Complexity at large


  • Carlos Gershenson


The following news item is taken in part from the March 28, 2012 issue of PLoS ONE titled “Hierarchy Measure for Complex Networks,” by Enys Mones, Lilla Vicsek, and Tamás Vicsek.

Here, we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network.

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The following news item is taken in part from the February 12, 2012 issue of Information titled “What Is Information?: Why Is It Relativistic and What Is Its Relationship to Materiality, Meaning and Organization,” by Robert K. Logan.

We review the historic development of concept of information including the relationship of Shannon information and entropy and the criticism of Shannon information because of its lack of a connection to meaning. We review the work of Kauffman, Logan et al. that shows that Shannon information fails to describe biotic information. We introduce the notion of the relativity of information and show that the concept of information depends on the context of where and how it is being used. We examine the relationship of information to meaning and materiality within information theory, cybernetics, and systems biology. We show that there exists a link between information and organization in biotic systems and in the various aspects of human culture including language, technology, science, economics, and governance.

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The following news item is taken in part from the April 13, 2012 issue of Science titled “Does It Compute?,” by Valda Vinson, Beverly A. Purnell, Laura M. Zahn, and John Travis.

A discussion of computational biology has to start with a pioneer of the field, Alan Turing, especially in this centennial year of his birth. He introduced us to the digital computer and proposed that much biology could be described by mathematical equations—the number of spirals in a sunflower is a Fibonacci number and pattern formation in animal skins can be described by a reaction diffusion model. Turing lacked the data and the computing power to substantiate his models. Today, the availability of vast quantities of new data, together with striking advances in computing power, is promising to give us new insights into the mechanisms of life. This special section, together with related content in Science Signaling and Science Careers, highlights recent advances and outstanding challenges.

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The following news item is taken in part from the April, 2012 issue of Physics Today titled “Networks in motion,” by Adilson E. Motter and Réka Albert.

Networks that govern communication, growth, herd behavior, and other key processes in nature and society are becoming increasingly amenable to modeling, forecast, and control.

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The following news item is taken in part from the April 5, 2012 issue of Nature titled “A universal model for mobility and migration patterns,” by Filippo Simini, Marta C. González, Amos Maritan, and Albert-László Barabási.

Introduced in its contemporary form in 1946 (…) the gravity law is the prevailing framework with which to predict population movement cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here, we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.

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The following news item is taken in part from the February, 2012 issue of arXiv titled “Patterns of cooperation: fairness and coordination in networks of interacting agents,” by Anne-Ly Do, Lars Rudolf, and Thilo Gross.

We study the self-assembly of a complex network of collaborations among self-interested agents. The agents can maintain different levels of cooperation with different partners. Further, they continuously, selectively, and independently adapt the amount of resources allocated to each of their collaborations to maximize the obtained payoff. We show analytically that the system approaches a state in which the agents make identical investments, and links produce identical benefits. Despite this high degree of social coordination some agents manage to secure privileged topological positions in the network enabling them to extract high payoffs. Our analytical investigations provide a rationale for the emergence of unidirectional nonreciprocal collaborations and different responses to the withdrawal of a partner from an interaction that have been reported in the psychological literature.

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The following news item is taken in part from the April, 2012 issue of arXiv titled “Challenges in Complex Systems Science,” by Maxi San Miguel, Jeffrey H. Johnson, Janos Kertesz, Kimmo Kaski, Albert Díaz-Guilera, Robert S. MacKay, Vittorio Loreto, Peter Erdi, and Dirk Helbing.

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; nonequilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; multilevel dynamics.

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The following news item is taken in part from the March 27, 2012 issue of PNAS titled “Structural diversity in social contagion,” by Johan Ugander, Lars Backstrom, Cameron Marlow, and Jon Kleinberg.

The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as fads, political opinions, the adoption of new technologies, and financial decisions. (...) We find that the probability of contagion is tightly controlled by the number of connected components in an individual's contact neighborhood, rather than by the actual size of the neighborhood. Surprisingly, once this “structural diversity” is controlled for, the size of the contact neighborhood is in fact generally a negative predictor of contagion. More broadly, our analysis shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.

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The following news item is taken in part from the May 4, 2012 issue of Scientific Reports titled “Universal features of correlated bursty behaviour,” by Márton Karsai, Kimmo Kaski, Albert-László Barabási and János Kertész.

Inhomogeneous temporal processes, like those appearing in human communications, neuron spike trains, and seismic signals, consist of high-activity bursty intervals alternating with long low-activity periods. In recent studies, such bursty behavior has been characterized by a fat-tailed inter-event time distribution, whereas temporal correlations were measured by the autocorrelation function. However, these characteristic functions are not capable to fully characterize temporally correlated heterogenous behavior. Here, we show that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution for a broad class of phenomena. We find that the correlations in these quite different systems can be commonly interpreted by memory effects and described by a simple phenomenological model, which displays temporal behavior qualitatively similar to that in real systems.

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The following news item is taken in part from the May 2, 2012 issue of PLoS ONE titled “Chromatin Computation,” by Barbara Bryant.

In living cells, DNA is packaged along with protein and RNA into chromatin. Chemical modifications to nucleotides and histone proteins are added, removed and recognized by multi-functional molecular complexes. Here, I define a new computational model, in which chromatin modifications are information units that can be written onto a one-dimensional string of nucleosomes, analogous to the symbols written onto cells of a Turing machine tape,... I prove that chromatin computers are computationally universal—and therefore, more powerful than the logic circuits often used to model transcription factor control of gene expression.

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The following news item is taken in part from the May, 2012 issue of Communications of the ACM titled “Programming the Global Brain,” by Abraham Bernstein, Mark Klein, and Thomas W. Malone.

New ways of combining networked humans and computers—whether they are called collective intelligence, social computing, or various other terms—are already extremely important and likely to become truly transformative in domains from education and industry to government and the arts. These systems are now routinely able to solve problems that would have been unthinkably difficult only a few short years ago, combining the communication and number-crunching capabilities of computer systems with the creativity and high-level cognitive capabilities of people.

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The following news item is taken in part from the April, 2012 issue of arXiv titled “The Emergence of Modularity in Biological Systems,” by Dirk M. Lorenz, Alice Jeng, and Michael W. Deem.

In this review, we discuss modularity and hierarchy in biological systems. We review examples from protein structure, genetics, and biological networks of modular partitioning of the geometry of biological space. We review theories to explain modular organization of biology, with a focus on explaining how biology may spontaneously organize to a structured form. That is, we seek to explain how biology nucleated from among the many possibilities in chemistry.

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The following news item is taken in part from the May, 2012 issue of Phys. Rev. Lett. titled “Controlling Complex Networks: How Much Energy Is Needed?,” by Gang Yan, Jie Ren, Ying-Cheng Lai, Choy-Heng Lai, and Baowen Li.

The outstanding problem of controlling complex networks is relevant to many areas of science and engineering, and has the potential to generate technological breakthroughs as well. We address the physically important issue of the energy required for achieving control by deriving and validating scaling laws for the lower and upper energy bounds. These bounds represent a reasonable estimate of the energy cost associated with control, and provide a step forward from the current research on controllability toward ultimate control of complex networked dynamical systems.

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The following news item is taken in part from the April, 2012 issue of Information Sciences titled “Local measures of information storage in complex distributed computation,” by Joseph T. Lizier, Mikhail Prokopenko, and Albert Y. Zomaya.

Information storage is a key component of intrinsic distributed computation. Despite the existence of appropriate measures for it (e.g., excess entropy), its role in interacting with information transfer and modification to give rise to distributed computation is not yet well-established. We explore how to quantify information storage on a local scale in space and time, so as to understand its role in the dynamics of distributed computation. To assist these explorations, we introduce the active information storage, which quantifies the information storage component that is directly in use in the computation of the next state of a process. We present the first profiles of local excess entropy and local active information storage in cellular automata, providing evidence that blinkers and background domains are dominant information storage processes in these systems. This application also demonstrates the manner in which these two measures of information storage are distinct but complementary. It also reveals other information storage phenomena, including the misinformative nature of local storage when information transfer dominates the computation, and demonstrates that the local entropy rate is a useful spatiotemporal filter for information transfer structure.

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The following news item is taken in part from the May 8, 2012 issue of PLoS Biol titled “Bit by Bit: The Darwinian Basis of Life,” by Gerald F. Joyce.

All known examples of life belong to the same biology, but there is increasing enthusiasm among astronomers, astrobiologists, and synthetic biologists that other forms of life may soon be discovered or synthesized. This enthusiasm should be tempered by the fact that the probability for life to originate is not known. As a guiding principle in parsing potential examples of alternative life, one should ask: How many heritable “bits” of information are involved, and where did they come from? A genetic system that contains more bits than the number that were required to initiate its operation might reasonably be considered a new form of life.

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The following news item is taken in part from “Some Computational Aspects of Essential Properties of Evolution and Life,” by Hector Zenil, James A.R. Marshall which appeared in arXiv.

Although evolution has inspired algorithmic methods of heuristic optimisation, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological phenomena. We argue that under reasonable assumptions, interesting conclusions can be drawn that are of relevance to behavioral evolution. We will focus on two important features of life—robustness and fitness—which, we will argue, are related to algorithmic probability and to the thermodynamics of computation, disciplines that may be capable of modelling key features of living organisms, and which can be used in formulating new algorithms of evolutionary computation.

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The following news item is taken in part from “Language Dynamics,” by Andrea Baroncelli, Vittorio Loreto and Francesca Tria which appeared in Advances in Complex Systems.

Thirty authors of different disciplines, ranging from cognitive science and linguistics to mathematics and physics, address the topic of language origin and evolution. Language dynamics is investigated through an interdisciplinary effort, involving field and synthetic experiments, modeling, and comparison of the theoretical predictions with empirical data. The result consists in new insights that significantly contribute to the ongoing debate on the origin and the evolution of language. In this Topical Issue, the state of the art of this novel and fertile approach is reported by major experts of the field.

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The following news item is taken in part from “Ice structures, patterns, and processes: A view across the icefields,” by Thorsten Bartels-Rausch et al. which appeared in Rev. Mod. Phys. 84, 885–944.

From the frontiers of research on ice dynamics in its broadest sense, this review surveys the structures of ice, the patterns or morphologies it may assume, and the physical and chemical processes in which it is involved. Open questions in the various fields of ice research in nature are highlighted, ranging from terrestrial and oceanic ice on Earth, to ice in the atmosphere, to ice on other Solar System bodies and in interstellar space.

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The following news item is taken in part from “Long trend dynamics in social media,” by Chunyan Wang and Bernardo A Huberman which appeared in EPJ Data Science 2012, 1:2.

A main characteristic of social media is that its diverse content, copiously generated by both standard outlets and general users, constantly competes for the scarce attention of large audiences. Out of this flood of information some topics manage to get enough attention to become the most popular ones and thus to be prominently displayed as trends. Equally important, some of these trends persist long enough so as to shape part of the social agenda. How this happens is the focus of this paper.

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