexity trajectory across the lifespan of human responses to five common RIG tasks, using a large sample (n = 3429). Our main finding is that the developmental curve of the estimated algorithmic complexity of responses is similar to what may be expected of a measure of higher cognitive abilities, with a performance peak around 25 and a decline starting around 60, suggesting that RIG tasks yield good estimates of such cognitive abilities. Our study illustrates that very short strings of, i.e., 10 items, are sufficient to have their complexity reliably estimated and to allow the documentation of an age-dependent decline in the approximate sense of complexity.
For decades, archaeologists thought democratic republics such as classical Athens and medieval Venice were a purely European phenomenon. Conventional wisdom held that in premodern, non-Western societies, despots simply extracted labor and wealth from their subjects. But archaeologists have identified several societies in pre-Columbian Mesoamerica that upend that model. They argue that societies such as Tlaxcallan in the central Mexican highlands and Tres Zapotes along the Mexican gulf coast were organized collectively, meaning that rulers shared power and commoners had a say in the government that presided over their lives. These societies were not necessarily full democracies in which citizens cast votes, but they were radically different from the autocratic, inherited rule found—or assumed—in most ancient societies. Archaeologists now say that these collective societies left telltale traces in their material culture and urban planning, such as repetitive architecture, an emphasis on public space over palaces, reliance on local production over exotic trade goods, and a narrowing of wealth gaps between elites and commoners.
Unearthing democracy's roots Lizzie Wade
Science 17 Mar 2017: Vol. 355, Issue 6330, pp. 1114-1118 DOI: 10.1126/science.355.6330.1114
What is noise? Common sense tells us it is a disturbance, an invasion of our perceptual space, a nuisance. But this is only part of a more complex story that the sciences and modern technologies might help us unravel. ‘Noise’ has a contextual meaning, but it also points at something ‘in nature’ (or in society)—and something that might also have a function and/or beneficial effects. In this article I show that what is categorized as ‘noise’ is there not necessarily to be removed or to be dispensed with, but to be used and taken advantage of.
Innovation is to organizations what evolution is to organisms: it is how organisations adapt to changes in the environment and improve. Governments, institutions and firms that innovate are more likely to prosper and stand the test of time; those that fail to do so fall behind their competitors and succumb to market and environmental change. Yet despite steady advances in our understanding of evolution, what drives innovation remains elusive. On the one hand, organizations invest heavily in systematic strategies to drive innovation. On the other, historical analysis and individual experience suggest that serendipity plays a significant role in the discovery process. To unify these two perspectives, we analyzed the mathematics of innovation as a search process for viable designs across a universe of building blocks. We then tested our insights using historical data from language, gastronomy and technology. By measuring the number of makeable designs as we acquire more components, we observed that the relative usefulness of different components is not fixed, but cross each other over time. When these crossovers are unanticipated, they appear to be the result of serendipity. But when we can predict crossovers ahead of time, they offer an opportunity to strategically increase the growth of our product space. Thus we find that the serendipitous and strategic visions of innovation can be viewed as different manifestations of the same thing: the changing importance of component building blocks over time.
Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve. Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours. The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm. Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times of random walks. We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure—graph surgery—affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.
Evolutionary dynamics on any population structure
Benjamin Allen, Gabor Lippner, Yu-Ting Chen, Babak Fotouhi, Naghmeh Momeni, Shing-Tung Yau & Martin A. Nowak
In the analysis of the robustness of multiplex networks, it is commonly assumed that a node is functioning only if its interdependent nodes are simultaneously functioning. According to this model, a multiplex network becomes more and more fragile as the number of layers increases. In this respect, the addition of a new layer of interdependent nodes to a preexisting multiplex network will never improve its robustness. Whereas such a model seems appropriate to understand the effect of interdependencies in the simplest scenario of a network composed of only two layers, it may seem unsuitable to characterize the robustness of real systems formed by multiple network layers. In fact, it seems unrealistic that a real system evolved, through the development of multiple layers of interactions, towards a fragile structure. In this paper, we introduce a model of percolation where the condition that makes a node functional is that the node is functioning in at least two of the layers of the network. The model reduces to the commonly adopted percolation model for multiplex networks when the number of layers equals two. For larger numbers of layers, however, the model describes a scenario where the addition of new layers boosts the robustness of the system by creating redundant interdependencies among layers. We prove this fact thanks to the development of a message-passing theory that is able to characterize the model in both synthetic and real-world multiplex graphs.
Redundant Interdependencies Boost the Robustness of Multiplex Networks Filippo Radicchi and Ginestra Bianconi Phys. Rev. X 7, 011013
Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two ubiquitous growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are standard statistical packages for estimating the structural properties of complex networks, there is no corresponding package when it comes to the estimation of growth mechanisms. This paper introduces the R package PAFit, which implements well-established statistical methods for estimating preferential attachment and node fitness, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure good performance for large-scale networks. In this paper, we first introduce the main functionalities of PAFit using simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks.
PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks Thong Pham, Paul Sheridan, Hidetoshi Shimodaira
Government, where decisions made in a moment can affect millions of people for a lifetime, needs constant reminders of its fallibility. A big part of that has to be a proper respect for the methods of science, as well as for its substantive discoveries. Psychologists assure us that human beings have a strong desire to accept things as true because we want them to be true, not only because they are the best explanation for what we observe. In the hands of policy-makers, that natural tendency can have deadly consequences. Science has developed impressive (though not infallible) techniques for correcting for such biases; our government could stand to do a bit better.
But perhaps consciousness is not uniquely troublesome. Going back to Gottfried Leibniz and Immanuel Kant, philosophers of science have struggled with a lesser known, but equally hard, problem of matter. What is physical matter in and of itself, behind the mathematical structure described by physics? This problem, too, seems to lie beyond the traditional methods of science, because all we can observe is what matter does, not what it is in itself—the “software” of the universe but not its ultimate “hardware.” On the surface, these problems seem entirely separate. But a closer look reveals that they might be deeply connected.
We explain the anomaly of election results between large cities and rural areas in terms of urban scaling in the 1948-2016 US elections and in the 2016 EU referendum of the UK. The scaling curves are all universal and depend on a single parameter only, and one of the parties always shows superlinear scaling and drives the process, while the sublinear exponent of the other party is merely the consequence of probability conservation. Based on the recently developed model of urban scaling, we give a microscopic model of voter behavior in which we replace diversity characterizing humans in creative aspects with social diversity and tolerance. The model can also predict new political developments such as the fragmentation of the left and 'the immigration paradox'.
Universal Scaling Laws in Metro Area Election Results
Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of 20 land use classes across 300 European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.
Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale Adrian Albert, Jasleen Kaur, Marta Gonzalez
Complexity science has spread from its origins in the physical sciences into biological and social sciences (1). Increasingly, the social sciences frame policy problems from the financial system to the food system as complex adaptive systems (CAS) and urge policy-makers to design legal solutions with CAS properties in mind. What is often poorly recognized in these initiatives is that legal systems are also complex adaptive systems (2). Just as it seems unwise to pursue regulatory measures while ignoring known CAS properties of the systems targeted for regulation, so too might failure to appreciate CAS qualities of legal systems yield policies founded upon unrealistic assumptions. Despite a long empirical studies tradition in law, there has been little use of complexity science. With few robust empirical studies of legal systems as CAS, researchers are left to gesture at seemingly evident assertions, with limited scientific support. We outline a research agenda to help fill this knowledge gap and advance practical applications.
Harnessing legal complexity J. B. Ruhl, Daniel Martin Katz, Michael J. Bommarito II
Science 31 Mar 2017: Vol. 355, Issue 6332, pp. 1377-1378 DOI: 10.1126/science.aag3013
There are few sights more spectacular than the swarming of a school of fish or a flock of birds that suddenly gives way to a directional motion. Arguably, our admiration is rooted in the surprise that individual organisms, capable of self-propulsion on their own, organize to move en masse in a coherent fashion. Coherent motion is common in a large class of biological and synthetic materials that are often referred to as active matter. Such materials consist of particles immersed in a fluid that can extract energy from their surroundings (or internal fuel) and convert it into directed motion. Living organisms, biological tissues, rods on a vibrated plate, and self-phoretic colloids are just a few examples (1). Similar to schools of fish and flocks of birds, active matter often exhibits random swarming motion (2–5) that until now was impossible to control or use. On page 1284 of this issue, Wu et al. (6) demonstrate that an active fluid can be manipulated to flow in a particular direction without any external stimuli by confining it in microchannels.
From chaos to order in active fluids Alexander Morozov
Science 24 Mar 2017: Vol. 355, Issue 6331, pp. 1262-1263 DOI: 10.1126/science.aam8998
Recent hydrological modelling1 and Earth observations2, 3 have located and quantified alarming rates of groundwater depletion worldwide. This depletion is primarily due to water withdrawals for irrigation1, 2, 4, but its connection with the main driver of irrigation, global food consumption, has not yet been explored. Here we show that approximately eleven per cent of non-renewable groundwater use for irrigation is embedded in international food trade, of which two-thirds are exported by Pakistan, the USA and India alone. Our quantification of groundwater depletion embedded in the world’s food trade is based on a combination of global, crop-specific estimates of non-renewable groundwater abstraction and international food trade data. A vast majority of the world’s population lives in countries sourcing nearly all their staple crop imports from partners who deplete groundwater to produce these crops, highlighting risks for global food and water security. Some countries, such as the USA, Mexico, Iran and China, are particularly exposed to these risks because they both produce and import food irrigated from rapidly depleting aquifers. Our results could help to improve the sustainability of global food production and groundwater resource management by identifying priority regions and agricultural products at risk as well as the end consumers of these products.
We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes.
Disease Localization in Multilayer Networks Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, and Yamir Moreno Phys. Rev. X 7, 011014
We are witnessing how urban areas are reclaiming road space, before devoted exclusively to cars, for pedestrians. With the increase of pedestrian activity, we need to update our existing transportation forecasting models by focusing more on people walking. The first step of extending the current models is to start with collecting information on pedestrians needed for the trip generation phase. This article discusses opportunities and limitations of tracking pedestrian activity by utilizing information provided by cellular networks. In order to track people, regardless of the underlying wireless media, two qualifications must be met: first, unique and anonymous identification, and second, geospatial visibility through time. While the latter requirement can be achieved with techniques that are similar for different wireless media, how to uniquely identify a pedestrian using a cellular network is domain-specific. We show that tracking of pedestrians using cellular networks can be done not only without their constant active participation, but also without disrupting normal cellular service. However, although this method is technically feasible, one should be very careful when wanting to implement it by keeping in mind a very important thing: how to protect people's privacy.
Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular Networks
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