Many biological systems execute tasks by dividing them into finer sub-tasks first. This is seen for example in the advanced division of labor of social insects like ants, bees or termites. One of the unsolved mysteries in biology is how a blind process of Darwinian selection could have led to such highly complex forms of sociality. To answer this question, we used simulated teams of robots and artificially evolved them to achieve maximum performance in a foraging task. We find that, as in social insects, this favored controllers that caused the robots to display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group. Remarkably, such a division of labor could be achieved even if the robots were not told beforehand how the global task of retrieving items back to their base could best be divided into smaller subtasks. This is the first time that a self-organized division of labor mechanism could be evolved entirely de-novo. In addition, these findings shed significant new light on the question of how natural systems managed to evolve complex sociality and division of labor.
Renewables will soon dominate energy production in our electric power system. And yet, how to integrate renewable energy into the grid and the market is still a subject of major debate. Decentral Smart Grid Control (DSGC) was recently proposed as a robust and decentralized approach to balance supply and demand and to guarantee a grid operation that is both economically and dynamically feasible. Here, we analyze the impact of network topology by assessing the stability of essential network motifs using both linear stability analysis and basin volume for delay systems. Our results indicate that if frequency measurements are averaged over sufficiently large time intervals, DSGC enhances the stability of extended power grid systems. We further investigate whether DSGC supports centralized and/or decentralized power production and fi?nd it to be applicable to both. However, our results on cycle-like systems suggest that DSGC favors systems with decentralized production. Here, lower line capacities and lower averaging times are required compared to those with centralized production.
Taming Instabilities in Power Grid Networks by Decentralized Control Benjamin Schäfer, Carsten Grabow, Sabine Auer, Jürgen Kurths, Dirk Witthaut, Marc Timme
#sna by Andrie de Vries In a previous post, I used page rank and community structure to create a plot of CRAN. This plot used vibrant colours to allow us to see some of the underlying structure of CRAN. However, much of this structure was still obfuscated by the amount of detail. Concretely, a large number of dots (packages) made it difficult to easily see the community structure.
In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.
Microbial communities associated with animals and plants (i.e., microbiomes) are implicated in the day-to-day functioning of their hosts. However, we do not yet know how these host-microbiome associations evolve. In this paper, we develop a computational framework for modelling the evolution of microbiomes. The models we use are neutral, and assume that microbes have no effect on the reproductive success of the hosts. Therefore, the patterns of microbiome diversity that we obtain in our simulations require a minimal set of assumptions relating to how microbes are acquired and how they are assembled in the environment. Despite the simplicity of our models, they help us understand the patterns seen in empirical data, and they allow us to build more complex hypotheses of host-microbe dynamics.
We evaluate complex time series of online user communication in Twitter social network. We construct spike trains of each user participating any interaction with any other users in the network. Retweet a message, mention a user in a message, and reply to a message are types of interaction observed in Twitter. By applying the local variation originally established for neuron spike trains, we quantify the temporal behavior of active and passive but popular users separately. We show that the local variation of active users give bursts independent of the activation frequency. On the other hand, the local variation of popular users present irregular random (Poisson) patterns and the resultant temporal patterns are highly influenced by the frequency of the attention, e.g. bursts for less popular users, but randomly distributed temporarily uncorrelated spikes for most popular users. To understand the coincidence in the temporal patterns of two distinct interactions, we propose linear correlations of the local variation of the filtered spikes based on concerned interactions. We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of retweet, a conventional measure of user popularity.
Temporal Pattern of Communication Spike Trains in Twitter: How Often, Who Interacts with Whom? Ceyda Sanlı, Renaud Lambiotte
In this article, we analyze the interrelationships among such notions as entropy, information, complexity, order and chaos and show using the theory of categories how to generalize the second law of thermodynamics as a law of increasing generalized entropy or a general law of complification. This law could be applied to any system with morphisms, including all of our universe and its subsystems. We discuss how such a general law and other laws of nature drive the evolution of the universe, including physicochemical and biological evolutions. In addition, we determine eliminating selection in physicochemical evolution as an extremely simplified prototype of natural selection. Laws of nature do not allow complexity and entropy to reach maximal values by generating structures. One could consider them as a kind of “breeder” of such selection.
Entropy, Information and Complexity or Which Aims the Arrow of Time? George E. Mikhailovsky and Alexander P. Levich
The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines - the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century.
25 Years of Self-Organized Criticality: Numerical Detection Methods R.T. James McAteer, Markus J. Aschwanden, Michaila Dimitropoulou, Manolis K. Georgoulis, Gunnar Pruessner, Laura Morales, Jack Ireland, Valentyna Abramenko
Are you fascinated by interdisciplinary work? — Are you into data analysis and model building? If yes, you might be interested in this position. In the last decades Econophysics emerged as a new, interdisciplinary field. Our group has longstanding expertise. We develop models for various issues in the economy, particularly in the financial markets. We apply the same standards as in traditional physics and base our models as much as possible on the empirical information.
Ecological networks are the description of interacting populations of different biological species sharing the same geographical area and time. These networks are characterized by temporal changes and constitute the skeleton of biodiversity and natural resources. Are you interested in understanding how ecological networks respond to environmental changes? Are you interested in engineering quantitative tools to assess how ecological networks are changing and will change? How can we design sustainable strategies to increase the likelihood of persistence of ecological networks subject to biotic and abiotic variations? Our work is quantitatively and computationally inclined sustained by field ecological data
Liver cancer is one of the most difficult cancers to detect, but synthetic biologist Tal Danino had a left-field thought: What if we could create a probiotic, edible bacteria that was "programmed" to find liver tumors? His insight exploits something we're just beginning to understand about bacteria: their power of quorum sensing, or doing something together once they reach critical mass. Danino, a TED Fellow, explains how quorum sensing works — and how clever bacteria working together could someday change cancer treatment.
Can artificial communities of agents develop language with scaling relations close to the Zipf law? As a preliminary answer to this question, we propose an Automata Networks model of the formation of a vocabulary on a population of individuals, under two in principle opposite strategies: the alignment and the least effort principle. Within the previous account to the emergence of linguistic conventions (specially, the Naming Game), we focus on modeling speaker and hearer efforts as actions over their vocabularies and we study the impact of these actions on the formation of a shared language. The numerical simulations are essentially based on an energy function, that measures the amount of local agreement between the vocabularies. The results suggests that on one dimensional lattices the best strategy to the formation of shared languages is the one that minimizes the efforts of speakers on communicative tasks.
Automata networks model for alignment and least effort on vocabulary formation Javier Vera, Felipe Urbina, Eric Goles
In a cascading power transmission outage, component outages propagate non-locally; after one component outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion do not accurately represent the propagation of cascades in power systems. However, cascading power outages do follow patterns, some of which are useful in understanding and reducing blackout risk. This paper describes a method by which the data from many cascading failure simulations can be transformed into a graph-based model of influences that provides actionable information about the many ways that cascades propagate in a particular system. The resulting "influence graph" model is Markovian, since component outage probabilities depend only on the outages that occurred in the prior generation. To validate the model we compare the distribution of cascade sizes resulting from n-2 contingencies in a 2896 branch test case to cascade sizes in the influence graph. The two distributions are remarkably similar. In addition, we derive an equation with which one can quickly identify modifications to the proposed system that will substantially reduce cascade propagation. With this equation one can quickly identify critical components that can be improved to substantially reduce the risk of large cascading blackouts.
Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology Paul D. H. Hines, Ian Dobson, Pooya Rezaei
Pinpointing the nodes whose removal most effectively disrupts a network has become a lot easier with the development of an efficient algorithm. Potential applications might include cybersecurity and disease control.
Network science: Destruction perfected • István A. Kovács & Albert-László Barabási
The science of ecology is about relationships—among organisms and habitats, on all scales—and how they provide information that helps us better understand our world. In the past 100 years, the field has moved from observations to experiments to forecasting. Next week, the Ecological Society of America (ESA), the world's largest ecological society, celebrates its centennial in Baltimore, an opportunity to reflect on the field's past and future. The gathering of international scientists, policy-makers, and students will not only explore the knowledge in hand, but consider what else is needed to chart a course over the next century in which humanity sustains and even improves the relationships that underpin life on Earth.
*2nd Third Infinity conference, 14th - 16th of October 2015, Goettingen, Germany - Call for Abstracts - deadline 14th August 2015*
A conference organized by the PhD students of the International Max Planck Research School for Physics of Biological and Complex Systems. This conference aims to bring together young researchers and leading scientists working on complex systems from the three fundamental perspectives: theory, experiments and simulations.
Here, it is proposed that thinking on a different level is required to understand, what really triggers or removes this barrier. Just counting “dimensions” or “variables” is insufficient. The true intrinsic curse we are facing is the curse of instability. In fact, we argue below that instabilities (a) cause an increase in dimensionality, (b) substantially raise the analytical difficulty, and (c) are a strong indicator for multiscale dynamical complexity. Of course, it turns out that (a)–(c) are intimately related. Although we shall primarily illustrate the concepts with examples arising in mathematics and closely related disciplines, it will be shown that the abstract concept occurs, independently, across disciplines. In fact, we shall see that the curse of instability has already implicitly triggered the emergence of entirely new scientific disciplines. Furthermore, it may lead to formulate more concrete guiding principles to address the complexity challenges of the 21st century.
Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timingsof node and link activations. In this paper, we set to explore the latter by using greedy walks as probes of temporal network structure. Given a temporal network (a sequence of contacts), greedy walks proceed from node to node by always following the first available contact. Because of this, their structure is particularly sensitive to temporal-topological patterns involving repeated contacts between sets of nodes. This becomes evident in their small coverage per step as compared to a temporal reference model -- in empirical temporal networks, greedy walks often get stuck within small sets of nodes because of correlated contact patterns. While this may also happen in static networks that have pronounced community structure, the use of the temporal reference model takes the underlying static network structure out of the equation and indicates that there is a purely temporal reason for the observations. Further analysis of the structure of greedy walks indicates that burst trains, sequences of repeated contacts between node pairs, are the dominant factor. However, there are larger patterns too, as shown with non-backtracking greedy walks. We proceed further to study the entropy rates of greedy walks, and show that the sequences of visited nodes are more structured and predictable in original data as compared to temporally uncorrelated references. Taken together, these results indicate a richness of correlated temporal-topological patterns in temporal networks.
Exploring Temporal Networks with Greedy Walks Jari Saramaki, Petter Holme
Cascades in multiplex financial networks with debts of different seniority
The seniority of debt, which determines the order in which a bankrupt institution repays its debts, is an important and sometimes contentious feature of financial crises, yet its impact on systemwide stability is not well understood. We capture seniority of debt in a multiplex network, a graph of nodes connected by multiple types of edges. Here an edge between banks denotes a debt contract of a certain level of seniority. Next we study cascading default. There exist multiple kinds of bankruptcy, indexed by the highest level of seniority at which a bank cannot repay all its debts. Self-interested banks would prefer that all their loans be made at the most senior level. However, mixing debts of different seniority levels makes the system more stable in that it shrinks the set of network densities for which bankruptcies spread widely. We compute the optimal ratio of senior to junior debts, which we call the optimal seniority ratio, for two uncorrelated Erdős-Rényi networks. If institutions erode their buffer against insolvency, then this optimal seniority ratio rises; in other words, if default thresholds fall, then more loans should be senior. We generalize the analytical results to arbitrarily many levels of seniority and to heavy-tailed degree distributions.
As a part of the consolidation of the National Laboratory of Complexity, the Center for Complexity Science of the National Autonomous University of Mexico is seeking outstanding candidates for five one year postdoctoral positions beginning in August, 2015. Research plans from all areas related to complex systems are encouraged.
Please send CV and research plan to cgg [at] unam.mx before June 10th.
The ELSI Origins Network (EON) announces the availability of ten post-doctoral research fellowships for research related to the Origins of Life to be funded between 2015-2018. Successful candidates will split their time between ELSI in Tokyo and another institute of the candidate’s choice, anywhere in the world. The fellowship will pay a salary for two years, which covers the time spent in both institutions, as well as a generous research budget. The positions will start on or before 1st April 2016. EON is an interdisciplinary international network which seeks to foster dialogue and collaboration within the Origins of Life community to articulate and answer fundamental questions about the nature and the reasons for the existence of life on Earth. Its goal is to bring together leading-edge research in all areas of the physical, mathematical, computational, and life sciences that bears on the emergence of life. EON is a part of the Earth-Life Science Institute (ELSI), which is chartered as a Japanese World Premier International Research Center, to study the origin of Earth-like planets and the origin of life as inter-related phenomena.
Finding the right mate is no cakewalk -- but is it even mathematically likely? In a charming talk, mathematician Hannah Fry shows patterns in how we look for love, and gives her top three tips (verified by math!) for finding that special someone.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.