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.
The aim of this conference is to bring together researchers from around the world working on discrete modeling of complex systems and analysis of their dynamics. The objective of this conference is to provide a forum for exchange of ideas, presentation of results of current research and to discuss potential future directions and developments in the field of discrete modeling of complex systems and analysis of their dynamics from methodological and phenomenological point of view. The conference will cover both theoretical and applied research. It will focus on discrete modeling methodologies and their applications to analysis across different scales of dynamics of complex systems. The 2015 Summer Solstice Conference topics include, but are not limited to, the following: • Challenges, benefits and theory of modeling and simulation of complex systems using cellular automata, lattice gas cellular automata, multi-agent based models, complex networks • Discrete models in biology and medicine • Discrete models in economy and social sciences • Discrete models of man made complex systems from nanotechnology to information networks • Tools of analysis of dynamics and multiscale phenomena of discrete models of complex systems There will be sessions of contributed presentations. The organizers reserve the right to assign contributed presentation as oral or poster. The Post Conference Proceedings are planned and all conference presenters will be invited to submit a paper for publication in the Proceedings. All submissions will be peer-reviewed.
The study of network theory is a highly interdisciplinary field, which has emerged as a major topic of interest in various disciplines ranging from physics and mathematics, to biology and sociology. This book promotes the diverse nature of the study of complex networks by balancing the needs of students from very different backgrounds. It references the most commonly used concepts in network theory, provides examples of their applications in solving practical problems, and clear indications on how to analyse their results.
In the first part of the book, students and researchers will discover the quantitative and analytical tools necessary to work with complex networks, including the most basic concepts in network and graph theory, linear and matrix algebra, as well as the physical concepts most frequently used for studying networks. They will also find instruction on some key skills such as how to proof analytic results and how to manipulate empirical network data. The bulk of the text is focused on instructing readers on the most useful tools for modern practitioners of network theory. These include degree distributions, random networks, network fragments, centrality measures, clusters and communities, communicability, and local and global properties of networks. The combination of theory, example and method that are presented in this text, should ready the student to conduct their own analysis of networks with confidence and allow teachers to select appropriate examples and problems to teach this subject in the classroom.
The aim of this book is to explain in simple language what we know and what we do not know about information and entropy — two of the most frequently discussed topics in recent literature — and whether they are relevant to life and the entire universe. Entropy is commonly interpreted as a measure of disorder. This interpretation has caused a great amount of "disorder" in the literature. One of the aims of this book is to put some "order" in this "disorder". The book explains with minimum amount of mathematics what information theory is and how it is related to thermodynamic entropy. Then it critically examines the application of these concepts to the question of "What is life?" and whether or not they can be applied to the entire universe.
The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.
Analytical Computation of the Epidemic Threshold on Temporal Networks Eugenio Valdano, Luca Ferreri, Chiara Poletto, and Vittoria Colizza Phys. Rev. X 5, 021005 (2015)
Termites, like many social insects, build nests of complex architecture. These constructions have been proposed to optimize different structural features. Here we describe the nest network of the termite Nasutitermes ephratae, which is among the largest nest-network reported for termites and show that it optimizes diverse parameters defining the network architecture. The network structure avoids multiple crossing of galleries and minimizes the overlap of foraging territories. Thus, these termites are able to minimize the number of galleries they build, while maximizing the foraging area available at the nest mounds. We present a simple computer algorithm that reproduces the basics characteristics of this termite nest network, showing that simple rules can produce complex architectural designs efficiently.
Emergence, self-organization and network efficiency in gigantic termite-nest-networks build using simple rules Diego Griffon, Carmen Andara, Klaus Jaffe
Multicellular eukaryotes can perform functions that exceed the possibilities of an individual cell. These functions emerge through interactions between differentiated cells that are precisely arranged in space. Bacteria also form multicellular collectives that consist of differentiated but genetically identical cells. How does the functionality of these collectives depend on the spatial arrangement of the differentiated bacteria? In a previous issue of PLOS Biology, van Gestel and colleagues reported an elegant example of how the spatial arrangement of differentiated cells gives rise to collective behavior in Bacillus subtilus colonies, further demonstrating the similarity of bacterial collectives to higher multicellular organisms.
Urbanization promotes economy, mobility, access and availability of resources, but on the other hand, generates higher levels of pollution, violence, crime, and mental distress. The health consequences of the agglomeration of people living close together are not fully understood. Particularly, it remains unclear how variations in the population size across cities impact the health of the population. We analyze the deviations from linearity of the scaling of several health-related quantities, such as the incidence and mortality of diseases, external causes of death, wellbeing, and health-care availability, in respect to the population size of cities in Brazil, Sweden and the USA. We find that deaths by non-communicable diseases tend to be relatively less common in larger cities, whereas the per-capita incidence of infectious diseases is relatively larger for increasing population size. Healthier life style and availability of medical support are disproportionally higher in larger cities. The results are connected with the optimization of human and physical resources, and with the non-linear effects of social networks in larger populations. An urban advantage in terms of health is not evident and using rates as indicators to compare cities with different population sizes may be insufficient.
The non-linear health consequences of living in larger cities Luis E. C. Rocha, Anna E. Thorson, Renaud Lambiotte
How do groups of animals, including humans, make decisions that affect the entire group? Evidence collected from schooling animals suggests that the process is somewhat democratic, with nearest neighbors and the majority shaping overall collective behavior. In animals with hierarchical social structures such as primates or wolves, however, such democracy may be complicated by dominance. Strandburg-Peshkin et al. monitored all the individuals within a baboon troop continuously over the course of their daily activities. Even within this highly socially structured species, movement decisions emerged via a shared process. Thus, democracy may be an inherent trait of collective behavior.
Shared decision-making drives collective movement in wild baboons Ariana Strandburg-Peshkin, Damien R. Farine, Iain D. Couzin, Margaret C. Crofoot
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.
CCS'15 Satellite Meeting: Information Processing in Complex Systems (IPCS'15)
Abstracts due: June 20 Decision of admission: June 25 Satellite meeting: October 1
All systems in nature have one thing in common: they process information. Information is registered in the state of a system and its elements, implicitly and invisibly. As elements interact, information is transferred. Indeed, bits of information about the state of one element will travel – imperfectly – to the state of the other element, forming its new state. This storage and transfer of information, possibly between levels of a multi level system, is imperfect due to randomness or noise. From this viewpoint, a system can be formalized as a collection of bits that is organized according to its rules of dynamics and its topology of interactions. Mapping out exactly how these bits of information percolate through the system could reveal new fundamental insights in how the parts orchestrate to produce the properties of the system. A theory of information processing would be capable of defining a set of universal properties of dynamical multi level complex systems, which describe and compare the dynamics of diverse complex systems ranging from social interaction to brain networks, from financial markets to biomedicine. Each possible combination of rules of dynamics and topology of interactions, with disparate semantics, would reduce to a single language of information processing.
Noise, as we usually think of it, is background sound that interferes with our ability to hear more interesting sounds. In general terms, though, it is anything that interferes with the reception of signals of any sort. It includes extraneous energy in the environment, degradation of signals in transit, and spontaneous random activity in receivers and signalers. Whatever the cause, the consequence of noise is error by receivers, and these errors are the key to understanding how noise shapes the evolution of communication.
Noise Matters breaks new ground in the scientific understanding of how communication evolves in the presence of noise. Combining insights of signal detection theory with evidence from decades of his own original research, Haven Wiley explains the profound effects of noise on the evolution of communication. The coevolution of signalers and receivers does not result in ideal, noise-free communication, Wiley finds. Instead, signalers and receivers evolve to a joint equilibrium in which communication is effective but never error-free. Noise is inescapable in the evolution of communication.
Wiley’s comprehensive approach considers communication on many different levels of biological organization, from cells to individual organisms, including humans. Social interactions, such as honesty, mate choice, and cooperation, are reassessed in the light of noisy communication. The final sections demonstrate that noise even affects how we think about human language, science, subjectivity, and freedom. Noise Matters thus contributes to understanding the behavior of animals, including ourselves.
The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem finding phase transitions of different kinds. Distinct phases are associated to different arrangements of the connections; but the need of drastic topological changes does not determine the presence, nor the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.
Phase transitions in Pareto optimal complex networks Luís F Seoane, Ricard Solé
A substantial volume of research has been devoted to studies of community structure in networks, but communities are not the only possible form of large-scale network structure. Here we describe a broad extension of community structure that encompasses traditional communities but includes a wide range of generalized structural patterns as well. We describe a principled method for detecting this generalized structure in empirical network data and demonstrate with real-world examples how it can be used to learn new things about the shape and meaning of networks.
Generalized communities in networks M. E. J. Newman, Tiago P. Peixoto
In social networks, individuals constantly drop ties and replace them by new ones in a highly unpredictable fashion. This highly dynamical nature of social ties has important implications for processes such as the spread of information or of epidemics. Several studies have demonstrated the influence of a number of factors on the intricate microscopic process of tie replacement, but the macroscopic long-term effects of such changes remain largely unexplored. Here we investigate whether, despite the inherent randomness at the microscopic level, there are macroscopic statistical regularities in the long-term evolution of social networks. In particular, we analyze the email network of a large organization with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical laws, characterized by exponentially decaying log-variations of the weight of social ties and of individuals' social strength. At the same time, we find that individuals have social signatures and communication strategies that are remarkably stable over the scale of several years.
Long-term evolution of techno-social networks: Statistical regularities, predictability and stability of social behaviors Antonia Godoy-Lorite, Roger Guimera, Marta Sales-Pardo
Cities and their transportation systems become increasingly complex and multimodal as they grow, and it is natural to wonder if it is possible to quantitatively characterize our difficulty to navigate in them and whether such navigation exceeds our cognitive limits. A transition between different searching strategies for navigating in metropolitan maps has been observed for large, complex metropolitan networks. This evidence suggests the existence of another limit associated to the cognitive overload and caused by large amounts of information to process. In this light, we analyzed the world's 15 largest metropolitan networks and estimated the information limit for determining a trip in a transportation system to be on the order of 8 bits. Similar to the "Dunbar number," which represents a limit to the size of an individual's friendship circle, our cognitive limit suggests that maps should not consist of more than about 250 connections points to be easily readable. We also show that including connections with other transportation modes dramatically increases the information needed to navigate in multilayer transportation networks: in large cities such as New York, Paris, and Tokyo, more than 80% of trips are above the 8-bit limit. Multimodal transportation systems in large cities have thus already exceeded human cognitive limits and consequently the traditional view of navigation in cities has to be revised substantially.
Information measures and cognitive limits in multilayer navigation Riccardo Gallotti, Mason A. Porter, Marc Barthelemy
Since December 2006, more than a thousand cities in México have suffered the effects of the war between several drug cartels, amongst themselves, as well as with Mexican armed forces. Sources are not in agreement about the number of casualties of this war, with reports varying from 30 to 100 thousand dead; the economic and social ravages are impossible to quantify. In this work we analyze the official report of casualties in terms of the location and the date of occurrence of the homicides. We show how the violence, as reflected by the number of casualties, has increased over time and spread across the country. Next, based on the correlations between cities in the changes of the monthly number of casualties attributed to organized crime, we construct a narco-war network where nodes are the affected cities and links represent correlations between them. We find that close geographical distance between violent cities does not imply a strong correlation amongst them. We observe that the dynamics of the conflict has evolved in short-term periods where a small core of violent cities determines the main theatre of the war at each stage. This kind of analysis may also help to describe the emergence and propagation of gang-related violence waves.
The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure.
The anatomy of urban social networks and its implications in the searchability problem • C. Herrera-Yagüe, C. M. Schneider, T. Couronné, Z. Smoreda, R. M. Benito, P. J. Zufiria & M. C. González
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