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Predicting panic is of critical importance in many areas of human and animal behavior, notably in the context of economics. The recent financial crisis is a case in point. Panic may be due to a specific external threat or selfgenerated nervousness. Here we show that the recent economic crisis and earlier large singleday panics were preceded by extended periods of high levels of market mimicry—direct evidence of uncertainty and nervousness, and of the comparatively weak influence of external n
We propose a dynamical model in which a network structure evolves in a selforganized critical (SOC) manner and explain a possible origin of the emergence of fractal and smallworld networks. Our model combines a network growth and its decay by failures of nodes. The decay mechanism reflects the instability of large functional networks against cascading overload failures. It is demonstrated that the dynamical system surely exhibits SOC characteristics, such as powerlaw forms of the avalanche size distribution, the cluster size distribution, and the distribution of the time interval between intermittent avalanches. During the network evolution, fractal networks are spontaneously generated when networks experience critical cascades of failures that lead to a percolation transition. In contrast, networks far from criticality have smallworld structures. We also observe the crossover behavior from fractal to smallworld structure in the network evolution.
Via Bernard Ryefield
Social networks affect every aspect of our lives, from the jobs we get and the technologies we adopt to the partners we choose and the healthiness of our lifestyles. But where do they come from?
Via Keith Hamon
A model of a banking network predicts the balance of high and lowpriority debts that ensures financial stability. Synopsis: http://physics.aps.org/synopsisfor/10.1103/PhysRevE.91.062813 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. Selfinterested 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 ErdősRényi 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 heavytailed degree distributions. Charles D. Brummitt and Teruyoshi Kobayashi Phys. Rev. E 91, 062813 (2015) Published June 24, 2015
Via Complexity Digest, A. J. AlvarezSocorro
Frequencydependent selection and demographic fluctuations play important roles in evolutionary and ecological processes. Under frequencydependent selection, the average fitness of the population may increase or decrease based on interactions between individuals within the population. This should be reflected in fluctuations of the population size even in constant environments. Here, we propose a stochastic model that naturally combines these two evolutionary ingredients by assuming frequencydependent competition between different types in an individualbased model. In contrast to previous game theoretic models, the carrying capacity of the population, and thus the population size, is determined by pairwise competition of individuals mediated by evolutionary games and demographic stochasticity. In the limit of infinite population size, the averaged stochastic dynamics is captured by deterministic competitive Lotka–Volterra equations. In small populations, demographic stochasticity may instead lead to the extinction of the entire population. Because the population size is driven by fitness in evolutionary games, a population of cooperators is less prone to go extinct than a population of defectors, whereas in the usual systems of fixed size the population would thrive regardless of its average payoff.
Via Ashish Umre
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 healthrelated quantities, such as the incidence and mortality of diseases, external causes of death, wellbeing, and healthcare availability, in respect to the population size of cities in Brazil, Sweden and the USA. We find that deaths by noncommunicable diseases tend to be relatively less common in larger cities, whereas the percapita 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 nonlinear 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 nonlinear health consequences of living in larger cities Luis E. C. Rocha, Anna E. Thorson, Renaud Lambiotte http://arxiv.org/abs/1506.02735
Via Complexity Digest
Diffusion of information, behavioural patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media. Here we study analytically and by simulations a general model that incorporates threshold mechanism capturing sensitivity to peer pressure, the effect of `immune' nodes who never adopt, and a perpetual flow of external information. While any constant, nonzero rate of dynamicallyintroduced innovators leads to global spreading, the kinetics by which the asymptotic state is approached show rich behaviour. In particular we find that, as a function of the density of immune nodes, there is a transition from fast to slow spreading governed by entirely different mechanisms. This transition happens below the percolation threshold of fragmentation of the network, and has its origin in the competition between cascading behaviour induced by innovators and blocking of adoption due to immune nodes. This change is accompanied by a percolation transition of the induced clusters. Kinetics of Social Contagion Zhongyuan Ruan, Gerardo Iniguez, Marton Karsai, Janos Kertesz http://arxiv.org/abs/1506.00251
Via Complexity Digest
Critical transitions in multistable systems have been discussed as models for a variety of phenomena ranging from the extinctions of species to socioeconomic changes and climate transitions between iceages and warmages. From bifurcation theory we can expect certain critical transitions to be preceded by a decreased recovery from external perturbations. The consequences of this critical slowing down have been observed as an increase in variance and autocorrelation prior to the transition. However especially in the presence of noise it is not clear, whether these changes in observation variables are statistically relevant such that they could be used as indicators for critical transitions. In this contribution we investigate the predictability of critical transitions in conceptual models. We study the the quadratic integrateandfire model and the van der Pol model, under the influence of external noise. We focus especially on the statistical analysis of the success of predictions and the overall predictability of the system. The performance of different indicator variables turns out to be dependent on the specific model under study and the conditions of accessing it. Furthermore, we study the influence of the magnitude of transitions on the predictive performance.
Via Bernard Ryefield
The emergence and maintenance of cooperative behavior is a fascinating topic in evolutionary biology and social science. The public goods game (PGG) is a paradigm for exploring cooperative behavior. In PGG, the total resulting payoff is divided equally among all participants. This feature still leads to the dominance of defection without substantially magnifying the public good by a multiplying factor. Much effort has been made to explain the evolution of cooperative strategies, including a recent model in which only a portion of the total benefit is shared by all the players through introducing a new strategy named persistent cooperation. A persistent cooperator is a contributor who is willing to pay a second cost to retrieve the remaining portion of the payoff contributed by themselves. In a previous study, this model was analyzed in the framework of wellmixed populations. This paper focuses on discussing the persistent cooperation in latticestructured populations. The evolutionary dynamics of the structured populations consisting of three types of competing players (pure cooperators, defectors and persistent cooperators) are revealed by theoretical analysis and numerical simulations. In particular, the approximate expressions of fixation probabilities for strategies are derived on onedimensional lattices. The phase diagrams of stationary states, the evolution of frequencies and spatial patterns for strategies are illustrated on both onedimensional and square lattices by simulations. Our results are consistent with the general observation that, at least in most situations, a structured population facilitates the evolution of cooperation. Specifically, here we find that the existence of persistent cooperators greatly suppresses the spreading of defectors under more relaxed conditions in structured populations compared to that obtained in wellmixed population.
Via Ashish Umre
We perform a multifractal analysis of the evolution of London's street network from 1786 to 2010. First, we show that a single fractal dimension, commonly associated with the morphological description of cities, does not su ce to capture the dynamics of the system. Instead, for a proper characterization of such a dynamics, the multifractal spectrum needs to be considered. Our analysis reveals that London evolves from an inhomogeneous fractal structure, that can be described in terms of a multifractal, to a homogeneous one, that converges to monofractality. We argue that London's multifractal to monofracal evolution might be a special outcome of the constraint imposed on its growth by a green belt. Through a series of simulations, we show that multifractal objects, constructed through di usion limited aggregation, evolve towards monofractality if their growth is constrained by a nonpermeable boundary. Multifractal to monofractal evolution of the London's street network Roberto Murcio, A. Paolo Masucci, Elsa Arcaute, Michael Batty http://arxiv.org/abs/1505.02760
Via Complexity Digest
Conférence donnée à aux Ateliers du thinktank The Shift Project le 12 mars 2015, par François Roddier, astrophysicien. LA THERMODYNAMIQUE DES TRANSITIONS ÉC...
Via Bernard Ryefield
Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations, which are the outcome of geographical, political and historical constraints. Using percolation theory on the street intersections and on the road network of Britain, we obtain hierarchies at different scales that are independent of administrative arrangements. Natural boundaries, such as islands and National Parks, consistently emerge at the largest/regional scales. Cities are devised through recursive percolations on each of the emerging clusters, but the system does not undergo a phase transition at the distance threshold at which cities can be defined. This specific distance is obtained by computing the fractal dimension of the clusters extracted at each distance threshold. We observe that the fractal dimension presents a maximum over all the different distance thresholds. The clusters obtained at this maximum are in very good correspondence to the morphological definition of cities given by satellite images, and by other methods previously developed by the authors (Arcaute et al. 2015). Hierarchical organisation of Britain through percolation theory Elsa Arcaute, Carlos Molinero, Erez Hatna, Roberto Murcio, Camilo VargasRuiz, Paolo Masucci, Jiaqiu Wang, Michael Batty http://arxiv.org/abs/1504.08318
Via Complexity Digest

This paper describes a new concept of cellular automata (CA). XCA consists of a set of arcs (edges). These arcs correspond to cells in CA. At a definite time, the arcs are connected to a directed graph. With each next time step, the arcs are exchanging their neighbors (adjacent arcs) according to rules that are dependent on the status of the adjacent arcs. With the extended cellular automaton (XCA) an artificial world may be simulated starting with a Big Bang. XCA does not require a grid like CA do. However, it can create one, just as the real universe after the big bang generated its own space, which previously did not exist. Examples with different rules show how manifold the concept of XCA is. Like the game of life simulates birth, survival, and death, this game should simulate a system that starts from a singularity, and evolves to a complex space.
Via Bernard Ryefield
We discuss how understanding the nature of chaotic dynamics allows us to control these systems. A controlled chaotic system can then serve as a versatile pattern generator that can be used for a range of application. Specifically, we will discuss the application of controlled chaos to the design of novel computational paradigms. Thus, we present an illustrative research arc, starting with ideas of control, based on the general understanding of chaos, moving over to applications that influence the course of building better devices.
Via Bernard Ryefield
The detection and characterization of selforganized 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 applicationoriented methods developed in the last 25 years. In the second half of this manuscript spacebased, timebased and spatialtemporal 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 SelfOrganized Criticality: Numerical Detection Methods R.T. James McAteer, Markus J. Aschwanden, Michaila Dimitropoulou, Manolis K. Georgoulis, Gunnar Pruessner, Laura Morales, Jack Ireland, Valentyna Abramenko http://arxiv.org/abs/1506.08142 ;
Via Complexity Digest
This paper presents a stepbystep methodology for Twitter sentiment analysis with application to retail brands. Two approaches are tested to measure variations in the public opinion about particular products and brands. The first, a lexiconbased method, uses a dictionary of words with assigned to them semantic scores to calculate a final polarity of a tweet, and incorporates part of speech tagging. The second, machine learning approach, tackles the problem as a text classification task employing two supervised classifiers  Naive Bayes and Support Vector Machines. We show that combining the lexicon and machine learning approaches by using a lexicon score as a one of the features in Naive Bayes and SVM classifications improves the accuracy of classification by 5%.
Via Ashish Umre
Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and realworld networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network. The Majority Illusion in Social Networks Kristina Lerman, Xiaoran Yan, XinZeng Wu http://arxiv.org/abs/1506.03022
Via Complexity Digest
Living organisms need to maintain energetic homeostasis. For many species, this implies taking actions with delayed consequences. For example, humans may have to decide between foraging for highcalorie but hardtoget, and lowcalorie but easytoget food, under threat of starvation. Homeostatic principles prescribe decisions that maximize the probability of sustaining appropriate energy levels across the entire foraging trajectory. Here, predictions from biological principles contrast with predictions from economic decisionmaking models based on maximizing the utility of the endpoint outcome of a choice. To empirically arbitrate between the predictions of biological and economic models for individual human decisionmaking, we devised a virtual foraging task in which players chose repeatedly between two foraging environments, lost energy by the passage of time, and gained energy probabilistically according to the statistics of the environment they chose. Reaching zero energy was framed as starvation. We used the mathematics of random walks to derive endpoint outcome distributions of the choices. This also furnished equivalent lotteries, presented in a purely economic, casinolike frame, in which starvation corresponded to winning nothing. Bayesian model comparison showed that—in both the foraging and the casino frames—participants’ choices depended jointly on the probability of starvation and the expected endpoint value of the outcome, but could not be explained by economic models based on combinations of statistical moments or on rankdependent utility. This implies that under precisely defined constraints biological principles are better suited to explain human decisionmaking than economic models based on endpoint utility maximization.
Via Ashish Umre
Recently, the dependence group has been proposed to study the robustness of networks with interdependent nodes. A dependence group means that a failed node in the group can lead to the failures of the whole group. Considering the situation of real networks that one failed node may not always break the functionality of a dependence group, we study a cascading failure model that a dependence group fails only when more than a fraction β of nodes of the group fail. We find that the network becomes more robust with the increasing of the parameter β. However, the type of percolation transition is always first order unless the model reduces to the classical network percolation model, which is independent of the degree distribution of the network. Furthermore, we find that a larger dependence group size does not always make the networks more fragile. We also present exact solutions to the size of the giant component and the critical point, which are in agreement with the simulations well.
Via Ashish Umre
The relationship between information and complexity is analyzed using a detailed literature analysis. Complexity is a multifaceted concept, with no single agreed definition. There are numerous approaches to defining and measuring complexity and organization, all involving the idea of information. Conceptions of complexity, order, organization, and “interesting order” are inextricably intertwined with those of information. Shannon's formalism captures information's unpredictable creative contributions to organized complexity; a full understanding of information's relation to structure and order is still lacking. Conceptual investigations of this topic should enrich the theoretical basis of the information science discipline, and create fruitful links with other disciplines that study the concepts of information and complexity. “Waiting for Carnot”: Information and complexity David Bawden and Lyn Robinson Journal of the Association for Information Science and Technology Early View http://dx.doi.org/10.1002/asi.23535
Via Complexity Digest
We examine all possible statistical pictures of violent conflicts over common era history with a focus on dealing with incompleteness and unreliability of data. We apply methods from extreme value theory on logtransformed data to remove compact support, then, owing to the boundedness of maximum casualties, retransform the data and derive expected means. We find the estimated mean likely to be at least three times larger than the sample mean, meaning severe underestimation of the severity of conflicts from naive observation. We check for robustness by sampling between high and low estimates and jackknifing the data. We study interarrival times between tail events and find (firstorder) memorylessless of events. The statistical pictures obtained are at variance with the claims about "long peace". On the tail risk of violent conflict and its underestimation Pasquale Cirillo, Nassim Nicholas Taleb http://arxiv.org/abs/1505.04722
Via Complexity Digest
Although positive incentives for cooperators and/or negative incentives for freeriders in social dilemmas play an important role in maintaining cooperation, there is still the outstanding issue of who should pay the cost of incentives. The secondorder freerider problem, in which players who do not provide the incentives dominate in a game, is a wellknown academic challenge. In order to meet this challenge, we devise and analyze a metaincentive game that integrates positive incentives (rewards) and negative incentives (punishments) with secondorder incentives, which are incentives for other players’ incentives. The critical assumption of our model is that players who tend to provide incentives to other players for their cooperative or noncooperative behavior also tend to provide incentives to their incentive behaviors. In this paper, we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups. We find that the secondorder freerider problem is completely resolved without any thirdorder or higher (meta) incentive under the assumption. To do so, a secondorder costly incentive, which is given individually (peertopeer) after playing donation games, is needed. The paper concludes that (1) secondorder incentives for firstorder reward are necessary for cooperative regimes, (2) a system without firstorder rewards cannot maintain a cooperative regime, (3) a system with firstorder rewards and no incentives for rewards is the worst because it never reaches cooperation, and (4) a system with rewards for incentives is more likely to be a cooperative regime than a system with punishments for incentives when the costeffect ratio of incentives is sufficiently large. This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives.
Via Ashish Umre
Largescale data from social media have a significant potential to describe complex phenomena in real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, we identify the dynamics of the volume, and show that this quantity has some information on the elections outcome. We find that the distribution of the tweet volume for each party follows a lognormal distribution with a positive autocorrelation over short terms. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion. Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting shortterm tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media. Twitterbased analysis of the dynamics of collective attention to political parties YoungHo Eom, Michelangelo Puliga, Jasmina Smailović, Igor Mozetič, Guido Caldarelli http://arxiv.org/abs/1504.06861
Via Complexity Digest, Complexity Institute
