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Amazing bead chain experiment in slow motion - Slo Mo #19 - Earth Unplugged

These beads seem to levitate, defy gravity and jump out of the beaker. But how and why do they act like this? We met up with Steve Mould, the science guy from Britain's Brightest, to explore the science behind the "self siphoning beads" - also known as "Newton's Beads".

To get a closer look at the phenomenon, we filmed them in slow motion to try to work out what exactly was happening, and how the behaviour changes with height.

 

 

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Dmitry Alexeev's curator insight, July 28, 2013 7:07 AM

not that much of biology - so far (if we don't think of dna) 

however the slow motion is amazing

Complexity - Complex Systems Theory
Complex systems present problems both in mathematical modelling and philosophical foundations. The study of complex systems represents a new approach to science that investigates how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. The equations from which models of complex systems are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of natural systems that are considered fundamentally complex. wikipedia (en)
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[1407.2425] Spatial maximum entropy modeling from presence/absence tropical forest data

Understanding the assembly of ecosystems to estimate the number of species at different spatial scales is a challenging problem. Until now, maximum entropy approaches have lacked the important feature of considering space in an explicit manner. We propose a spatially explicit maximum entropy model suitable to describe spatial patterns such as the species area relationship and the endemic area relationship. Starting from the minimal information extracted from presence/absence data, we compare the behavior of two models considering the occurrence or lack thereof of each species and information on spatial correlations. Our approach uses the information at shorter spatial scales to infer the spatial organization at larger ones. We also hypothesize a possible ecological interpretation of the effective interaction we use to characterize spatial clustering.

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The scaling of human interactions with city size

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.

 

Markus Schläpfer, Luís M. A. Bettencourt, Sébastian Grauwin, Mathias Raschke, Rob Claxton, Zbigniew Smoreda, Geoffrey B. West, and Carlo Ratti
The scaling of human interactions with city size
J. R. Soc. Interface. 2014 11 20130789; http://dx.doi.org/10.1098/rsif.2013.0789

 

on ArXiv: http://arxiv.org/abs/1210.5215


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on ArXiv: http://arxiv.org/abs/1210.5215

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Large-scale Fluctuations of Lyapunov Exponents in Diffusive Systems

We present a general formalism for computing Lyapunov exponents and their fluctuations in spatially extended systems described by diffusive fluctuating hydrodynamics, thus extending the concepts of dynamical system theory to a broad range of non-equilibrium systems. Our analytical results compare favorably with simulations of a lattice model of heat conduction. We further show how the computation of Lyapunov exponents for the Symmetric Simple Exclusion Process relates to damage spreading and to a two-species pair annihilation process, for which our formalism yields new finite size results.

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Combining segregation and integration: Schelling model dynamics for heterogeneous population

The Schelling model is a simple agent based model that demonstrates how individuals' relocation decisions generate residential segregation in cities. Agents belong to one of two groups and occupy cells of rectangular space. Agents react to the fraction of agents of their own group within the neighborhood around their cell. Agents stay put when this fraction is above a given tolerance threshold but seek a new location if the fraction is below the threshold. The model is well known for its tipping point behavior: an initial random (integrated) pattern remains integrated when the tolerance threshold is below 1/3 but becomes segregated when the tolerance threshold is above 1/3.
In this paper, we demonstrate that the variety of the Schelling model steady patterns is richer than the segregation-integration dichotomy and contains patterns that consist of segregated patches for each of the two groups alongside patches where both groups are spatially integrated. We obtain such patterns by considering a general version of the model in which the mechanisms of agents' interactions remain the same but the tolerance threshold varies between agents of both groups.
We show that the model produces patterns of mixed integration and segregation when the tolerance threshold of most agents is either below the tipping point or above 2/3. In these cases, the mixed patterns are relatively insensitive to the model's parameters.

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Game Theory Makes New Predictions for Evolution | Simons Foundation

Game Theory Makes New Predictions for Evolution | Simons Foundation | Complexity - Complex Systems Theory | Scoop.it
An insight borrowed from computer science suggests that evolution values both fitness and diversity.

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The direction of evolution: The rise of cooperative organization

The direction of evolution: The rise of cooperative organization | Complexity - Complex Systems Theory | Scoop.it

Two great trends are evident in the evolution of life on Earth: towards increasing diversification and towards increasing integration. Diversification has spread living processes across the planet, progressively increasing the range of environments and free energy sources exploited by life. Integration has proceeded through a stepwise process in which living entities at one level are integrated into cooperative groups that become larger-scale entities at the next level, and so on, producing cooperative organizations of increasing scale (for example, cooperative groups of simple cells gave rise to the more complex eukaryote cells, groups of these gave rise to multi-cellular organisms, and cooperative groups of these organisms produced animal societies). The trend towards increasing integration has continued during human evolution with the progressive increase in the scale of human groups and societies. The trends towards increasing diversification and integration are both driven by selection. An understanding of the trajectory and causal drivers of the trends suggests that they are likely to culminate in the emergence of a global entity. This entity would emerge from the integration of the living processes, matter, energy and technology of the planet into a global cooperative organization. Such an integration of the results of previous diversifications would enable the global entity to exploit the widest possible range of resources across the varied circumstances of the planet. This paper demonstrates that it's case for directionality meets the tests and criticisms that have proven fatal to previous claims for directionality in evolution.


The direction of evolution: The rise of cooperative organization
John E. Stewart

Biosystems
Available online 1 June 2014

http://dx.doi.org/10.1016/j.biosystems.2014.05.006


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Eli Levine's curator insight, June 15, 10:06 PM

Cooperation is the best way to improve, sustain, maintain, and repair.  Competition is what drives everyone and everything towards something different, be it competition for resources or competition against the elements around us.

 

I don't get what the point of competition amongst the species is for.  Part of cooperation, after all, is knowing what works, learning about what could work better or doesn't work, and then letting the negative or sub-optimal slip back beneath the waves of ignorance, such that the new ways can rise to prominence.

 

Change is the only constant in this universe of universes.

 

Yet cooperation, I think, yields the higher and stronger of the universal structures that are out there, even if it means that there are still losers and winners.  The only difference is the level of consent and consensus that's reached within the social, ecological, economical, and/or political landscape.  One way works towards what is best.  The other way simply yields what is best at competing, which is not the same as being the actual best solution to a given problem or condition.

 

Think about it.

Luciano Lampi's curator insight, June 16, 9:51 AM

is this the end of stove pipes?

Ra's curator insight, June 22, 6:02 AM

Have I been reading too much science fiction?

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NSF Research Helps UNESCO Preserve Subaks in Bali

NSF Research Helps UNESCO Preserve Subaks in Bali | Complexity - Complex Systems Theory | Scoop.it
Immersed in the world of Balinese water temples and cooperative farms, Anthropologist J. Stephen Lansing’s NSF funded research helped win UNESCO World Heritage Site status for Bali’s subaks.
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Designing Complex Dynamics in Cellular Automata with Memory

Since their inception at Macy conferences in later 1940s complex systems remain the most controversial topic of inter-disciplinary sciences. The term `complex system' is the most vague and liberally used scientific term. Using elementary cellular automata (ECA), and exploiting the CA classification, we demonstrate elusiveness of `complexity' by shifting space-time dynamics of the automata from simple to complex by enriching cells with {\it memory}. This way, we can transform any ECA class to another ECA class --- without changing skeleton of cell-state transition function --- and vice versa by just selecting a right kind of memory. A systematic analysis display that memory helps `discover' hidden information and behaviour on trivial --- uniform, periodic, and non-trivial --- chaotic, complex --- dynamical systems.

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ECCS 2014 Satellite

ECCS 2014 Satellite | Complexity - Complex Systems Theory | Scoop.it

Workshop on Robustness, Adaptability and Critical Transitions in Living Systems. Submit your Abstract at http://seis.bristol.ac.uk/~fs13378/eccs_2014_livingsys.html

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Networks as a Privileged Way to Develop Mesoscopic Level Approaches in Systems Biology

The methodologies advocated in computational biology are in many cases proper system-level approaches. These methodologies are variously connected to the notion of “mesosystem” and thus on the focus on relational structures that are at the basis of biological regulation. Here, I describe how the formalization of biological systems by means of graph theory constitutes an extremely fruitful approach to biology. I suggest the epistemological relevance of the notion of graph resides in its multilevel character allowing for a natural “middle-out” causation making largely obsolete the traditional opposition between “top-down” and “bottom-up” styles of reasoning, so fulfilling the foundation dream of systems science of a direct link between systems analysis and the underlying physical reality.

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COLLECTIVE ADAPTATIVE SYSTEMS SYNTHESIS WITH NON-ZERO-SUM GAMES

COLLECTIVE ADAPTATIVE SYSTEMS SYNTHESIS WITH NON-ZERO-SUM GAMES | Complexity - Complex Systems Theory | Scoop.it

The objective of CASSTING is to develop a novel approach for analysing and designing collective adaptive systems in their totality, by setting up a game theoretic framework. Here components are viewed as players, their behaviour is captured by strategies, system runs are plays, and specifications are winning conditions. We will develop formalisms for modelling collective adaptive systems as games, and algorithms for synthesising optimal strategies (and components).

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3rd International Conference on Complex Dynamical Systems and Their Applications: New Mathematical Concepts and Applications | The European Mathematical Society

3rd International Conference on Complex Dynamical Systems and Their Applications: New Mathematical Concepts and Applications | The European Mathematical Society | Complexity - Complex Systems Theory | Scoop.it
3rd International Conference on Complex Dynamical Systems and Their Applications: New Mathematical Concepts and... http://t.co/Wd83nLP9Ik
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The influence of persuasion in opinion formation and polarization

We present a model that explores the influence of persuasion in a population of agents with positive and negative opinion orientations. The opinion of each agent is represented by an integer number k that expresses its level of agreement on a given issue, from totally against k=-M to totally in favor k=M. Same-orientation agents persuade each other with probability p, becoming more extreme, while opposite-orientation agents become more moderate as they reach a compromise with probability q. The population initially evolves to (a) a polarized state for r=p/q>1, where opinions' distribution is peaked at the extreme values k=±M, or (b) a centralized state for r<1, with most opinions around k=±1. When r»1, polarization lasts for a time that diverges as rMlnN, where N is the population's size. Finally, an extremist consensus (k=M or -M) is reached in a time that scales as r-1 for r«1.

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The Fascinating World of Complex Systems

Part 1:             http://www.multimedia.ethz.ch/campus/zurichmeetsny/?doi=10.3930/ETHZ/AV-80b92958-97b0-4ad7-b07f-b15192931efc&autostart=false
 
Part 2:             http://www.multimedia.ethz.ch/campus/zurichmeetsny/?doi=10.3930/ETHZ/AV-1db36e67-b2d7-4229-8973-ef1bb54dde27&autostart=false
  
http://www.complexsys.org/publicprograms.html


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june holley's curator insight, July 9, 8:40 AM

Videos on complex systems.

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Evolution’s Contrarian Capacity for Creativity - Facts So Romantic - Nautilus

Evolution’s Contrarian Capacity for Creativity - Facts So Romantic - Nautilus | Complexity - Complex Systems Theory | Scoop.it

One can imagine life evolving again and again, crashing on the rocks of time and circumstance, until finally it hit upon just the right mutation rate—one that eons later would produce organisms and species and ecosystems.

 

 

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Studying Collective Human Decision Making and Creativity with Evolutionary Computation

We report a summary of our interdisciplinary research project "Evolutionary Perspective on Collective Decision Making" that was conducted through close collaboration between computational, organizational and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways---(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision making processes, and (3) as a research tool for collecting high-resolution experimental data of actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.

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Are Big, Rich Cities Greener Than Poor Ones?

Are Big, Rich Cities Greener Than Poor Ones? | Complexity - Complex Systems Theory | Scoop.it
When it comes to cities, being big and rich is better for the planet than being big and poor, according to a new study of carbon dioxide emissions from cities around the world. But is this correct?

Via Claudia Mihai, Roger D. Jones, PhD
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Global Civil Unrest: Contagion, Self-Organization, and Prediction

Global Civil Unrest: Contagion, Self-Organization, and Prediction | Complexity - Complex Systems Theory | Scoop.it

Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, records of civil unrest of 170 countries during the period 1919–2008 are analyzed. It is demonstrated that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.

 

 

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The Science of Inequality

The Science of Inequality | Complexity - Complex Systems Theory | Scoop.it

In 2011, the wrath of the 99% kindled Occupy movements around the world. The protests petered out, but in their wake an international conversation about inequality has arisen, with tens of thousands of speeches, articles, and blogs engaging everyone from President Barack Obama on down. Ideology and emotion drive much of the debate. But increasingly, the discussion is sustained by a tide of new data on the gulf between rich and poor.

This special issue uses these fresh waves of data to explore the origins, impact, and future of inequality around the world. Archaeological and ethnographic data are revealing how inequality got its start in our ancestors (see pp. 822 and 824). New surveys of emerging economies offer more reliable estimates of people's incomes and how they change as countries develop (see p. 832). And in the past decade in developed capitalist nations, intensive effort and interdisciplinary collaborations have produced large data sets, including the compilation of a century of income data and two centuries of wealth data into the World Top Incomes Database (WTID) (see p. 826 and Piketty and Saez, p. 838).

Science 23 May 2014: 
Vol. 344 no. 6186 pp. 818-821 
DOI: 10.1126/science.344.6186.818


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Nigel Goldenfeld: We Need a Theory of Life

Nigel Goldenfeld: We Need a Theory of Life | Complexity - Complex Systems Theory | Scoop.it
I was intrigued when Carl Woese told me his collaboration with University of Illinois physicist Nigel Goldenfeld was the most productive one of his entire career, and was pleased to finally run into Goldenfeld last September at lunch in the courtyard...
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▶ Large-Scale Structure in Networks - YouTube

▶ Large-Scale Structure in Networks - YouTube | Complexity - Complex Systems Theory | Scoop.it
Mark Newman May 2, 2014 Annual Science Board Symposium and Meeting Complexity: Theory and Practice
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Eli Levine's curator insight, June 9, 2:40 AM

To know the structure is to know a HUMONGOUS part of the function and, thus, the ability to predict.  It seems to me to be a large fractal pattern of clusters, nodes and connections (but, that is just in my relatively uneducated eye). 

 

Never forget, though, that there are important qualitative aspects to networks (think of defacto qualities of the nodes, groups of nodes and the connections amongst them).  Very important for social and/or ecological/causal relation networks (essentially, a network that outlines and maps accurately the function of a system and all of the flows of information and material resources).

 

Really cool stuff here.

 

Think about it..

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Dynamics of Complex Systems - Y. Bar-Yam

Dynamics of Complex Systems - Y. Bar-Yam | Complexity - Complex Systems Theory | Scoop.it

Textbook for seminar/course on complex systems.
View full text in PDF format

The study of complex systems in a unified framework has become recognized in recent years as a new scientific discipline, the ultimate of interdisciplinary fields. Breaking down the barriers between physics, chemistry and biology and the so-called soft sciences of psychology, sociology, economics, and anthropology, this text explores the universal physical and mathematical principles that govern the emergence of complex systems from simple components.

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Agent Based Modeling of Complex Adaptive Systems - advanced : Course Home

Agent Based Modeling of Complex Adaptive Systems - advanced : Course Home | Complexity - Complex Systems Theory | Scoop.it

Building on Complex Adaptive Systems theory and basic Agent Based Modeling knowledge presented in SPM4530, the Advanced course will focus on the model development process. The students are expected to conceptualize, develop and verify a model during the course, individually or in a group. The modeling tasks will be, as much as possible, based on real life research problems, formulated by various research groups from within and outside the faculty.  

Study Goals
The main goal of the course is to learn how to form a modeling question, perform a system decomposition, conceptualize and formalize the system elements, implement and verify the simulation and validate an Agent Based Model of a socio-technical system. 

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Dynamics of Complex Systems | NECSI | Libros y ...

Dynamics of Complex Systems | NECSI | Libros y ... | Complexity - Complex Systems Theory | Scoop.it
Complejidady Economía's insight: Full Online Text (Dynamics of Complex Systems | NECSI | @scoopit http://t.co/sVePaP2sG2)
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Insights from the Wikipedia Contest (IEEE Contest for Data Mining 2011)

The Wikimedia Foundation has recently observed that newly joining editors on Wikipedia are increasingly failing to integrate into the Wikipedia editors' community, i.e. the community is becoming increasingly harder to penetrate. To sustain healthy growth of the community, the Wikimedia Foundation aims to quantitatively understand the factors that determine the editing behavior, and explain why most new editors become inactive soon after joining. As a step towards this broader goal, the Wikimedia foundation sponsored the ICDM (IEEE International Conference for Data Mining) contest for the year 2011.
The objective for the participants was to develop models to predict the number of edits that an editor will make in future five months based on the editing history of the editor. Here we describe the approach we followed for developing predictive models towards this goal, the results that we obtained and the modeling insights that we gained from this exercise. In addition, towards the broader goal of Wikimedia Foundation, we also summarize the factors that emerged during our model building exercise as powerful predictors of future editing activity.

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