Complexity at large


  • Edited by Carlos Gershenson


The following news item is taken in part from the 2013/03 issue of PLoS Comput Biol titled “Stability and Responsiveness in a Self-Organized Living Architecture,” by Simon Garnier, Tucker Murphy, Matthew Lutz, Edward Hurme, Simon Leblanc, and Iain D. Couzin.

Robustness and adaptability are central to the functioning of biological systems, from gene networks to animal societies. Yet the mechanisms by which living organisms achieve both stability to perturbations and sensitivity to input are poorly understood. Here, we present an integrated study of a living architecture in which army ants interconnect their bodies to span gaps. We demonstrate that these self-assembled bridges are a highly effective means of maintaining traffic flow over unpredictable terrain. The individual-level rules responsible depend only on locally-estimated traffic intensity and the number of neighbours to which ants are attached within the structure. We employ a parameterized computational model to reveal that bridges are tuned to be maximally stable in the face of regular, periodic fluctuations in traffic. However, analysis of the model also suggests that interactions among ants give rise to feedback processes that result in bridges being highly responsive to sudden interruptions in traffic. Subsequent field experiments confirm this prediction and thus the dual nature of stability and flexibility in living bridges. Our study demonstrates the importance of robust and adaptive modular architecture to efficient traffic organization and reveals general principles regarding the regulation of form in biological self-assemblies.

A link to this article can be found at


The following news item is taken in part from the 2013/04 issue of PLoS Med titled “Big Data Opportunities for Global Infectious Disease Surveillance,” by Simon I. Hay, Dylan B. George, Catherine L. Moyes, and John S. Brownstein.

Systems to provide static spatially continuous maps of infectious disease risk and continually updated reports of infectious disease occurrence exist but to-date the two have never been combined.

Novel online data sources, such as social media, combined with epidemiologically relevant environmental information are valuable new data sources that can assist the “real-time” updating of spatial maps.

Advances in machine learning and the use of crowd sourcing open up the possibility of developing a continually updated atlas of infectious diseases.

Freely available dynamic infectious disease risk maps would be valuable to a wide range of health professionals from policy makers prioritizing limited resources to individual clinicians.

A link to this article can be found at


The following news item is taken in part from the 2013/04 issue of PNAS titled “Limits of social mobilization,” by Alex Rutherford, Manuel Cebrian, Sohan Dsouza, Esteban Moro, Alex Pentland, and Iyad Rahwan.

The Internet and social media have enabled the mobilization of large crowds to achieve time-critical feats, ranging from mapping crises in real time, to organizing mass rallies, to conducting search-and-rescue operations over large geographies. Despite significant success, selection bias may lead to inflated expectations of the efficacy of social mobilization for these tasks. What are the limits of social mobilization, and how reliable is it in operating at these limits? We build on recent results on the spatiotemporal structure of social and information networks to elucidate the constraints they pose on social mobilization. We use the DARPA Network Challenge as our working scenario, in which social media were used to locate 10 balloons across the United States. We conduct high-resolution simulations for referral-based crowdsourcing and obtain a statistical characterization of the population recruited, geography covered, and time to completion. Our results demonstrate that the outcome is plausible without the presence of mass media but lies at the limit of what time-critical social mobilization can achieve. Success relies critically on highly connected individuals willing to mobilize people in distant locations, overcoming the local trapping of diffusion in highly dense areas. However, even under these highly favorable conditions, the risk of unsuccessful search remains significant. These findings have implications for the design of better incentive schemes for social mobilization. They also call for caution in estimating the reliability of this capability.

A link to this article can be found at


The following news item is taken in part from the 2013/04 issue of arXiv titled “Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis,” by Nelson Fernandez, Carlos Maldonado, and Carlos Gershenson.

In this chapter review measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem.

Emergence is defined as the information produced by a system or process. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at multiple scales, which can be studied with multiscale profiles.

A link to this article can be found at


The following news item is taken in part from the 2013/03 issue of Scientific Reports titled “Evolution of collective action in adaptive social structures,” by João A. Moreira, Jorge M. Pacheco, and Francisco C. Santos.

Many problems in nature can be conveniently framed as a problem of evolution of collective cooperative behavior, often modelled resorting to the tools of evolutionary game theory in well-mixed populations, combined with an appropriate N-person dilemma. Yet, the well-mixed assumption fails to describe the population dynamics whenever individuals have a say in deciding which groups they will participate. Here we propose a simple model in which dynamical group formation is described as a result of a topological evolution of a social network of interactions. We show analytically how evolutionary dynamics under public goods games in finite adaptive networks can be effectively transformed into a N-Person dilemma involving both coordination and coexistence. Such dynamics would be impossible to foresee from more conventional two-person interactions as well as from descriptions based on infinite, well-mixed populations. Finally, we show how stochastic effects help rendering cooperation viable, promoting polymorphic configurations in which cooperators prevail.

A link to this article can be found at


The following news item is taken in part from the 2013/04 issue of EPJ Data Science titled “The Dynamics of Health Behavior Sentiments on a Large Online Social Network,” by Marcel Salathe, Duy Q Vu, Shashank Khandelwal, and David R Hunter.

Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals—homophily—or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious—by which we merely mean predictive of future negative sentiment expression—while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content dependent.

A link to this article can be found at


The following news item is taken in part from the 2013/04 issue of Cognitive Science titled “Neural Computation and the Computational Theory of Cognition,” by Gualtiero Piccinini and Sonya Bahar.

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. However, current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.

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The following news item is taken in part from the 2013/04 issue of Complex Adaptive Systems Modeling titled “Information and phase transitions in socio-economic systems,” by Terry Bossomaier, Lionel Barnett, and Michael Harré.

We examine the role of information-based measures in detecting and analysing phase transitions. We contend that phase transitions have a general character, visible in transitions in systems as diverse as classical flocking models, human expertise, and social networks. Information-based measures such as mutual information and transfer entropy are particularly suited to detecting the change in scale and range of coupling in systems that herald a phase transition in progress, but their use is not necessarily straightforward, possessing difficulties in accurate estimation due to limited sample sizes and the complexities of analyzing non-stationary time series. These difficulties are surmountable with careful experimental choices. Their effectiveness in revealing unexpected connections between diverse systems makes them a promising tool for future research.

A link to this article can be found at


The following news item is taken in part from the 2013/04 issue of Complex Adaptive Systems Modeling titled “Information theoretical methods for complex network structure reconstruction,” by Enrique Hernández-Lemus and Jesús M Siqueiros-García.

Information-theoretical measures (in particular, Mutual Information) are applied for the probabilistic inference of complex networks. Data Processing Inequality is used to find and assess for direct and indirect interactions impact in complex networks.

We outline the mathematical basis of information, theoretical assessment of complex network structure and discuss some examples of application in the fields of biological systems and social networks.

A link to this article can be found at


The following news item is taken in part from the 2013/03 issue of arXiv titled “Beyond Reductionism Twice: No Laws Entail Biosphere Evolution, Formal Cause Laws Beyond Efficient Cause Laws,” by Stuart Kauffman.

Newton set the stage for our view of how science should be done. We remain in what I will call the “Newtonian Paradigm” in all of physics, including Newton, Einstein, and Schrodinger. As I will show shortly, Newton invented and bequeathed to us ‘efficient cause entailing laws’ for the entire becoming of the universe. With Laplace this became the foundation of contemporary reductionism in which all that can happen in the world is due to efficient cause entailing laws. More this framework stands as our dominant way to do science. The Newtonian Paradigm has done enormous work in science, and helped lead to the Industrial Revolution, and even our entry into Modernity.

In this article, I propose to challenge the adequacy of the Newtonian Paradigm on two ground: (1) For the evolution of the biosphere beyond the watershed of life, we can formulate no efficient cause entailing laws that allow us to deduce the evolution of the biosphere. A fortiori, the same holds for the evolution of the economy, legal systems, social systems, and culture. Because I have discussed this before with my colleagues Longo and Montevil and elsewhere, my discussion of this first point will be rather brief. (2) What I shall choose to call, after Aristotle's four causes, noted below, Formal Cause Laws derived from specific “ensemble theories” tell us about the world. However, Formal Cause laws are not reducible to efficient cause entailing laws of the Newtonian Paradigm and, critically, have already, unnoticed, crept into biology concerning the origin of life, and economics concerning economic growth. Formal cause laws appear to be a new way to do science, independent of efficient cause entailing laws. Thus Formal Cause laws can be independent of any specific material substrate. This may bear on the sufficiency of Materialism in our account of the world.

A link to this article can be found at


The following news item is taken in part from the 2013/03 issue of PLoS ONE titled “Universities Scale Like Cities,” by Anthony F. J. van Raan.

Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this article, we demonstrate that universities show a similar behavior as cities in the distribution of the “gross university income” in terms of total number of citations over “size” in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling. We find that deviations from the expected behavior can indeed be explained by specific local properties of universities, particularly the field-specific composition of a university, and its quality in terms of field-normalized citation impact. By studying both the set of the 500 largest universities worldwide and a specific subset of these 500 universities—the top-100 European universities—we are also able to distinguish between properties of universities with as well as without selection of one specific local property, the quality of a university in terms of its average field-normalized citation impact. It also reveals an interesting observation concerning the working of a crucial property in networked systems, preferential attachment.

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The following news item is taken in part from the 2013/03 issue of J. Phys. A: Math. Theor. titled “Networking—a statistical physics perspective,” by Chi Ho Yeung and David Saad.

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.

A link to this article can be found at


The following news item is taken in part from the 2013/03 issue of arXiv titled “On active information storage in input-driven systems,” by Oliver Obst, Joschka Boedecker, Benedikt Schmidt, Minoru Asada.

Information theory and the framework of information dynamics have been used to provide tools to characterise complex systems. In particular, we are interested in quantifying information storage, information modification and information transfer as characteristic elements of computation. Although these quantities are defined for autonomous dynamical systems, information dynamics can also help to get a “wholistic” understanding of input-driven systems such as neural networks. In this case, we do not distinguish between the system itself, and the effects the input has to the system. This may be desired in some cases, but it will change the questions we are able to answer, and is consequently an important consideration, for example, for biological systems which perform non-trivial computations and also retain a short-term memory of past inputs. Many other real world systems like cortical networks are also heavily input-driven, and application of tools designed for autonomous dynamic systems may not necessarily lead to intuitively interpretable results.

The aim of our work is to extend the measurements used in the information dynamics framework for input-driven systems. Using the proposed input-corrected information storage we hope to better quantify system behaviour, which will be important for heavily input-driven systems like artificial neural networks to abstract from specific benchmarks, or for brain networks, where intervention is difficult, individual components cannot be tested in isolation or with arbitrary input data.

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The following news item is taken in part from the 2013/03 issue of Scientific Reports titled “Unique in the Crowd: The privacy bounds of human mobility,” by Yves-Alexandre de Montjoye, César A. Hidalgo, Michel Verleysen, and Vincent D. Blondel.

We study 15 months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.

A link to this article can be found at


The following news item is taken in part from the 2013/03 issue of Environment and Planning B: Planning and Design titled “The future cities agenda,” by Michael Batty.

Suddenly, “cities” have become the hottest topic on the planet. National research institutes and local governments as well as various global agencies are all scrambling to get a piece of the action as cities become the places where it is considered future economic prosperity firmly lies while also offering the prospect of rescuing a developed world mired in recession.

A link to this article can be found at


The following news item is taken in part from the 2013/03 issue of Complex Adaptive Systems Modeling titled “Segregation mechanisms of tissue cells: from experimental data to models,” by Előd Méhes and Tamás Vicsek.

Considerable advance has been made in recent years in the research field of pattern formation by segregation of tissue cells. Research has become more quantitative partly due to more in-depth analysis of experimental data and the emergence modeling approaches. In this review, we present experimental observations, including some of our new results, on various aspects of two and three dimensional segregation events and then summarize the computational modeling approaches.

A link to this article can be found at–3206-1-4.


The following news item is taken in part from the 2013/03 issue of arXiv titled “Hierarchy in complex systems: the possible and the actual,” by Bernat Corominas-Murtra, Joaquín Goñi, Ricard V. Solé, and Carlos Rodríguez-Caso.

Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach resulting from the convergence of theoretical morphology and network theory that allows constructing a 3D morphospace of hierarchies and hence comparing the hierarchical organization of ecological, cellular, technological, and social networks. Embedded within large voids in the morphospace of all possible hierarchies, four major groups are identified. Two of them match the expected from random networks with similar connectivity, thus suggesting that nonadaptive factors are at work. Ecological and gene networks define the other two, indicating that their topological order is the result of functional constraints. These results are consistent with an exploration of the morphospace using in silico evolved networks.

A link to this article can be found at