Libros y Papers sobre Complejidad - Sistemas Complejos
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Publicaciones sobre Complejidad y Sistemas Complejos
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Dynamics of deceptive interactions in social networks

In this paper we examine the role of lies in human social relations by implementing some salient characteristics of deceptive interactions into an opinion formation model, so as to describe the dynamical behaviour of a social network more realistically. In this model we take into account such basic properties of social networks as the dynamics of the intensity of interactions, the influence of public opinion, and the fact that in every human interaction it might be convenient to deceive or withhold information depending on the instantaneous situation of each individual in the network. We find that lies shape the topology of social networks, especially the formation of tightly linked, small communities with loose connections between them. We also find that agents with a larger proportion of deceptive interactions are the ones that connect communities of different opinion, and in this sense they have substantial centrality in the network. We then discuss the consequences of these results for the social behaviour of humans and predict the changes that could arise due to a varying tolerance for lies in society.

 

Dynamics of deceptive interactions in social networks
Rafael A. Barrio, Tzipe Govezensky, Robin Dunbar, Gerardo Iñiguez, Kimmo Kaski

http://arxiv.org/abs/1509.03918


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Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities

While cities have been the engine for innovation and growth for many millennia, they have also endured disproportionately more crime than smaller cities. Similarly to other urban sociological quantities, such as income, gross domestic product (GDP) and number of granted patents, it has been observed that crime scales super-linearly with city size. The default assumption is that super-linear scaling of crime, like other urban attributes, derives from agglomerative effects (that is, increasing returns from potentially more productive connections among criminals). However, crime initiation appears to be generated linearly with the population of a city, and the number of law enforcement officials scales sublinearly with city population. We hypothesize that the observed scaling exponent for net crime in a city is the result of competing dynamics between criminals and law enforcement, each with different scaling exponents, and where criminals win in the numbers game. We propose a simple dynamical model able to accommodate these empirical observations, as well as the potential multiple scaling regimes emerging from the competitive dynamics between crime and law enforcement. Our model is also general enough to be able to correctly account for crime in universities, where university crime does not scale super-linearly, but linearly with enrolment size.

 

Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities
Soumya Banerjee, Pascal Van Hentenryck & Manuel Cebrian

Palgrave Communications 1, Article number: 15022 (2015) http://dx.doi.org/10.1057/palcomms.2015.22 


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The Globe of Economic Complexity

The Globe of Economic Complexity | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

Visualize $15 Trillion of World Exports

One dot equals $100M of exports

 

http://globe.cid.harvard.edu


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Luciano Lampi's curator insight, August 30, 2015 12:06 PM

Fantastic tool. Explore it.

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Supersampling and Network Reconstruction of Urban Mobility

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. 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 required.

 

Sagarra O, Szell M, Santi P, Díaz-Guilera A, Ratti C (2015) Supersampling and Network Reconstruction of Urban Mobility. PLoS ONE 10(8): e0134508. http://dx.doi.org/10.1371/journal.pone.0134508 ;


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Anticipating Economic Market Crises Using Measures of Collective Panic

Anticipating Economic Market Crises Using Measures of Collective Panic | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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 self-generated nervousness. Here we show that the recent economic crisis and earlier large single-day 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
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Fractal and Small-World Networks Formed by Self-Organized Critical Dynamics

Fractal and Small-World Networks Formed by Self-Organized Critical Dynamics | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

We propose a dynamical model in which a network structure evolves in a self-organized critical (SOC) manner and explain a possible origin of the emergence of fractal and small-world 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 power-law 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 small-world structures. We also observe the crossover behavior from fractal to small-world structure in the network evolution.


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In social networks, group boundaries promote the spread of ideas, study finds

In social networks, group boundaries promote the spread of ideas, study finds | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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?

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Keith Hamon's curator insight, July 3, 2015 5:34 PM

Boundaries are critical for the movement of information. They enable the flow of information.

 

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Cascades in multiplex financial networks with debts of different seniority

Cascades in multiplex financial networks with debts of different seniority | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

A model of a banking network predicts the balance of high- and low-priority debts that ensures financial stability.

 

Synopsis: http://physics.aps.org/synopsis-for/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. 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.

 

Charles D. Brummitt and Teruyoshi Kobayashi

Phys. Rev. E 91, 062813 (2015)

Published June 24, 2015


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Stochastic game dynamics under demographic fluctuations

Stochastic game dynamics under demographic fluctuations | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
Frequency-dependent selection and demographic fluctuations play important roles in evolutionary and ecological processes. Under frequency-dependent 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 frequency-dependent competition between different types in an individual-based 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.

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The non-linear health consequences of living in larger cities

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

http://arxiv.org/abs/1506.02735


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Kinetics of Social Contagion

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, non-zero rate of dynamically-introduced 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


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[1505.05738] Predictability of Critical Transitions

[1505.05738] Predictability of Critical Transitions | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

Critical transitions in multistable systems have been discussed as models for a variety of phenomena ranging from the extinctions of species to socio-economic changes and climate transitions between ice-ages and warm-ages. 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 integrate-and-fire 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.


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Models: From exploration to prediction: Bad reputation of modeling in some disciplines results from nebulous goals

Until the second half of last century, science was progressing on two legs, theory and experiment. Karl Popper built his Logic of Scientific Discovery [1] on the dichotomy of these two pillars. He said in a nutshell: The established theories reflect the state of the art in science, theories are falsified by new experimental data, and new theories emerge that can explain the new findings together with the established body of knowledge. The two examples par excellence for Popper's epistemology are (i) Einstein's theory of relativity and (ii) quantum mechanics. The advent of electronic computation in the middle of the 20th century changed the situation and brought a new player on the stage: scientific computing. The very modest possibilities, computational speed, and storage capacities of the early electronic computers allowed for handling highly approximate models only and the prediction made by the pioneers in numerical research were commonly ridiculed by hard-nosed experimenters. By now, the situation has completely changed because of the breath-taking development of electronic facilities, and computational science has indeed become the third leg on which gain in scientific knowledge rests. Although the computational approach has become a well-established research tool there are still serious misunderstandings and wrong expectations in the significance of the results derived from computer models. This essay makes an attempt to illustrate some of the common problems.

 

Models: From exploration to prediction: Bad reputation of modeling in some disciplines results from nebulous goals
Peter Schuster

Complexity

http://dx.doi.org/10.1002/cplx.21729


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The multi-layer network nature of systemic risk and its implications for the costs of financial crises

•We present a multi-layer network approach to quantify systemic-risk.
•Systemic-risk is drastically underestimated when computed on single layers only, as is current practice.
•We introduce a nation-wide systemic-risk index that reflects the public costs for crises.
•The index unveils drastically higher risk than estimated by current risk indicators.
•We demonstrate the validity of the method on a complete dataset of the Mexican financial system.

 

The multi-layer network nature of systemic risk and its implications for the costs of financial crises
Sebastian Poledna, José Luis Molina-Borboa, Serafín Martínez-Jaramillo, , Marco van der Leij, Stefan Thurner

http://dx.doi.org/10.1016/j.jfs.2015.08.001 ;


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The collaborative roots of corruption

Recent financial scandals highlight the devastating consequences of corruption. While much is known about individual immoral behavior, little is known about the collaborative roots of curruption. In a novel experimental paradigm, people could adhere to one of two competing moral norms: collaborate vs. be honest. Whereas collaborative settings may boost honesty due to increased observability, accountability, and reluctance to force others to become accomplices, we show that collaboration, particularly on equal terms, is inductive to the emergence of corruption. When partners' profits are not aligned, or when individuals complete a comparable task alone, corruption levels drop. These findings reveal a dark side of collaboration, suggesting that human cooperative tendencies, and not merely greed, take part in shaping corruption.

 

The collaborative roots of corruption
Ori Weisela and Shaul Shalvi

PNAS

http://dx.doi.org/10.1073/pnas.1423035112 ;


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Introduction to the Modeling and Analysis of Complex Systems | Open SUNY Textbooks

Introduction to the Modeling and Analysis of Complex Systems | Open SUNY Textbooks | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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Creating an Artificial World with a New Kind of Cellular Automata

Creating an Artificial World with a New Kind of Cellular Automata | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

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.


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Exploiting chaos for applications

Exploiting chaos for applications | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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.

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25 Years of Self-Organized Criticality: Numerical Detection Methods

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

http://arxiv.org/abs/1506.08142 ;


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Marcelo Errera's curator insight, July 25, 2015 11:12 AM
What is complexity ? Is it an effect caused by some principle ? Is it the cause of something else ? Those questions are fundamental. By the way, CL publications have been showing power law, not exponential are the correct mathematical expression for most natural phenomena.
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Twitter Sentiment Analysis

This paper presents a step-by-step 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 lexicon-based 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%.

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The Majority Illusion in Social Networks

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 real-world 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, Xin-Zeng Wu

http://arxiv.org/abs/1506.03022


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june holley's curator insight, June 15, 2015 7:25 AM

We need to develop strategies so that its not easy for us or others to develop majority illusions.


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[1506.04544] Interplay between consensus and coherence in a model of interacting opinions

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Maintaining Homeostasis by Decision-Making

Maintaining Homeostasis by Decision-Making | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

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 high-calorie but hard-to-get, and low-calorie but easy-to-get 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 decision-making 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 decision-making, 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, casino-like 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 rank-dependent utility. This implies that under precisely defined constraints biological principles are better suited to explain human decision-making than economic models based on endpoint utility maximization.


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Percolation on Networks with Conditional Dependence Group

Percolation on Networks with Conditional Dependence Group | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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.

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