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Is Science Mostly Driven by Ideas or by Tools?

We are standing now as we stood in the 1950s, between a Kuhnian dream of sudden illumination and a Galisonian reality of laborious exploring. On one side are string theory and speculations about multiverses; on the other are all-sky surveys and observations of real black holes. The balance today is more even than it was in the 1950s. String theory is a far more promising venture than Einstein's unified field theory. Kuhn and Galison are running neck and neck in the race for glory. We are lucky to live in a time when both are going strong.

Is Science Mostly Driven by Ideas or by Tools?
Freeman J. Dyson
Science 14 December 2012:
Vol. 338 no. 6113 pp. 1426-1427
http://dx.doi.org/10.1126/science.1232773
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Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development

Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development | Papers | Scoop.it

We investigate the relation between the development of reactive and cognitive capabilities. In particular we investigate whether the development of reactive capabilities prevents or promotes the development of cognitive capabilities in a population of evolving robots that have to solve a time-delay navigation task in a double T-Maze environment. Analysis of the experiments reveals that the evolving robots always select reactive strategies that rely on cognitive offloading, i.e., the possibility of acting so as to encode onto the relation between the agent and the environment the states that can be used later to regulate the agent’s behavior. The discovery of these strategies does not prevent, but rather facilitates, the development of cognitive strategies that also rely on the extraction and use of internal states. Detailed analysis of the results obtained in the different experimental conditions provides evidence that helps clarify why, contrary to expectations, reactive and cognitive strategies tend to have synergetic relationships.

 

Carvalho JT, Nolfi S (2016) Cognitive Offloading Does Not Prevent but Rather Promotes Cognitive Development. PLoS ONE 11(8): e0160679. doi:10.1371/journal.pone.0160679

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Big data analyses reveal patterns and drivers of the movements of southern elephant seals

Big data analyses reveal patterns and drivers of the movements of southern elephant seals | Papers | Scoop.it

The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with “big data”, that require no ‘a priori’ assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for “big data” techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.

 

Big data analyses reveal patterns and drivers of the movements of southern elephant seals
Jorge P. Rodríguez, Juan Fernández-Gracia, Michele Thums, Mark A. Hindell, Ana M. M. Sequeira, Mark G. Meekan, Daniel P. Costa, Christophe Guinet, Robert G. Harcourt, Clive R. McMahon, Monica Muelbert, Carlos M. Duarte & Víctor M. Eguíluz
Scientific Reports 7, Article number: 112 (2017)
doi:10.1038/s41598-017-00165-0

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Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems

Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an equivalent isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) in which the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, widely regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.

 

Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems

Alyssa M Adams, Hector Zenil, Paul CW Davies, Sara I Walker

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Zipf's law, unbounded complexity and open-ended evolution

A major problem for evolutionary theory is understanding the so called {\em open-ended} nature of evolutionary change. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterise evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces. Several theoretical and computational approaches have been developed to properly characterise OEE. Interestingly, many complex systems displaying OEE, from language to proteins, share a common statistical property: the presence of Zipf's law. Given and inventory of basic items required to build more complex structures Zipf's law tells us that most of these elements are rare whereas a few of them are extremely common. Using Algorithmic Information Theory, in this paper we provide a fundamental definition for open-endedness, which can be understood as {\em postulates}. Its statistical counterpart, based on standard Shannon Information theory, has the structure of a variational problem which is shown to lead to Zipf's law as the expected consequence of an evolutionary processes displaying OEE. We further explore the problem of information conservation through an OEE process and we conclude that statistical information (standard Shannon information) is not conserved, resulting into the paradoxical situation in which the increase of information content has the effect of erasing itself. We prove that this paradox is solved if we consider non-statistical forms of information. This last result implies that standard information theory may not be a suitable theoretical framework to explore the persistence and increase of the information content in OEE systems.

 

Zipf's law, unbounded complexity and open-ended evolution

Bernat Corominas-Murtra, Luís Seoane, Ricard Solé

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Design of the Artificial: lessons from the biological roots of general intelligence

Our desire and fascination with intelligent machines dates back to the antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought (syllogism) and Heron of Alexandria's mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.

 

Design of the Artificial: lessons from the biological roots of general intelligence
Nima Dehghani

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The many facets of community detection in complex networks

Community detection, the decomposition of a graph into essential 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 assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these 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. SchaubEmail author, Jean-Charles Delvenne, Martin Rosvall and Renaud Lambiotte
Applied Network Science20172:4
DOI: 10.1007/s41109-017-0023-6

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How Brain Scientists Forgot That Brains Have Owners

How Brain Scientists Forgot That Brains Have Owners | Papers | Scoop.it
Five neuroscientists argue that fancy new technologies have led the field astray.
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Adaptive Local Information Transfer in Random Boolean Networks

Living systems such as gene regulatory networks and neuronal networks have been supposed to work close to dynamical criticality, where their information-processing ability is optimal at the whole-system level. We investigate how this global information-processing optimality is related to the local information transfer at each individual-unit level. In particular, we introduce an internal adjustment process of the local information transfer and examine whether the former can emerge from the latter. We propose an adaptive random Boolean network model in which each unit rewires its incoming arcs from other units to balance stability of its information processing based on the measurement of the local information transfer pattern. First, we show numerically that random Boolean networks can self-organize toward near dynamical criticality in our model. Second, the proposed model is analyzed by a mean-field theory. We recognize that the rewiring rule has a bootstrapping feature. The stationary indegree distribution is calculated semi-analytically and is shown to be close to dynamical criticality in a broad range of model parameter values.

 

Adaptive Local Information Transfer in Random Boolean Networks

Taichi Haruna

Artificial Life

Winter 2017, Vol. 23, No. 1, Pages: 105-118
Posted Online February 27, 2017.
(doi:10.1162/ARTL_a_00224)

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Complexity of lakes in a latitudinal gradient

•The usefulness of quantitative indicators of ecological complexity is evaluated.
•Chaos should not be confused with complexity.
•Light and temperature cause different ranges of complexity in the gradient.
•Homoeostasis variation is related to the seasonal changes and transitions.
•Autopoiesis reveals groups with higher and lower degree of autonomy.

 

Complexity of lakes in a latitudinal gradient

Nelson Fernández, José Aguilar, C.A. Piña-García, Carlos Gershenson

Ecological Complexity
Volume 31, September 2017, Pages 1–20

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The need for a translational science of democracy

The bitterly factious 2016 U.S. presidential election campaign was the culmination of several trends that, taken together, constitute a syndrome of chronic ailments in the body politic. Ironically, these destructive trends have accelerated just as science has rapidly improved our understanding of them and their underlying causes. But mere understanding is not sufficient to repair our politics. The challenge is to build a translational science of democracy that maintains scientific rigor while actively promoting the health of the body politic.

 

Michael A. Neblo, William Minozzi, Kevin M. Esterling, Jon Green, Jonathon Kingzette, David M. J. Lazer

Science  03 Mar 2017:
Vol. 355, Issue 6328, pp. 914-915
DOI: 10.1126/science.aal3900

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Disease Localization in Multilayer Networks

Disease Localization in Multilayer Networks | Papers | Scoop.it

We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes.

 

Disease Localization in Multilayer Networks
Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, and Yamir Moreno
Phys. Rev. X 7, 011014

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The role of the interaction network in the emergence of diversity of behavior

How can systems in which individuals’ inner workings are very similar to each other, as neural networks or ant colonies, produce so many qualitatively different behaviors, giving rise to roles and specialization? In this work, we bring new perspectives to this question by focusing on the underlying network that defines how individuals in these systems interact. We applied a genetic algorithm to optimize rules and connections of cellular automata in order to solve the density classification task, a classical problem used to study emergent behaviors in decentralized computational systems. The networks used were all generated by the introduction of shortcuts in an originally regular topology, following the small-world model. Even though all cells follow the exact same rules, we observed the existence of different classes of cells’ behaviors in the best cellular automata found—most cells were responsible for memory and others for integration of information. Through the analysis of structural measures and patterns of connections (motifs) in successful cellular automata, we observed that the distribution of shortcuts between distant regions and the speed in which a cell can gather information from different parts of the system seem to be the main factors for the specialization we observed, demonstrating how heterogeneity in a network can create heterogeneity of behavior.

 

Godoy A, Tabacof P, Von Zuben FJ (2017) The role of the interaction network in the emergence of diversity of behavior. PLoS ONE 12(2): e0172073. doi:10.1371/journal.pone.0172073

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Pathways towards instability in financial networks

Pathways towards instability in financial networks | Papers | Scoop.it

Following the financial crisis of 2007–2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the details of how financial institutions interact typically play a decisive role, and a general understanding of precisely how network topology creates instability remains lacking. Here we show how processes that are widely believed to stabilize the financial system, that is, market integration and diversification, can actually drive it towards instability, as they contribute to create cyclical structures which tend to amplify financial distress, thereby undermining systemic stability and making large crises more likely. This result holds irrespective of the details of how institutions interact, showing that policy-relevant analysis of the factors affecting financial stability can be carried out while abstracting away from such details.

 

Pathways towards instability in financial networks
Marco Bardoscia, Stefano Battiston, Fabio Caccioli & Guido Caldarelli
Nature Communications 8, Article number: 14416 (2017)
doi:10.1038/ncomms14416

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Building a Science of Experience: Neurophenomenology and Related Disciplines

Building a Science of Experience: Neurophenomenology and Related Disciplines | Papers | Scoop.it

Context: More than 20 years ago Varela initiated a research program to advance in the scientific study of consciousness, neurophenomenology. Problem: Has Varela’s neurophenomenology, the solution to the “hard problem,” been successful? Which issues remain unresolved, and why? Method: This introduction sketches the progress that has been made since then and links it to the contributions to this special issue. Results: Instead of a unified research field, today we find a variety of different interpretations and implementations of neurophenomenology. We argue that neurophenomenology needs to give additional attention to its experiential dimension by addressing first-person methods’ specific challenges and by rethinking the relationship between the frameworks of the first- and third-person approaches.

 

Valenzuela-Moguillansky C., Vásquez-Rosati A. & Riegler A. (2017) Building a Science of Experience: Neurophenomenology and Related Disciplines. Constructivist Foundations 12(2): 131–138. Available at http://constructivist.info/12/2/131.editorial

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Quantifying Synergistic Information Using Intermediate Stochastic Variables

Quantifying Synergistic Information Using Intermediate Stochastic Variables | Papers | Scoop.it

Quantifying synergy among stochastic variables is an important open problem in information theory. Information synergy occurs when multiple sources together predict an outcome variable better than the sum of single-source predictions. It is an essential phenomenon in biology such as in neuronal networks and cellular regulatory processes, where different information flows integrate to produce a single response, but also in social cooperation processes as well as in statistical inference tasks in machine learning. Here we propose a metric of synergistic entropy and synergistic information from first principles. The proposed measure relies on so-called synergistic random variables (SRVs) which are constructed to have zero mutual information about individual source variables but non-zero mutual information about the complete set of source variables. We prove several basic and desired properties of our measure, including bounds and additivity properties. In addition, we prove several important consequences of our measure, including the fact that different types of synergistic information may co-exist between the same sets of variables. A numerical implementation is provided, which we use to demonstrate that synergy is associated with resilience to noise. Our measure may be a marked step forward in the study of multivariate information theory and its numerous applications

 

Quantifying Synergistic Information Using Intermediate Stochastic Variables
Rick Quax, Omri Har-Shemesh and Peter M. A. Sloot

Entropy 2017, 19(2), 85; doi:10.3390/e19020085

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What Is Morphological Computation? On How the Body Contributes to Cognition and Control

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “offloading computation from the brain to the body,” where the body is said to perform “morphological computation.” Our investigation of four characteristic cases of morphological computation in animals and robots shows that the “offloading” perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as reservoir computing, where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control—how it contributes to the overall orchestration of intelligent behavior.

 

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

Vincent C. Müller, Matej Hoffmann

Artificial Life

Winter 2017, Vol. 23, No. 1, Pages: 1-24
Posted Online February 27, 2017.
(doi:10.1162/ARTL_a_00219)

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Primordial Sex Facilitates the Emergence of Evolution

Compartments are ubiquitous throughout biology, yet their importance stretches back to the origin of cells. In the context of origin of life, we assume that a protocell, a compartment enclosing functional components, requires N components to be evolvable. We take interest in the timescale in which a minimal evolvable protocell is produced. We show that when protocells fuse and share information, the time to produce an evolvable protocell scales algebraically in N, in contrast to an exponential scaling in the absence of fusion. We discuss the implications of this result for origins of life, as well as other biological processes.

 

Primordial Sex Facilitates the Emergence of Evolution
Sam Sinai, Jason Olejarz, Iulia A. Neagu, Martin A. Nowak

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Sequence of purchases in credit card data reveal life styles in urban populations

From our most basic consumption to secondary needs, our spending habits reflect our life styles. Yet, in computational social sciences there is an open question about the existence of ubiquitous trends in spending habits by various groups at urban scale. Limited information collected by expenditure surveys have not proven conclusive in this regard. This is because, the frequency of purchases by type is highly uneven and follows a Zipf-like distribution. In this work, we apply text compression techniques to the purchase codes of credit card data to detect the significant sequences of transactions of each user. Five groups of consumers emerge when grouped by their similarity based on these sequences. Remarkably, individuals in each consumer group are also similar in age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, we find that it can give us insights on collective behavior.

 

Sequence of purchases in credit card data reveal life styles in urban populations
Riccardo Di Clemente, Miguel Luengo-Oroz, Matias Travizano, Bapu Vaitla, Marta C. Gonzalez

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Collective Learning in China's Regional Economic Development

Industrial development is the process by which economies learn how to produce new products and services. But how do economies learn? And who do they learn from? The literature on economic geography and economic development has emphasized two learning channels: inter-industry learning, which involves learning from related industries; and inter-regional learning, which involves learning from neighboring regions. Here we use 25 years of data describing the evolution of China's economy between 1990 and 2015--a period when China multiplied its GDP per capita by a factor of ten--to explore how Chinese provinces diversified their economies. First, we show that the probability that a province will develop a new industry increases with the number of related industries that are already present in that province, a fact that is suggestive of inter-industry learning. Also, we show that the probability that a province will develop an industry increases with the number of neighboring provinces that are developed in that industry, a fact suggestive of inter-regional learning. Moreover, we find that the combination of these two channels exhibit diminishing returns, meaning that the contribution of either of these learning channels is redundant when the other one is present. Finally, we address endogeneity concerns by using the introduction of high-speed rail as an instrument to isolate the effects of inter-regional learning. Our differences-in-differences (DID) analysis reveals that the introduction of high speed-rail increased the industrial similarity of pairs of provinces connected by high-speed rail. Also, industries in provinces that were connected by rail increased their productivity when they were connected by rail to other provinces where that industry was already present. These findings suggest that inter-regional and inter-industry learning played a role in China's great economic expansion.

 

Collective Learning in China's Regional Economic Development
Jian Gao, Bogang Jun, Alex "Sandy" Pentland, Tao Zhou, Cesar A. Hidalgo

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Can A Robot Have Free Will?

Using insights from cybernetics and an information-based understanding of biological systems, a precise, scientifically inspired, definition of free-will is offered and the essential requirements for an agent to possess it in principle are set out. These are: a) there must be a self to self-determine; b) there must be a non-zero probability of more than one option being enacted; c) there must be an internal means of choosing among options (which is not merely random, since randomness is not a choice). For (a) to be fulfilled, the agent of self-determination must be organisationally closed (a `Kantian whole'). For (c) to be fulfilled: d) options must be generated from an internal model of the self which can calculate future states contingent on possible responses; e) choosing among these options requires their evaluation using an internally generated goal defined on an objective function representing the overall `master function' of the agent and f) for `deep free-will', at least two nested levels of choice and goal (d-e) must be enacted by the agent. The agent must also be able to enact its choice in physical reality. The only systems known to meet all these criteria are living organisms, not just humans, but a wide range of organisms. The main impediment to free-will in present-day artificial robots, is their lack of being a Kantian whole. Consciousness does not seem to be a requirement and the minimum complexity for a free-will system may be quite low and include relatively simple life-forms that are at least able to learn.

 

Farnsworth, K. Can A Robot Have Free Will?. Preprints 2017, 2017020105 (doi: 10.20944/preprints201702.0105.v1).

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Analyzing the coevolution of interorganizational networks and organizational performance: Automakers’ production networks in Japan

Organizations create networks with one another, and these networks may in turn shape the organizations involved. Until recently, such complex dynamic processes could not be rigorously empirically analyzed because of a lack of suitable modeling and validation methods. Using stochastic actor-oriented models and unique longitudinal survey data on the changing structure of interfirm production networks in the automotive industry in Japan, this paper illustrates how to quantitatively assess and validate (1) the dynamic micro-mechanism by which organizations form their networks and (2) the role of the dynamic network structures in organizational performance. The applied model helps to explain the endogenous processes behind the recent diversification of Japanese automobile production networks. Specifically, testing the effects of network topology and network diffusion on organizational performance, the novel modeling framework enables us to discern that the restructuring of interorganizational networks led to the increase of Japanese automakers’ production per employee, and not the reverse. Traditional models that do not allow for interaction between interorganizational structure and organizational agency misrepresent this mechanism.

 

Analyzing the coevolution of interorganizational networks and organizational performance: Automakers’ production networks in Japan

Matous, P. & Todo, Y. Appl Netw Sci (2017) 2: 5. doi:10.1007/s41109-017-0024-5

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Human migration

Human migration | Papers | Scoop.it
In today’s polarized political environment, public discussion about refugees and migrants has become heated and muddled. Nature examines the facts around migration and the increasing use of technology to monitor people’s mobility. And we talk to scientists about their experiences and concerns when moving between, and living and working in, other countries.
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The mobility of elite life scientists: Professional and personal determinants

•We use a dataset of 10,051 elite life scientists to study the predictors of mobility.
•Scientists with more publications and NIH funding are more likely to move.
•Recent NIH funding is associated with a lower likelihood of moving.
•The quality of the peer environment is an important influencer of mobility.
•Scientists, especially mothers, are less likely to move when children are adolescent.

 

The mobility of elite life scientists: Professional and personal determinants

Pierre Azoulay, Ina Ganguli, Joshua Graff Zivin

Research Policy
Volume 46, Issue 3, April 2017, Pages 573–590

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Global Patterns of Synchronization in Human Communications

Global Patterns of Synchronization in Human Communications | Papers | Scoop.it

Social media are transforming global communication and coordination and provide unprecedented opportunities for studying socio-technical domains. Here we study global dynamical patterns of communication on Twitter across many scales. Underlying the observed patterns is both the diurnal rotation of the earth, day and night, and the synchrony required for contingency of actions between individuals. We find that urban areas show a cyclic contraction and expansion that resembles heartbeats linked to social rather than natural cycles. Different urban areas have characteristic signatures of daily collective activities. We show that the differences detected are consistent with a new emergent global synchrony that couples behavior in distant regions across the world. Although local synchrony is the major force that shapes the collective behavior in cities, a larger-scale synchronization is beginning to occur.

 

Alfredo J. Morales, Vaibhav Vavilala, Rosa M. Benito, Yaneer Bar-Yam, Global patterns of synchronization in human communications, Journal of the Royal Society (2017).

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Will Democracy Survive Big Data and Artificial Intelligence?

Will Democracy Survive Big Data and Artificial Intelligence? | Papers | Scoop.it

We are at the historic moment, where we have to decide on the right path—a path that allows us all to benefit from the digital revolution. Therefore, we urge to adhere to the following fundamental principles:

1. to increasingly decentralize the function of information systems;

2. to support informational self-determination and participation;

3. to improve transparency in order to achieve greater trust;

4. to reduce the distortion and pollution of information;

5. to enable user-controlled information filters;

6. to support social and economic diversity;

7. to improve interoperability and collaborative opportunities;

8. to create digital assistants and coordination tools;

9. to support collective intelligence, and

10. to promote responsible behavior of citizens in the digital world through digital literacy and enlightenment.

 

Will Democracy Survive Big Data and Artificial Intelligence?
By Dirk Helbing, Bruno S. Frey, Gerd Gigerenzer, Ernst Hafen, Michael Hagner, Yvonne Hofstetter, Jeroen van den Hoven, Roberto V. Zicari, Andrej Zwitter on February 25, 2017

Scientific American

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