Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behaviour in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organisation can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system towards criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.
Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.
From seconds to months: multi-scale dynamics of mobile telephone calls Jari Saramaki, Esteban Moro
A fundamental goal of population genetics is to understand why levels of genetic diversity vary among species and populations. Under the assumptions of the neutral model of molecular evolution, the amount of variation present in a population should be directly proportional to the size of the population. However, this prediction does not tally with real-life observations: levels of genetic diversity are found to be substantially more uniform, even among species with widely differing population sizes, than expected. Because natural selection—which removes genetically linked neutral variation—is more efficient in larger populations, selection on novel mutations offers a potential reconciliation of this paradox. In this work, we align and jointly analyze whole genome genetic variation data from a wide variety of species. Using this dataset and population genetic models of the impact of selection on neutral variation, we test the prediction that selection will disproportionally remove neutral variation in species with large population sizes. We show that genomic signature of natural selection is pervasive across most species, and that the amount of linked neutral variation removed by selection correlates with proxies for population size. We propose that pervasive natural selection constrains neutral diversity and provides an explanation for why neutral diversity does not scale as expected with population size.
We show that all existing deterministic microscopic traffic models with identical drivers (including both two-phase and three-phase models) can be understood as special cases from a master model by expansion around well-defined ground states. This allows two traffic models to be compared in a well-defined way. The three-phase models are characterized by the vanishing of leading orders of expansion within a certain density range, and as an example the popular intelligent driver models (IDM) is shown to be equivalent to a generalized optimal velocity (OV) model. We also explore the diverse solutions of the generalized OV model that can be important both for understanding human driving behaviors and algorithms for autonomous driverless vehicles.
Classification and Unification of the Microscopic Deterministic Traffic Models with Identical Drivers Bo Yang, Christopher Monterola
Condensed-matter physics is becoming increasingly oriented towards materials science and engineering. That's the conclusion reached by two physicists, Michael Shulman and Marc Warner, after analysing the statistics of abstracts for the main annual (March) meeting of the American Physical Society since 2007 (http://arxiv.org/abs/1502.03103 ). They enumerated key words used in abstracts to identify trends over the past eight years, and say that during this time the words that are increasing in popularity are often ones associated with specific types of material system, such as 'layer', 'thin', 'organic', 'oxide' and indeed 'material'. In contrast, words or (word fragments) with generally declining popularity include 'superconduct' and 'flux' (as well as, oddly, 'science').
We show that the log-periodic power law singularity model (LPPLS), a mathematical embodiment of positive feedbacks between agents and of their hierarchical dynamical organization, has a significant predictive power in financial markets. We find that LPPLS-based strategies significantly outperform the randomized ones and that they are robust with respect to a large selection of assets and time periods. The dynamics of prices thus markedly deviate from randomness in certain pockets of predictability that can be associated with bubble market regimes. Our hybrid approach, marrying finance with the trading strategies, and critical phenomena with LPPLS, demonstrates that targeting information related to phase transitions enables the forecast of financial bubbles and crashes punctuating the dynamics of prices.
Using trading strategies to detect phase transitions in financial markets Z. Forró, R. Woodard, and D. Sornette Phys. Rev. E 91, 042803
The human body plays host to a vast and diverse microbial community. From metabolic regulation to immunologic maintenance, the microbiome performs functions vital to our health. Innovations in the Microbiome distils the most critical insights from the recent explosion in microbiome research. As science continues to unravel the host–microbiome relationship, clues are emerging for the treatment of disease.
Human mobility in a city represents a fascinating complex system that combines social interactions, daily constraints and random explorations. New collections of data that capture human mobility not only help us to understand their underlying patterns but also to design intelligent systems. Bringing us the opportunity to reduce traffic and to develop other applications that make cities more adaptable to human needs. In this paper, we propose an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed by the collectivity as a whole. Using big data sets recently released during the Telecom Italia Big Data Challenge, we show that our algorithm allows us to reduce the overall traffic in a smart city thanks to synergetic effects, with the participation of individuals in the system, playing a crucial role.
Personalized routing for multitudes in smart cities De Domenico M, Lima A, González MC, Arenas A EPJ Data Science 2015, 4 :1
The overwhelming success of online social networks, the key actors in the cosmos of the Web 2.0, has reshaped human interactions on a worldwide scale. To understand the fundamental mechanisms which determine the fate of online social networks at the system level, we recently introduced a general ecological theory of the digital world. In this paper, we discuss the impact of heterogeneity in the network intrinsic fitness and present how the general theory can be applied to understand the competition between an international network, like Facebook, and local services. To this end, we construct a 1:1000 scale model of the digital world enclosing the 80 countries with most Internet users. We find that above a certain threshold the level of global connectivity can lead to the extinction of local networks. In addition, we reveal the complex role the tendency of individuals to engage in more active networks plays for the probability of local networks to become extinct and provide insights into the conditions under which they can prevail.
A 1:1000 scale model of the digital world: Global connectivity can lead to the extinction of local networks Kaj-Kolja Kleineberg, Marian Boguna
In the last few years, electronic media brought a revolution in the traceability of social phenomena. As particles in a bubble chamber, social trajectories leave digital trails that can be analyzed to gain a deeper understanding of collective life. To make sense of these traces a renewed collaboration between social and natural scientists is needed. In this paper, we claim that current research strategies based on micro-macro models are unfit to unfold the complexity of collective existence and that the priority should instead be the development of new formal tools to exploit the richness of digital data.
Memes were originally framed in relationship to genes. In The Selfish Gene, Dawkins claimed that humans are “survival machines” for our genes, the replicating molecules that emerged from the primordial soup and that, through mutation and natural selection, evolved to generate beings that were more effective as carriers and propagators of genes. Still, Dawkins explained, genes could not account for all of human behavior, particularly the evolution of cultures. So he identified a second replicator, a “unit of cultural transmission” that he believed was “leaping from brain to brain” through imitation. He named these units “memes,” an adaption of the Greek word mimene, “to imitate.” Dawkins’ memes include everything from ideas, songs, and religious ideals to pottery fads. Like genes, memes mutate and evolve, competing for a limited resource—namely, our attention. Memes are, in Dawkins’ view, viruses of the mind—infectious. The successful ones grow exponentially, like a super flu. While memes are sometimes malignant (hellfire and faith, for atheist Dawkins), sometimes benign (catchy songs), and sometimes terrible for our genes (abstinence), memes do not have conscious motives. But still, he claims, memes parasitize us and drive us.
The Intergovernmental Panel on Climate Change (IPCC) is becoming irrelevant to climate policy. By seeking consensus and avoiding controversy, the organization is suffering from the streetlight effect — focusing ever more attention on a well-lit pool of the brightest climate science. But the insights that matter are out in the darkness, far from the places that the natural sciences alone can illuminate.
Climate change: Embed the social sciences in climate policy David Victor
In modern states, mobilization policy has been used to awaken people to new ideas such as national identity, industrial capitalism, and civic society. However, it has long been debated whether mobilization in new countries or in countries under reconstruction creates an integrated identity or results in fragmentation of various ethnic groups. Although the idea that identity is not immutable but malleable is now widely accepted in political science, sociology, and other social sciences, the degree to which identity can be reconstructed once it has been mobilized remains unclear. This study employs an agent-based model to address questions regarding the relationship between governments' mobilization and the integration of identity in countries. The analysis suggests that more rapid mobilization by governments stabilizes a greater ethnic cleavage. This result is found to be robust by changing parameters and by modifying the specifications of the model. In addition, the analysis presents two other implications. The first is that a spiraling fragmentation of identity might occur if governments fail to accommodate people. The second is that in an age of advanced communication, governments need more assimilative power than before in order to secure integration. The analysis suggests that future research about identity formation in countries should consider the rigidity as well as the flexibility of identity.
Detailed knowledge of the energy needs at relatively high spatial and temporal resolution is crucial for the electricity infrastructure planning of a region. However, such information is typically limited by the scarcity of data on human activities, in particular in developing countries where electrification of rural areas is sought. The analysis of society-wide mobile phone records has recently proven to offer unprecedented insights into the spatio-temporal distribution of people, but this information has never been used to support electrification planning strategies anywhere and for rural areas in developing countries in particular. The aim of this project is the assessment of the contribution of mobile phone data for the development of bottom-up energy demand models, in order to enhance energy planning studies and existing electrification practices. More specifically, this work introduces a framework that combines mobile phone data analysis, socioeconomic and geo-referenced data analysis, and state-of-the-art energy infrastructure engineering techniques to assess the techno-economic feasibility of different centralized and decentralized electrification options for rural areas in a developing country. Specific electrification options considered include extensions of the existing medium voltage (MV) grid, diesel engine-based community-level Microgrids, and individual household-level solar photovoltaic (PV) systems. The framework and relevant methodology are demonstrated throughout the paper using the case of Senegal and the mobile phone data made available for the 'D4D-Senegal' innovation challenge. The results are extremely encouraging and highlight the potential of mobile phone data to support more efficient and economically attractive electrification plans.
Using Mobile Phone Data for Electricity Infrastructure Planning Eduardo Alejandro Martinez-Cesena, Pierluigi Mancarella, Mamadou Ndiaye, Markus Schläpfer
Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.
Much of our commerce and traveling depend on the efficient operation of large scale networks. Some of those, such as electric power grids, transportation systems, communication networks, and others, must maintain their efficiency even after several failures, or malicious attacks. We outline a procedure that modifies any given network to enhance its robustness, defined as the size of its largest connected component after a succession of attacks, whilst keeping a high efficiency, described in terms of the shortest paths among nodes. We also show that this generated set of networks is very similar to networks optimized for robustness in several aspects such as high assortativity and the presence of an onion-like structure.
Generating Robust and Efficient Networks Under Targeted Attacks Vitor H. P. Louzada, Fabio Daolio, Hans J. Herrmann, Marco Tomassini
Online social networks represent a popular and highly diverse class of social media systems. Despite this variety, each of these systems undergoes a general process of online social network assembly, which represents the complicated and heterogeneous changes that transform newly born systems into mature platforms. However, little is known about this process. For example, how much of a network's assembly is driven by simple growth? How does a network's structure change as it matures? How does network structure vary with adoption rates and user heterogeneity, and do these properties play different roles at different points in the assembly? We investigate these and other questions using a unique dataset of online connections among the roughly one million users at the first 100 colleges admitted to Facebook, captured just 20 months after its launch. We first show that different vintages and adoption rates across this population of networks reveal temporal dynamics of the assembly process, and that assembly is only loosely related to network growth. We then exploit natural experiments embedded in this dataset and complementary data obtained via Internet archaeology to show that different subnetworks, e.g., among students and among alumni, matured at different rates toward similar end states. These results shed new light on the processes and patterns of online social network assembly, and may facilitate more effective design for online social systems.
Assembling thefacebook: Using heterogeneity to understand online social network assembly Abigail Z. Jacobs, Samuel F. Way, Johan Ugander, Aaron Clauset
Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that aim to draw policies from the activities of humans in space. Despite recent availability of large scale data sets related to human mobility such as GPS traces, mobile phone data, etc., it is still true that such data sets represent a subsample of the population of interest, and then might give an incomplete picture of the entire population in question. Notwithstanding the abundant usage of such inherently limited data sets, the impact of sampling biases on mobility patterns is unclear -- we do not have methods available to reliably infer mobility information from a limited data set. Here, we investigate the effects of sampling using a data set of millions of taxi movements in New York City. On the one hand, we show that mobility patterns are highly stable once an appropriate simple rescaling is applied to the data, implying negligible loss of information due to subsampling over long time scales. On the other hand, contrasting an appropriate null model on the weighted network of vehicle flows reveals distinctive features which need to be accounted for. Accordingly, we formulate a "supersampling" methodology which allows us to reliably extrapolate mobility data from a reduced sample and propose a number of network-based metrics to reliably assess its quality (and that of other human mobility models). Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is needed.
Supersampling and network reconstruction of urban mobility Oleguer Sagarra, Michael Szell, Paolo Santi, Albert Diaz-Guilera, Carlo Ratti
While expected applications of Modular Robotic Systems (MRS) span various workspaces, scales, and structures, practical implementations of such systems lag behind their potentials in performing real-world tasks. Challenges of enhancing MRS capabilities not only are limited to designing reliable, responsive, and robust hardware, but also include developing software and algorithms that can effectively fulfill tasks through performing fundamental functions like shape-formation, locomotion, manipulation, etc. Thus, MRS solution methods must be able to resolve problems arising from the tightly-coupled kinematics of interconnected modules and their inherent limitations in resources, communication, connection strength, etc. in performing such functions through domain-specific operations including Self-reconfiguration, Flow, Gait, Self-assembly, Self-disassembly, Self-adaptation, Grasping, Collective actuation, and Enveloping. Despite the large number of developed solution methods, there is no inclusive and updated study in the literature dedicated to classifying, analyzing, and comparing their specifications and capabilities in a systematic manner. This paper aims to fill in this gap through reviewing 64 solution methods and algorithms according to their application in each operation and by investigating their capabilities in (1) modeling and simplifying MRS problems through Abstraction methods, (2) solving MRS problems through Solution and Control methods, and (3) coordinating actions of modules through Synchronization methods. Challenging issues of each solution approach along with their advantages and weaknesses are also analyzed and open problems and improvement outlooks are mentioned. Overall, this paper aims to investigate the research areas in MRS algorithms that have been evolved so far and to explore promising research directions for the future.
Modular robotic systems: Methods and algorithms for abstraction, planning, control, and synchronization Hossein Ahmadzadeh, Ellips Masehian
Statistical studies of languages have focused on the rank-frequency distribution of words. Instead, we introduce here a measure of how word ranks change in time and call this distribution rank diversity. We calculate this diversity for books published in six European languages since 1800, and find that it follows a universal lognormal distribution. Based on the mean and standard deviation associated with the lognormal distribution, we define three different word regimes of languages: “heads” consist of words which almost do not change their rank in time, “bodies” are words of general use, while “tails” are comprised by context-specific words and vary their rank considerably in time. The heads and bodies reflect the size of language cores identified by linguists for basic communication. We propose a Gaussian random walk model which reproduces the rank variation of words in time and thus the diversity. Rank diversity of words can be understood as the result of random variations in rank, where the size of the variation depends on the rank itself. We find that the core size is similar for all languages studied.
In 2000 mobile phone users accounted for 12% of the world’s population. By the end of 2014, this figure had reached 96%, i.e., 6.8 billion people. The number of mobile phones in developed countries amounts to 128% of the inhabitants and 90% in developing countries. There has been an explosive increase in the number of ways we use them through their built-in sensors, capable of recording location, acceleration, acquiring images and videos, interacting with other devices and, obviously, connecting to the internet. Probably the most unexpected and disruptive effect of the emergence of always-connected mankind is data, the digital breadcrumbs that we leave behind us. Because, thanks to these data, human activities on a global scale can be observed and, therefore, measured, quantified and, ultimately, predicted. Only fortune-tellers and consultants can predict the future without data, says the network scientist Laszlo Barabasi. But if we get enough detailed information on some phenomenon, even an unexpected or bizarre one – a black swan – then we can predict it.
Remember domino theory? One country going Communist was supposed to topple the next, and then the next, and the next. The metaphor drove much of United States foreign policy in the middle of the 20th century. But it had the wrong name. From a physical point of view, it should have been called the “sandpile theory.” Real-world political phase transitions tend to happen not in neat sequences, but in sudden coordinated fits, like the Arab Spring, or the collapse of the Eastern Bloc. These reflect quiet periods punctuated by crises—like a sandpile. You can add grains of sand to the top of a sandpile for a while, to no apparent effect. Then, all at once, an avalanche sweeps sand down from the top in an irregular pattern, possibly setting off little sub-avalanches as it goes.
Human alterations of Earth's environments are pervasive. Visible changes include the built environment, conversion of forests and grasslands to agriculture, algal blooms, smog, and the siltation of dams and estuaries. Less obvious transformations include increases in ozone, carbon dioxide (CO2), and methane (CH4) in the atmosphere, and ocean acidification. Motivated by the pervasiveness of these alterations, Crutzen and Stoermer argued in 2000 that we live in the “Anthropocene,” a time in which humans have replaced nature as the dominant environmental force on Earth (1). Many of these wide-ranging changes first emerged during the past 200 years and accelerated rapidly in the 20th century (2). Yet, a focus on the most recent changes risks overlooking pervasive human transformations of Earth's surface for thousands of years, with profound effects on the atmosphere, climate, and biodiversity.
Defining the epoch we live in William F. Ruddiman, Erle C. Ellis, Jed O. Kaplan, Dorian Q. Fuller
The Constitution of the United States empowers the Congress to pass copyright laws to promote knowledge creation in the society and more specifically scientific knowledge. Many interesting economic studies have been conducted on copyright law, but very little research has been done to study the impact of the law on knowledge creation. In this paper we develop and analyze an agent-based model to investigate the impact of copyright on the creation and discovery of new knowledge. The model suggests that, for the most part, the extension of the copyright term hinders scholars in producing new knowledge. Furthermore, extending the copyright term tends to harm everyone, including scholars who have access to all published articles in the research field. However, we also identify situations where extending copyright term promotes rather than hinders knowledge creation. Additionally, scholars that publish copyrighted materials tend to out-perform those who do not creating a potential tension between individual incentives and the public good.
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