Collaborations: The fourth age of research


  • Edited by Carlos Gershenson

The following news item is taken in part from the May 30, 2013 issue of Nature titled “Collaborations: The Fourth Age of Research,” by Jonathan Adams.

Research has progressed through three ages: the individual, the institutional, and the national. Nations competed to be at the cutting edge because this contributed to the wider economy through knowledge, new processes, and products.

Today, we are entering the fourth age of research, driven by international collaborations between elite research groups. This will challenge the ability of nations to conserve their scientific wealth either as intellectual property or as research talent. Tensions are growing between the knowledge a country needs to remain competitive and the assets it can exclusively secure and between the collaborative and domestic parts of the research base. Institutions that do not form international collaborations risk progressive disenfranchisement and countries that do not nurture their talent will lose out entirely.

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The following news item is taken in part from the May 2013 issue of arXiv titled “Early Warning Signals: The Charted and Uncharted Territories,” by Carl Boettiger, Noam Ross, and Alan Hastings.

The realization that complex systems such as ecological communities can collapse or shift regimes suddenly and without rapid external forcing poses a serious challenge to our understanding and management of the natural world. The potential to identify early warning signals that would allow researchers and managers to predict such events before they happen has therefore been an invaluable discovery that offers a way forward despite such seemingly unpredictable behavior. Research into early warning signals has demonstrated that it is possible to define and detect such early warning signals in advance of a transition in certain contexts. Here, we describe the pattern emerging as research continues to explore just how far we can generalize these results. A core of examples emerges that shares three properties: the phenomenon of rapid regime shifts, a pattern of “critical slowing down” that can be used to detect the approaching shift, and a mechanism of bifurcation driving the sudden change. As research has expanded beyond these core examples, it is becoming clear that not all systems that show regime shifts exhibit critical slowing down or vice versa. Even when systems exhibit critical slowing down, statistical detection is a challenge. We review the literature that explores these edge cases and highlights the need for (a) new early warning behaviors that can be used in cases where rapid shifts do not exhibit critical slowing down; (b) the development of methods to identify which behavior might be an appropriate signal when encountering a novel system, bearing in mind that a positive indication for some systems is a negative indication in others; and (c) statistical methods that can distinguish between signatures of early warning behaviors and noise.

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The following news item is taken in part from the May 15, 2013 issue of Scientific American titled “Big Data Needs a Big Theory to Go with It,” by Geoffrey West.

As the world becomes increasingly complex and interconnected, some of our biggest challenges have begun to seem intractable. What should we do about uncertainty in the financial markets? How can we predict energy supply and demand? How will climate change play out? How do we cope with rapid urbanization? Our traditional approaches to these problems are often qualitative and disjointed and lead to unintended consequences. To bring scientific rigor to the challenges of our time, we need to develop a deeper understanding of complexity itself.

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The following news item is taken in part from the May 24, 2013 issue of Science titled “Culture, Genes, and the Human Revolution,” by Simon E. Fisher and Matt Ridley.

State-of-the-art DNA sequencing is providing ever more detailed insights into the genomes of humans, extant apes, and even extinct hominins, offering unprecedented opportunities to uncover the molecular variants that make us human. A common assumption is that the emergence of behaviorally modern humans after 200,000 years ago required—and followed—a specific biological change triggered by one or more genetic mutations. For example, [it has been] argued that the dawn of human culture stemmed from a single genetic change that “fostered the uniquely modern ability to adapt to a remarkable range of natural and social circumstance.” However, are evolutionary changes in our genome a cause or a consequence of cultural innovation?

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The following news item is taken in part from the May 2013 issue of arXiv titled “Community Detection and Graph Partitioning,” by M. E. J. Newman.

Many methods have been proposed for community detection in networks. Some of the most promising are methods based on statistical inference, which rest on solid mathematical foundations and return excellent results in practice. In this article, we show that two of the most widely used inference methods can be mapped directly onto versions of the standard minimum-cut graph partitioning problem, which allows us to apply any of the many well-understood partitioning algorithms to the solution of community detection problems. We illustrate the approach by adapting the Laplacian spectral partitioning method to perform community inference, testing the resulting algorithm on a range of examples, including computer-generated and real-world networks. Both the quality of the results and the running time rival the best previous methods.

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The following news item is taken in part from the June 2013 issue of Behavioral and Brain Sciences titled “Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science,” by Andy Clark.

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top–down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this “hierarchical prediction machine” approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action.

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The following news item is taken in part from the May 14, 2013 issue of Physical Review Letters titled “Controllability Transition and Nonlocality in Network Control,” by Jie Sun and Adilson E. Motter.

A common goal in the control of a large network is to minimize the number of driver nodes or control inputs. Yet, the physical determination of control signals and the properties of the resulting control trajectories remain widely underexplored. Here, we show that (i) numerical control fails in practice even for linear systems if the controllability Gramian is ill conditioned, which occurs frequently even when existing controllability criteria are satisfied unambiguously; (ii) the control trajectories are generally nonlocal in the phase space, and their lengths are strongly anticorrelated with the numerical success rate and number of control inputs; and (iii) numerical success rate increases abruptly from zero to nearly one as the number of control inputs is increased, a transformation we term numerical controllability transition. This reveals a trade-off between nonlocality of the control trajectory in the phase space and nonlocality of the control inputs in the network itself. The failure of numerical control cannot be overcome in general by merely increasing numerical precision—successful control requires instead increasing the number of control inputs beyond the numerical controllability transition.

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The following news item is taken in part from the May 2013 issue of arXiv titled “Exploiting Ecological Principles to Better Understand Cancer Progression and Treatment,” by David Basanta and Alexander R. A. Anderson.

A small but growing number of people are finding interesting parallels between ecosystems as studied by ecologists (think of a Savanna, the Amazon rain forest or a Coral reef) and tumors. The idea of viewing cancer from an ecological perspective has many implications, but fundamentally, it means that we should not see cancer just as a group of mutated cells. A more useful definition of cancer is to consider it a disruption in the complex balance of many interacting cellular and microenvironmental elements in a specific organ. This perspective means that organs undergoing carcinogenesis should be seen as sophisticated ecosystems in homeostasis that cancer cells can disrupt. It also makes cancer seem even more complex but may ultimately provide insights that make it more treatable. Here, we discuss how ecological principles can be used to better understand cancer progression and treatment, using several mathematical and computational models to illustrate our argument.

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The following news item is taken in part from the May 16, 2013 issue of PLoS Computational Biology titled “The Emergence of Environmental Homeostasis in Complex Ecosystems,” by James G. Dyke and Iain S. Weaver.

Life on Earth is perhaps greater than three and a half billion years old and it would appear that once it started it never stopped. During this period, a number of dramatic shocks and drivers have affected the Earth. These include the impacts of massive asteroids, runaway climate change, and increases in brightness of the Sun. Has life on Earth simply been lucky in withstanding such perturbations? Are there any self-regulating or homeostatic processes operating in the Earth system that would reduce the severity of such perturbations? If such planetary processes exist, to what extent are they the result of the actions of life? In this study, we show how the regulation of environmental conditions can emerge as a consequence of life's effects. If life is both affected by and affects it environment, then this coupled system can self-organize into a robust control system that was first described during the early cybernetics movement around the middle of the 20th century. Our findings are in principle applicable to a wide range of real-world systems (…).

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The following news item is taken in part from the May 3, 2013 issue of The Knowledge Engineering Review titled “A Review on Agent-Based Technology for Traffic and Transportation,” by Ana L. C. Bazzan and Franziska Klügl.

In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In this article, we review the literature related to the areas of agent-based traffic modeling and simulation and agent-based traffic control and management. Later, we discuss and summarize the main achievements and the challenges.

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The following news item is taken in part from the 2013/05 issue of Physics Today titled “Chaos at fifty,” by Adilson E. Motter and David K. Campbell.

Starting in the 19th century (…) and culminating with a 1963 paper by MIT meteorologist Edward Lorenz (…), a series of developments revealed that the notion of deterministic predictability, although appealingly intuitive, is in practice false for most systems. Small uncertainties in an initial state can indeed become large errors in a final one. Even simple systems for which all forces are known can behave unpredictably. Determinism, surprisingly enough, does not preclude chaos.

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The following news item is taken in part from the May 2, 2013 issue of Nature titled “Globally Networked Risks and How to Respond,” by Dirk Helbing.

Today's strongly connected, global networks have produced highly interdependent systems that we do not understand and cannot control well. These systems are vulnerable to failure at all scales, posing serious threats to society, even when external shocks are absent. As the complexity and interaction strengths in our networked world increase, man-made systems can become unstable, creating uncontrollable situations even when decision makers are well skilled, have all data and technology at their disposal, and do their best. To make these systems manageable, a fundamental redesign is needed. A “Global Systems Science” might create the required knowledge and paradigm shift in thinking.

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The following news item is taken in part from the April 2013 issue of arXiv titled “Networks in Cognitive Science,” by Andrea Baronchelli, Ramon Ferrer-i-Cancho, Romualdo Pastor-Satorras, Nick Chater, and Morten H. Christiansen.

Networks of interconnected nodes have long played a key role in cognitive science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, or collaborations among scientists. Today, the inclusion of network theory into cognitive sciences and the expansion of complex systems science promise to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this article, we review recent contributions of network theory at different levels and domains within the cognitive sciences.

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The following news item is taken in part from the April 2013 issue of Scientific Reports titled “Quantifying Trading Behavior in Financial Markets Using Google Trends,” by Tobias Preis, Helen Susannah Moat, and H. Eugene Stanley.

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behaviors that have led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.

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The following news item is taken in part from the 2013 issue of Biological Journal of the Linnean Society titled “Between Holism and Reductionism: A Philosophical Primer on Emergence,” by Massimo Pigliucci.

Ever since Darwin, a great deal of the conceptual history of biology may be read as a struggle between two philosophical positions: reductionism and holism. On one hand, we have the reductionist claim that evolution has to be understood in terms of changes at the fundamental causal level of the gene. As Richard Dawkins famously put it, organisms are just “lumbering robots” in the service of their genetic masters. On the other hand, there is a long holistic tradition that focuses on the complexity of developmental systems, on the nonlinearity of gene–environment interactions, and on multilevel selective processes to argue that the full story of biology is a bit more complicated than that. Reductionism can marshal on its behalf the spectacular successes of genetics and molecular biology throughout the 20th and 21st centuries. Holism has built on the development of entirely new disciplines and conceptual frameworks over the past few decades, including evo-devo and phenotypic plasticity. Yet, a number of biologists are still actively looking for a way out of the reductionism–holism counterposition, often mentioning the word “emergence” as a way to deal with the conundrum. This article briefly examines the philosophical history of the concept of emergence, distinguishes between epistemic and ontological accounts of it, and comments on conceptions of emergence that can actually be useful for practicing evolutionary biologists.

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The following news item is taken in part from the May 19, 2013 issue of Physical Review Letters titled “Causal Entropic Forces,” by A. D. Wissner-Gross and C. E. Freer.

Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.

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The following news item is taken in part from the May 2, 2013 issue of PLoS One titled “Employment Growth through Labor Flow Networks,” by Omar A. Guerrero and Robert L. Axtell.

It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here, we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment microdata, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm–worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high-performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.

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