One interesting characteristic of some complex systems is the formation of macro level constructions perceived as having features that cannot be reduced to their micro level constituents. This characteristic is considered to be the expression of synergy where the joint action of the constituents produces unique features that are irreducible to the constituents isolated behavior or their simple composition. The synergy, characterizing complex systems, has been well acknowledged but difficult to conceptualize and quantify in the context of computing the emerging meaning of various linguistic and conceptual constructs. In this paper, we propose a novel measure/procedure for quantifying semantic synergy. This measure draws on a general idea of synergy as has been proposed in biology. We validate this measure by providing evidence for its ability to predict the semantic transparency of linguistic compounds (Experiment 1) and the abstractness rating of nouns (Experiment 2).
A Novel Procedure for Measuring Semantic Synergy Yair Neuman, Yiftach Neuman, and Yochai Cohen
This short note draws some connections between Mandelbrot‗s empirical legacy, and the interdisciplinary work that followed in finance. Much of this work is now labeled econophysics, but some has always been more in the realm of economics than physics. In a few areas the overlap is even becoming quite complete as in market microstructure. I will also give some ideas about the various successes and failures in this area, and some directions for the future of agent- based modeling in particular.
LeBaron, B. Eur. Phys. J. Spec. Top. (2016) 225: 3243. doi:10.1140/epjst/e2016-60123-4
This article describes the themes found in the past 25 years of creativity research. Computational methods and network analysis were used to map keyword theme development across ~1,400 documents and ~5,000 unique keywords from 1990 (the first year keywords are available in Web of Science) to 2015.
Mapping the Themes, Impact, and Cohesion of Creativity Research over the Last 25 Years Rich Williams, Mark A. Runco & Eric Berlow Creativity Research Journal.Volume 28, 2016 - Issue 4 Pages 385-394 | Published online: 14 Nov 2016
This paper aims to establish theoretical foundations of graph product multilayer networks (GPMNs), a family of multilayer networks that can be obtained as a graph product of two or more factor networks. Cartesian, direct (tensor), and strong product operators are considered, and then generalized. We first describe mathematical relationships between GPMNs and their factor networks regarding their degree/strength, adjacency, and Laplacian spectra, and then show that those relationships can still hold for nonsimple and generalized GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic thresholds, modeling propagation in nontrivial space and time, and analyzing higher-order properties of self-similar networks. Directions of future research are also discussed.
Graph Product Multilayer Networks: Spectral Properties and Applications Hiroki Sayama
Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system’s configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.
Detection of timescales in evolving complex systems Richard K. Darst, Clara Granell, Alex Arenas, Sergio Gómez, Jari Saramäki & Santo Fortunato
A multidimensional financial system could provide benefits for individuals, companies, and states. Instead of top-down control, which is destined to eventually fail in a hyperconnected world, a bottom-up creation of value can unleash creative potential and drive innovations. Multiple currency dimensions can represent different externalities and thus enable the design of incentives and feedback mechanisms that foster the ability of complex dynamical systems to self-organize and lead to a more resilient society and sustainable economy. Modern information and communication technologies play a crucial role in this process, as Web 2.0 and online social networks promote cooperation and collaboration on unprecedented scales. Within this contribution, we discuss how one dimension of a multidimensional currency system could represent socio-digital capital (Social Bitcoins) that can be generated in a bottom-up way by individuals who perform search and navigation tasks in a future version of the digital world. The incentive to mine Social Bitcoins could sustain digital diversity, which mitigates the risk of totalitarian control by powerful monopolies of information and can create new business opportunities needed in times where a large fraction of current jobs is estimated to disappear due to computerisation.
A “Social Bitcoin” could sustain a democratic digital world
Kleineberg, KK. & Helbing, D. Eur. Phys. J. Spec. Top. (2016) 225: 3231. doi:10.1140/epjst/e2016-60156-7
Citations are commonly held to represent scientific impact. To date, however, there is no empirical evidence in support of this postulate that is central to research assessment exercises and Science of Science studies. Here, we report on the first empirical verification of the degree to which citation numbers represent scientific impact as it is actually perceived by experts in their respective field. We run a large-scale survey of about 2000 corresponding authors who performed a pairwise impact assessment task across more than 20000 scientific articles. Results of the survey show that citation data and perceived impact do not align well, unless one properly accounts for strong psychological biases that affect the opinions of experts with respect to their own papers vs. those of others. First, researchers tend to largely prefer their own publications to the most cited papers in their field of research. Second, there is only a mild positive correlation between the number of citations of top-cited papers in given research areas and expert preference in pairwise comparisons. This also applies to pairs of papers with several orders of magnitude differences in their total number of accumulated citations. However, when researchers were asked to choose among pairs of their own papers, thus eliminating the bias favouring one's own papers over those of others, they did systematically prefer the most cited article. We conclude that, when scientists have full information and are making unbiased choices, expert opinion on impact is congruent with citation numbers.
Quantifying perceived impact of scientific publications
Filippo Radicchi, Alexander Weissman, Johan Bollen
Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Rényi q entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First, we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed with appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral-based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.
Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison Manlio De Domenico and Jacob Biamonte Phys. Rev. X 6, 041062 – Published 21 December 2016
A characteristic feature of complex systems is their deep structure, meaning that the definition of their states and observables depends on the level, or the scale, at which the system is considered. This scale dependence is reflected in the distinction of micro- and macro-states, referring to lower and higher levels of description. There are several conceptual and formal frameworks to address the relation between them. Here, we focus on an approach in which macrostates are contextually emergent from (rather than fully reducible to) microstates and can be constructed by contextual partitions of the space of microstates. We discuss criteria for the stability of such partitions, in particular under the microstate dynamics, and outline some examples. Finally, we address the question of how macrostates arising from stable partitions can be identified as relevant or meaningful.
Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems’ behavior.
Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction Geoff Boeing
Systems 2016, 4(4), 37; doi:10.3390/systems4040037
We re-examine the isotropic Precursor-Rule (of the anisotropic X-Rule) and show that it is also logically universal. The Precursor-Rule was selected from a sample of biased cellular automata rules classified by input-entropy. These biases followed most "Life-Like" constraints --- in particular isotropy, but not simple birth/survival logic. The Precursor-Rule was chosen for its spontaneously emergent mobile and stable patterns, gliders and eaters/reflectors, but glider-guns, originally absent, have recently been discovered, as well as other complex structures from the Game-of-Life lexicon. We demonstrate these newly discovered structures, and build the logical gates required for universality in the logical sense.
X-Rule's Precursor is also Logically Universal José Manuel Gómez Soto, Andrew Wuensche
The behavior of many real-world phenomena can be modeled by non-linear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a synchronous update graph dynamical system. Specifically, we study the structure learning problem for spatially distributed dynamical systems coupled via a directed acyclic graph. Unlike established structure learning procedures that find locally maximum posterior probabilities of a network structure containing latent variables, our work exploits the properties of dynamical systems to compute globally optimal approximations of these distributions. We arrive at this result by the use of time delay embedding theorems. Taking an information-theoretic perspective, we show that the log-likelihood has an intuitive interpretation in terms of information transfer.
An Information Criterion for Inferring Coupling of Distributed Dynamical Systems
Oliver M. Cliff, Mikhail Prokopenko and Robert Fitch
Ties between individuals on a social network can represent different dimensions of interactions, and the spreading of information and innovations on these networks could potentially be driven by some dimensions more than by others. In this paper we investigate this issue by studying the diffusion of microfinance within rural India villages and accounting for the whole multilayer structure of the underlying social networks. We define a new measure of node centrality, diffusion versatility, and show that this is a better predictor of microfinance participation rate than previously introduced measures defined on aggregated single-layer social networks. Moreover, we untangle the role played by each social dimension and find that the most prominent role is played by the nodes that are central on layers concerned with trust, shedding new light on the key triggers of the diffusion of microfinance.
Untangling the role of diverse social dimensions in the diffusion of microfinance Elisa Omodei and Alex Arenas Applied Network Science20161:14 DOI: 10.1007/s41109-016-0016-x
A characteristic property of networks is their ability to propagate influences, such as infectious diseases, behavioral changes, and failures. An especially important class of such contagious dynamics is that of cascading processes. These processes include, for example, cascading failures in infrastructure systems, extinctions cascades in ecological networks, and information cascades in social systems. In this review, we discuss recent progress and challenges associated with the modeling, prediction, detection, and control of cascades in networks.
Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.
Information theory, predictability, and the emergence of complex life Luís F Seoane, Ricard Solé
The process of “self-organization” takes place in open and complex systems that acquire spatio-temporal or functional structures without specific ordering instructions from the outside. In domains such as physics, chemistry or biology, the phrase, “far from equilibrium”, refers to systems that are “far from thermal equilibrium”, while in other disciplines, the term refers to the property of being “away from the resting state”. Such systems are “complex” in the sense that they are composed of many interacting components, parts, elements, etc., and “open” in the sense that they exchange with their environment matter, energy, and information. Here, “information” may imply Shannon information, as a measure of the capacity of a channel through which a message passes, pragmatic information, as the impact of a message on recipients, or semantic information, as the meaning conveyed by a message.
Information and Self-Organization Hermann Haken and Juval Portugali
The prevalence of many urban phenomena changes systematically with population size 1 . We propose a theory that unifies models of economic complexity 2,3 and cultural evolution 4 to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.
Explaining the prevalence, scaling and variance of urban phenomena Andres Gomez-Lievano, Oscar Patterson-Lomba & Ricardo Hausmann
Nature Human Behaviour 1, Article number: 0012 (2016) doi:10.1038/s41562-016-0012
Regarding costly punishment of two types, especially peer-punishment is considered to decrease the average payoff of all players as well as pool-punishment does, and to facilitate the antisocial punishment as a result of natural selection. To solve those problems, the author has proposed the probabilistic peer-punishment based on the difference of payoff. In the limited condition, the proposed peer-punishment has shown the positive effects on the evolution of cooperation, and increased the average payoff of all players.
Based on those findings, this study exhibits the characteristics of the evolution of cooperation by the proposed peer-punishment. Those characteristics present the significant contribution to knowledge that for the evolution of cooperation, a limited number of players should cause severe damage to defectors at the large expense of their payoff when connections between them are sparse, whereas a greater number of players should share the responsibility to punish defectors at the relatively small expense of their payoff when connections between them are dense.
Characteristics of the evolution of cooperation by the probabilistic peer-punishment based on the difference of payoff
Chaos, Solitons & Fractals Volume 95, February 2017, Pages 77–83
The introduction of ICT in techno-socio-economic systems, such as Smart Grids, traffic management, food supply chains and others, transforms the role of simulation as a scientific method for studying these complex systems. The scientific focus and challenge in simulations move from understanding system complexity to actually prototyping online and distributed regulatory mechanisms for supporting system operations. Existing simulation tools are not designed to address the challenges of this new reality, however, simulation is all about capturing reality at an adequate level of detail. This paper fills this gap by introducing a Java-based distributed simulation framework for inter-connected and inter-dependent techno-socio-economic system: SFINA, the Simulation Framework for Intelligent Network Adaptations. Three layers outline the design approach of SFINA: (i) integration of domain knowledge and dynamics that govern various techno-socio-economic systems, (ii) system modeling with dynamic flow networks represented by temporal directed weighted graphs and (iii) simulation of generic regulation models, policies and mechanisms applicable in several domains. SFINA aims at minimizing the fragmentation and discrepancies between different simulation communities by allowing the interoperability of SFINA with several other existing domain backends. The coupling of three such backends with SFINA is illustrated in the domain of Smart Grids and disaster mitigation. It is shown that the same model of cascading failures in Smart Grids is developed once and evaluated with both MATPOWER and InterPSS backends without changing a single line of application code. Similarly, application code developed in SFINA is reused for the evaluation of mitigation strategies in a backend that simulates the flows of a disaster spread. Results provide a proof-of-concept for the high modularity and reconfigurability of SFINA and puts the foundations of a new generation of simulation tools that prototype and validate online decentralized regulation in techno-socio-economic systems.
SFINA - Simulation Framework for Intelligent Network Adaptations
Evangelos Pournaras, Ben-Elias Brandt, Manish Thapa, Dinesh Acharya, Jose Espejo-Uribe, Mark Ballandies, Dirk Helbing
Simulation Modelling Practice and Theory Volume 72, March 2017, Pages 34–50
Kelp forests support diverse and productive ecological communities throughout temperate and arctic regions worldwide, providing numerous ecosystem services to humans. Literature suggests that kelp forests are increasingly threatened by a variety of human impacts, including climate change, overfishing, and direct harvest. We provide the first globally comprehensive analysis of kelp forest change over the past 50 y, identifying a high degree of variation in the magnitude and direction of change across the geographic range of kelps. These results suggest region-specific responses to global change, with local drivers playing an important role in driving patterns of kelp abundance. Increased monitoring aimed at understanding regional kelp forest dynamics is likely to prove most effective for the adaptive management of these important ecosystems.
Rhesus macaques experience variable levels of stress on the basis of their position in the social hierarchy. To examine how stress affects immune function, Snyder-Mackler et al. manipulated the social status of individual macaques (see the Perspective by Sapolsky). Social status influenced the immune system at multiple levels, from immune cell numbers to gene expression, and altered signaling pathways in a model of response to infection. Macaques possess a plastic and adaptive immune response wherein social subordination promotes antibacterial responses, whereas high social status promotes antiviral responses.
The emergence in the United States of large-scale “megaregions” centered on major metropolitan areas is a phenomenon often taken for granted in both scholarly studies and popular accounts of contemporary economic geography. This paper uses a data set of more than 4,000,000 commuter flows as the basis for an empirical approach to the identification of such megaregions. We compare a method which uses a visual heuristic for understanding areal aggregation to a method which uses a computational partitioning algorithm, and we reflect upon the strengths and limitations of both. We discuss how choices about input parameters and scale of analysis can lead to different results, and stress the importance of comparing computational results with “common sense” interpretations of geographic coherence. The results provide a new perspective on the functional economic geography of the United States from a megaregion perspective, and shed light on the old geographic problem of the division of space into areal units.
Dash Nelson G, Rae A (2016) An Economic Geography of the United States: From Commutes to Megaregions. PLoS ONE 11(11): e0166083. doi:10.1371/journal.pone.0166083
Many complex phenomena, from trait selection in biological systems to hierarchy formation in social and economic entities, show signs of competition and heterogeneous performance in the temporal evolution of their components, which may eventually lead to stratified structures such as the worldwide wealth distribution. However, it is still unclear whether the road to hierarchical complexity is determined by the particularities of each phenomena, or if there are generic mechanisms of stratification common to many systems. Human sports and games, with their (varied but simple) rules of competition and measures of performance, serve as an ideal test-bed to look for universal features of hierarchy formation. With this goal in mind, we analyse here the behaviour of performance rankings over time of players and teams for several sports and games, and find statistical regularities in the dynamics of ranks. Specifically the rank diversity, a measure of the number of elements occupying a given rank over a length of time, has the same functional form in sports and games as in languages, another system where competition is determined by the use or disuse of grammatical structures. We use a Gaussian random walk model to reproduce the rank diversity of the studied sports and games. We also discuss the relation between rank diversity and the cumulative rank distribution. Our results support the notion that hierarchical phenomena may be driven by the same underlying mechanisms of rank formation, regardless of the nature of their components. Moreover, such regularities can in principle be used to predict lifetimes of rank occupancy, thus increasing our ability to forecast stratification in the presence of competition.
Generic temporal features of performance rankings in sports and games
José A Morales, Sergio Sánchez, Jorge Flores, Carlos Pineda, Carlos Gershenson, Germinal Cocho, Jerónimo Zizumbo, Rosalío F Rodríguez, Gerardo Iñiguez
EPJ Data Sci. (2016) 5: 33. doi:10.1140/epjds/s13688-016-0096-y
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.
The many facets of community detection in complex networks Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
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