News items


  • Carlos Gershenson


The following news item is taken in part from the November 26, 2010 issue of Science titled “From the Connectome to the Synaptome: An Epic Love Story,” by Javier DeFelipe.

A major challenge in neuroscience is to decipher the structural layout of the brain. The term “connectome” has recently been proposed to refer to the highly organized connection matrix of the human brain. However, defining how information flows through such a complex system represents [an extremely] difficult (…) task (…). Circuit diagrams of the nervous system can be considered at different levels, although they are surely impossible to complete at the synaptic level. Nevertheless, advances in our capacity to marry macroscopic and microscopic data may help establish a realistic statistical model that could describe connectivity at the ultrastructural level, the “synaptome,” giving us cause for optimism.

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The following news item is taken in part from the October, 2010 issue of PLoS ONE titled “Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?,” by Konstantinos V. Katsikopoulos and Andrew J. King.

Using a set of simple models, we present theoretical conditions (involving group size and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions—where individuals are able to consider the success of previous decision outcomes—the collective's aggregated information is almost always superior.

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The following news item is taken in part from the November 27, 2010 issue of arXiv titled “Networks and the Epidemiology of Infectious Disease,” by Leon Danon, Ashley P. Ford, Thomas House, Chris P. Jewell, Matt J. Keeling, Gareth O. Roberts, Joshua V. Ross, and Matthew C. Vernon.

The science of networks has revolutionized research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here, we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterized; the statistical methods that can be applied to infer the epidemiological parameters on a realized network; and finally simulation and analytical methods to determine epidemic dynamics on a given network.

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The following news item is taken in part from the November 4, 2010 issue of PLoS Comput Biol titled “Infectious Disease Modeling of Social Contagion in Networks,” by Alison L. Hill, David G. Rand, Martin A. Nowak, and Nicholas A. Christakis.

Information, trends, behaviors, and even health states may spread between contacts in a social network, similar to disease transmission. However, a major difference is that as well as being spread infectiously, it is possible to acquire this state spontaneously. For example, you can gain knowledge of a particular piece of information either by being told about it, or by discovering it yourself. In this article, we introduce a mathematical modeling framework that allows us to compare the dynamics of these social contagions to traditional infectious diseases. As an example, we study the spread of obesity.

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The following news item is taken in part from the November 18, 2010 issue of PLoS Comput Biol titled “Network Analysis of Global Influenza Spread,” by Joseph Chan, Antony Holmes, and Raul Rabadan.

As evidenced by several historic vaccine failures, the design and implementation of the influenza vaccine remains an imperfect science. On a local scale, our technique can output the most likely origins of a virus circulating in a given location. On a global scale, we can pinpoint regions of the world that would maximally disrupt viral transmission with an increase in vaccine implementation. We demonstrate our method on seasonal H3N2 and H1N1 and foresee similar application to other seasonal viruses, including swine-origin H1N1, once more seasonal data are collected.

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The following news item is taken in part from the first issue of Cliodynamics: The Journal of Theoretical and Mathematical History titled “Cycling in the Complexity of Early Societies,” by Sergey Gavrilets, David G. Anderson, and Peter Turchin.

Warfare is commonly viewed as a driving force of the process of aggregation of initially independent villages into larger and more complex political units that started several 1000 years ago and quickly lead to the appearance of chiefdoms, states, and empires. Here, we build on extensions and generalizations of Carneiro's (1970) argument to develop a spatially explicit agent-based model of the emergence of early complex societies via warfare. A general prediction of our model is continuous stochastic cycling in which the growth of individual polities in size, wealth/power, and complexity is interrupted by their quick collapse.

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The following news item is taken in part from the November 19, 2010 issue of arXiv titled “Hierarchy and information in feedforward networks,” by Bernat Corominas-Murtra, Joaquín Goñi, Carlos Rodríguez-Caso, and Ricard Solé.

In this article, we define a hierarchical index for feedforward structures taking, as the starting point, three fundamental concepts underlying hierarchy: order, predictability, and pyramidal structure. Our definition applies to the so-called causal graphs, that is, connected, directed acyclic graphs in which the arrows depict a direct causal relation between two elements defining the nodes. The estimator of hierarchy is obtained by evaluating the complexity of causal paths against the uncertainty in recovering them from a given end point. This naturally leads us to a definition of mutual information which, properly normalized and weighted through the layered structure of the graph, results in suitable index of hierarchy with strong theoretical grounds.

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The following news item is taken in part from the December 13, 2010 issue of PNAS titled “There's Plenty of Time for Evolution,” by Herbert S. Wilf and Warren J. Ewens.

Objections to Darwinian evolution are often based on the time required to carry out the necessary mutations. Seemingly, exponential numbers of mutations are needed. We show that such estimates ignore the effects of natural selection, and that the numbers of necessary mutations are thereby reduced to about K log L, rather than KL, where L is the length of the genomic “word,” and K is the number of possible “letters” that can occupy any position in the word. The required theory makes contact with the theory of radix-exchange sorting in theoretical computer science and the asymptotic analysis of certain sums that occur there.

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The following news item is taken in part from the Online First articles of Theory in Biosciences titled “Mathematical Modeling of Evolution. Solved and Open Problems,” by Peter Schuster.

Evolution is a highly complex multilevel process and mathematical modeling of evolutionary phenomenon requires proper abstraction and radical reduction to essential features. Examples are natural selection, Mendel's laws of inheritance, optimization by mutation and selection, and neutral evolution. An attempt is made to describe the roots of evolutionary theory in mathematical terms.

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The following news item is taken in part from the December 10, 2010 issue of arXiv titled “Are biological systems poised at criticality?,” by Thierry Mora and William Bialek.

Many of life's most fascinating phenomena emerge from interactions among many elements—many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this “inverse” approach, using examples form families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised at a very special point in their parameter space—a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems.

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The following news item is taken in part from the January 14, 2011 issue of Science titled “Quantitative Analysis of Culture Using Millions of Digitized Books,” by Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K. Gray, The Google Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and Erez Lieberman Aiden.

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of “culturomics,” focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.

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The following news item is taken in part from the December 19, 2010 issue of arXiv titled “BioLogistics and the Struggle for Efficiency: Concepts and Perspectives,” by Dirk Helbing, Andreas Deutsch, Stefan Diez, Karsten Peters, Yannis Kalaidzidis, Kathrin Padberg, Stefan Lammer, Anders Johansson, Georg Breier, Frank Schulze, and Marino Zerial.

The growth of world population, limitation of resources, economic problems, and environmental issues force engineers to develop increasingly efficient solutions for logistic systems. Pure optimization for efficiency, however, has often led to technical solutions that are vulnerable to variations in supply and demand, and to perturbations. In contrast, nature already provides a large variety of efficient, flexible and robust logistic solutions. Can we utilize biological principles to design systems, which can flexibly adapt to hardly predictable, fluctuating conditions? We propose a bioinspired “BioLogistics” approach to deduce dynamic organization processes and principles of adaptive self-control from biological systems, and to transfer them to man-made logistics (including nanologistics), using principles of modularity, self-assembly, self-organization, and decentralized coordination. Conversely, logistic models can help revealing the logic of biological processes at the systems level.

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The following news item is taken in part from the February 1, 2010 issue of PNAS titled “Continuous-time Model of Structural Balance,” by Seth A. Marvel, Jon Kleinberg, Robert D. Kleinberg, and Steven H. Strogatz.

It is not uncommon for certain social networks to divide into two opposing camps in response to stress. This happens, for example, in networks of political parties during winner-takes-all elections, in networks of companies competing to establish technical standards, and in networks of nations faced with mounting threats of war. A simple model for these two-sided separations is the dynamical system dX/dt = X2, where X is a matrix of the friendliness or unfriendliness between pairs of nodes in the network.

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The following news item is taken in part from the January 10, 2011 issue of arXiv titled “Modular Random Boolean Networks,” by Rodrigo Poblanno-Balp and Carlos Gershenson.

Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with regular topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs are closer to criticality than regular RBNs.

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The following news item is taken in part from the January 19, 2011 issue of Nature titled “Systemic risk in banking ecosystems,” by Andrew G. Haldane and Robert M. May.

In the run-up to the recent financial crisis, an increasingly elaborate set of financial instruments emerged, intended to optimize returns to individual institutions with seemingly minimal risk. Essentially no attention was given to their possible effects on the stability of the system as a whole. Drawing analogies with the dynamics of ecological food webs and with networks within which infectious diseases spread, we explore the interplay between complexity and stability in deliberately simplified models of financial networks. We suggest some policy lessons that can be drawn from such models, with the explicit aim of minimizing systemic risk.

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The following news item is taken in part from the January 28, 2011 issue of Science titled “The Newest Synthesis: Understanding the Interplay of Evolutionary and Ecological Dynamics,” by Thomas W. Schoener.

The effect of ecological change on evolution has long been a focus of scientific research. The reverse—how evolutionary dynamics affect ecological traits—has only recently captured our attention, however, with the realization that evolution can occur over ecological time scales. This newly highlighted causal direction and the implied feedback loop—ecoevolutionary dynamics—is invigorating both ecologists and evolutionists and blurring the distinction between them.

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The following news item is taken in part from the December, 2010 issue of Trends in Ecology & Evolution titled “Swarm intelligence in plant roots,” by František Baluška, Simcha Lev-Yadun, and Stefano Mancuso.

Swarm intelligence occurs when two or more individuals independently, or at least partly independently, acquire information that is processed through social interactions and is used to solve a cognitive problem in a way that would be impossible for isolated individuals. We propose at least one example of swarm intelligence in plants: coordination of individual roots in complex root systems.

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The following news item is taken in part from the January 19, 2011 issue of Nature titled “Primitive agriculture in a social amoeba,” by Debra A. Brock, Tracy E. Douglas, David C. Queller, and Joan E. Strassmann.

Here, we show that the social amoeba Dictyostelium discoideum has a primitive farming symbiosis that includes dispersal and prudent harvesting of the crop. About one-third of wild-collected clones engage in husbandry of bacteria. Instead of consuming all bacteria in their patch, they stop feeding early and incorporate bacteria into their fruiting bodies. They then carry bacteria during spore dispersal and can seed a new food crop, which is a major advantage if edible bacteria are lacking at the new site.

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The following news item is taken in part from the January 21, 2011 issue of arXiv titled “Evolutionary Mechanics: New Engineering Principles for the Emergence of Flexibility in a Dynamic and Uncertain World,” by James M. Whitacre, Philipp Rohlfshagen, and Axel Bender.

Engineered systems are designed to deftly operate under predetermined conditions yet are notoriously fragile when unexpected perturbations arise. In contrast, biological systems operate in a highly flexible manner, learn quickly adequate responses to novel conditions, and evolve new routines/traits to remain competitive under persistent environmental change. A recent theory on the origins of biological flexibility has proposed that degeneracy—the existence of multifunctional components with partially overlapping functions—is a primary determinant of the robustness and adaptability found in evolved systems. While degeneracy's contribution to biological flexibility is well documented, there has been little investigation of degeneracy design principles for achieving flexibility in systems engineering.

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The following news item is taken in part from the January 25, 2011 issue of PNAS titled “Morphological Change in Machines Accelerates the Evolution of Robust Behavior,” by Josh Bongard.

Most animals exhibit significant neurological and morphological change throughout their lifetime. No robots to date, however, grow new morphological structure while behaving. This is due to technological limitations but also because it is unclear that morphological change provides a benefit to the acquisition of robust behavior in machines. Here, I show that in evolving populations of simulated robots, if robots grow from anguilliform into legged robots during their lifetime in the early stages of evolution, and the anguilliform body plan is gradually lost during later stages of evolution, gaits are evolved for the final, legged form of the robot more rapidly—and the evolved gaits are more robust—compared to evolving populations of legged robots that do not transition through the anguilliform body plan.

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The following news item is taken in part from the January, 2011 issue of Entropy titled “Complexity through Recombination: From Chemistry to Biology,” by Niles Lehman, Carolina Díaz Arenas, Wesley A. White, and Francis J. Schmidt.

Recombination is a common event in nature, with examples in physics, chemistry, and biology. This process is characterized by the spontaneous reorganization of structural units to form new entities. On reorganization, the complexity of the overall system can change. In particular, the components of the system can now experience a new response to externally applied selection criteria, such that the evolutionary trajectory of the system is altered. The link between chemical and biological forms of recombination is explored. The results underscore the importance of recombination in the origins of life on the Earth and its subsequent evolutionary divergence.

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The following news item is taken in part from the January 19, 2011 issue of Knowledge@Wharton, titled “Gross Domestic Happiness: What Is the Relationship between Money and Well-being?”

What exactly is the relationship between money and happiness? It is a difficult question to pin down, experts say. While more money may make us happier, other considerations—such as whether you live in an economically advanced country and how you think about your time—also play into the equation. An increasing number of economists, sociologists, and psychologists are now working in the field, and most agree that there is a strong link between a country's level of economic development and the happiness of its people.

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International Conference on Swarm Intelligence (ICSI 2011), Cergy, France, 2011/06/14-15

International Conference on Complex Systems (ICCS 2011), Boston, MA, 2011/06/26-07/01

GECCO 2011: Genetic and Evolutionary Computation Conference, Dublin, Ireland, 2011/07/12-16

IJCAI 2011, The 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, 2011/07/16-22

Third International Workshop on nonlinear Dynamics and Synchronization—INDS′11; Sixteenth International Symposium on Theoretical Electrical Engineering—ISTET′11, Klagenfurt am Wörthersee, Austria, 2011/07/25-27

ECAL 11: European Conference on Artificial Life, Paris, France, 2011/08/8-12

The 2011 International Conference on Adaptive & Intelligent Systems—ICAIS′11, Klagenfurt, Austria, 2011/09/06-08

European Conference on Complex Systems 2011, Vienna, Austria, 2011/09/12-16