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Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It

Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It | collectibles from scoop.it | Scoop.it

Simon DeDeo, a research fellow in applied mathematics and complex systems at the Santa Fe Institute, had a problem. He was collaborating on a new project analyzing 300 years’ worth of data from the archives of London’s Old Bailey, the central criminal court of England and Wales. Granted, there was clean data in the usual straightforward Excel spreadsheet format, including such variables as indictment, verdict, and sentence for each case. But there were also full court transcripts, containing some 10 million words recorded during just under 200,000 trials.

 

“How the hell do you analyze that data?” DeDeo wondered. It wasn’t the size of the data set that was daunting; by big data standards, the size was quite manageable. It was the sheer complexity and lack of formal structure that posed a problem. This “big data” looked nothing like the kinds of traditional data sets the former physicist would have encountered earlier in his career, when the research paradigm involved forming a hypothesis, deciding precisely what one wished to measure, then building an apparatus to make that measurement as accurately as possible.

 

“In physics, you typically have one kind of data and you know the system really well,” said DeDeo. “Now we have this new multimodal data [gleaned] from biological systems and human social systems, and the data is gathered before we even have a hypothesis.” The data is there in all its messy, multi-dimensional glory, waiting to be queried, but how does one know which questions to ask when the scientific method has been turned on its head?


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Arjen ten Have's curator insight, October 9, 2013 2:48 PM

This is not as much work for math, here is where it gets interesting, where it really becomes INTERdisciplinary rather than MULTI. The same for Bioinformatics. We are developing tools to correct for instance MSAs, very simple tricks that deal with the complexity. The biologist has to explain the math guy what he wants. It is not about new math, it is about flexibility!

Mark Waser's curator insight, October 10, 2013 4:53 PM

I dislike the title and the initial thrust but the article is well worth reading by the end.

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Physicists Make the Case That Our Brains' Learning Is Controlled by Entropy

Physicists Make the Case That Our Brains' Learning Is Controlled by Entropy | collectibles from scoop.it | Scoop.it
Everything's connected.

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A theoretical foundation for multi-scale regular vegetation patterns

A theoretical foundation for multi-scale regular vegetation patterns | collectibles from scoop.it | Scoop.it

Empirically validated mathematical models show that a combination of intraspecific competition between subterranean social-insect colonies and scale-dependent feedbacks between plants can explain the spatially periodic vegetation patterns observed in many landscapes, such as the Namib Desert ‘fairy circles’.

 

A theoretical foundation for multi-scale regular vegetation patterns

Corina E. Tarnita, Juan A. Bonachela, Efrat Sheffer, Jennifer A. Guyton, Tyler C. Coverdale, Ryan A. Long & Robert M. Pringle

Nature 541, 398–401 (19 January 2017) doi:10.1038/nature20801


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Sustaining the Commons | Free eBook

Sustaining the Commons | Free eBook | collectibles from scoop.it | Scoop.it

This textbook discusses the main framework, concepts and applications of the work of Elinor Ostrom for an undergraduate audience.


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How Do You Say “Life” in Physics?

How Do You Say “Life” in Physics? | collectibles from scoop.it | Scoop.it

We think we know life when we see it. Darwin’s theory even explains how one form of life evolves into another. But what is the difference between a robin and a rock, when both obey the same physical laws? In other words, how do you say “life” in physics? Some have argued that the word is untranslatable. But maybe it simply needed the right translator.

 

http://nautil.us/issue/34/adaptation/how-do-you-say-life-in-physics


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Rescooped by Vasileios Basios from Self-organizing and Systems Mapping
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Systems Thinking: A journey along the way

Systems Thinking: A journey along the way | collectibles from scoop.it | Scoop.it
A systems view is somewhat in contradiction to the concept of analysis, which is breaking things down into smaller pieces to simplify the study. Analysis brings with it the risk of potentially loos…

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FlaTt AND Fluid: How companies without hierarchy manage themselves

FlaTt AND Fluid: How companies without hierarchy manage themselves | collectibles from scoop.it | Scoop.it
This post explores three flat and fluid organizations in completely different industries and of vastly different sizes. …

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Stigmergy as a Universal Coordination Mechanism I: Definition and Components

Stigmergy as a Universal Coordination Mechanism I: Definition and Components | collectibles from scoop.it | Scoop.it

The concept of stigmergy has been used to analyze self-organizing activities in an ever-widening range of domains, including social insects, robotics, web communities and human society. Yet, it is still poorly understood and as such its full power remains underappreciated. The present paper clarifies the issue by defining stigmergy as a mechanism of indirect coordination in which the trace left by an action in a medium stimulates subsequent actions. It then analyses the fundamental concepts used in the definition: action, agent, medium, trace and coordination. It clarifies how stigmergy enables complex, coordinated activity without any need for planning, control, communication, simultaneous presence, or even mutual awareness. The resulting self-organization is driven by a combination of positive and negative feedbacks, amplifying beneficial developments while suppressing errors. Thus, stigmergy is applicable to a very broad variety of cases, from chemical reactions to bodily coordination and Internet-supported collaboration in Wikipedia.

 

Stigmergy as a Universal Coordination Mechanism I: Definition and Components
Leslie Marsh, Ted G. Lewis, Francis Heylighen

Cognitive Systems Research

http://dx.doi.org/10.1016/j.cogsys.2015.12.002 ;


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Open Sourcing Social Change: Inside the Constellation Model | TIM Review

Open Sourcing Social Change: Inside the Constellation Model | TIM Review | collectibles from scoop.it | Scoop.it
"In spite of current ads and slogans, the world doesn't change one person at a time. It changes as networks of relationships form among people who discover they share a common cause and vision of what's possible." Margaret Wheatley and Deborah Freize

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The physics of life

The physics of life | collectibles from scoop.it | Scoop.it
From flocking birds to swarming molecules, physicists are seeking to understand 'active matter' — and looking for a fundamental theory of the living world.

 

http://www.nature.com/news/the-physics-of-life-1.19105


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Marcelo Errera's curator insight, January 13, 2016 1:09 PM

Organization emerges naturally. One more manifestation of the constructal law.

 

By the way, soon to appear:

The Physics of Life: The Evolution of Everything 
by Adrian Bejan 
Link: http://amzn.com/1250078822

Francisco Restivo's curator insight, January 14, 2016 6:29 PM

Living world is the real laboratory.

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Holacracy, Managerless Offices and The Future of Work

Holacracy, Managerless Offices and The Future of Work | collectibles from scoop.it | Scoop.it
Brian Robertson, Co-Founder of HolacracyOne, gives an overview of his revolutionary new self-management system.

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Why You Need A Boss, Even If You Hate Her

Why You Need A Boss, Even If You Hate Her | collectibles from scoop.it | Scoop.it
Without hierarchy, there is chaos.

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june holley's curator insight, January 27, 2016 1:04 PM

This is ridiculous!

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Emergence of Consensus in a Multi-Robot Network: from Abstract Models to Empirical Validation

Consensus dynamics in decentralised multiagent systems are subject to intense studies, and several different models have been proposed and analysed. Among these, the naming game stands out for its simplicity and applicability to a wide range of phenomena and applications, from semiotics to engineering. Despite the wide range of studies available, the implementation of theoretical models in real distributed systems is not always straightforward, as the physical platform imposes several constraints that may have a bearing on the consensus dynamics. In this paper, we investigate the effects of an implementation of the naming game for the kilobot robotic platform, in which we consider concurrent execution of games and physical interferences. Consensus dynamics are analysed in the light of the continuously evolving communication network created by the robots, highlighting how the different regimes crucially depend on the robot density and on their ability to spread widely in the experimental arena. We find that physical interferences reduce the benefits resulting from robot mobility in terms of consensus time, but also result in lower cognitive load for individual agents.

 

Emergence of Consensus in a Multi-Robot Network: from Abstract Models to Empirical Validation
Vito Trianni, Daniele De Simone, Andreagiovanni Reina, Andrea Baronchelli

http://arxiv.org/abs/1601.04952


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Stigmergy as a Universal Coordination Mechanism I: Definition and Components

Stigmergy as a Universal Coordination Mechanism I: Definition and Components | collectibles from scoop.it | Scoop.it

The concept of stigmergy has been used to analyze self-organizing activities in an ever-widening range of domains, including social insects, robotics, web communities and human society. Yet, it is still poorly understood and as such its full power remains underappreciated. The present paper clarifies the issue by defining stigmergy as a mechanism of indirect coordination in which the trace left by an action in a medium stimulates subsequent actions. It then analyses the fundamental concepts used in the definition: action, agent, medium, trace and coordination. It clarifies how stigmergy enables complex, coordinated activity without any need for planning, control, communication, simultaneous presence, or even mutual awareness. The resulting self-organization is driven by a combination of positive and negative feedbacks, amplifying beneficial developments while suppressing errors. Thus, stigmergy is applicable to a very broad variety of cases, from chemical reactions to bodily coordination and Internet-supported collaboration in Wikipedia.

 

Stigmergy as a Universal Coordination Mechanism I: Definition and Components
Leslie Marsh, Ted G. Lewis, Francis Heylighen

Cognitive Systems Research

http://dx.doi.org/10.1016/j.cogsys.2015.12.002 ;


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A solution to the single-question crowd wisdom problem

Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.

 

A solution to the single-question crowd wisdom problem

Dražen Prelec, H. Sebastian Seung & John McCoy

Nature 541, 532–535 (26 January 2017) doi:10.1038/nature21054

 


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Six Steps to Developing a Societal Change System | Networking Action

Six Steps to Developing a Societal Change System | Networking Action | collectibles from scoop.it | Scoop.it

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Age of Entanglement

Age of Entanglement | collectibles from scoop.it | Scoop.it

This essay proposes a map for four domains of creative exploration—Science, Engineering, Design and Art—in an attempt to represent the antidisciplinary hypothesis: that knowledge can no longer be ascribed to, or produced within, disciplinary boundaries, but is entirely entangled. The goal is to establish a tentative, yet holistic, cartography of the interrelation between these domains, where one realm can incite ®evolution inside another; and where a single individual or project can reside in multiple dominions. Mostly, this is an invitation to question and to amend what is being proposed.

 

Age of Entanglement
By Neri Oxman

Journal of Design of Science

http://jods.mitpress.mit.edu/pub/AgeOfEntanglement


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Tip Ghosh's curator insight, March 20, 2016 3:49 PM

The article links art and science together

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What makes good strategy?

What makes good strategy? | collectibles from scoop.it | Scoop.it
In Good Strategy/Bad Strategy, Richard Rumelt describes what bad strategy is and why we see so much of it. He also shares his framework for what drives good strategy along with guidance on how to create more of it. For a quick overview of his work, make sure to

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Mental Modeler - Fuzzy Logic Cognitive Mapping

Mental Modeler - Fuzzy Logic Cognitive Mapping | collectibles from scoop.it | Scoop.it

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june holley's curator insight, January 21, 2016 7:43 AM

Example of a simple web-based tool that can be used by communities to look at implications of decisions. 

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Flat and Fluid: How Companies Without Hierarchy Manage Themselves — Medium

Flat and Fluid:  How Companies Without Hierarchy Manage Themselves — Medium | collectibles from scoop.it | Scoop.it
This post explores three flat and fluid organizations in completely different industries and of vastly different sizes. …

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Earth Talk: Fritjof Capra - The Systems View of Life

A talk given at Schumacher College (UK), Dartington on May 7th 2014. The great challenge of our time is to build and nurture sustainable communities, designe...

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System Mapping for You

System Mapping for You | collectibles from scoop.it | Scoop.it
Get this simple tool that helps us make sense of the complex patterns around us. HSD offers you a tool created for mapping your own complex systems.

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Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach | collectibles from scoop.it | Scoop.it
Background Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.

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A pulsating star in the constellation Lyra revealed a peculiar mathematical object

A pulsating star in the constellation Lyra revealed a peculiar mathematical object | collectibles from scoop.it | Scoop.it

A pulsating star in the constellation Lyra generates a unique fractal pattern that hints at unknown stellar processes.


What struck John Learned about the blinking of KIC 5520878, a bluish-white star 16,000 light-years away, was how artificial it seemed. A “variable” star, KIC 5520878 brightens and dims in a six-hour cycle, seesawing between cool-and-clear and hot-and-opaque. Overlaying this rhythm is a second, subtler variation of unknown origin; this frequency interplays with the first to make some of the star’s pulses brighter than others. In the fluctuations, Learned had identified interesting and, he thought, possibly intelligent sequences, such as prime numbers (which have been floated as a conceivable basis of extraterrestrial communication). He then found hints that the star’s pulses were chaotic.


But when Learned mentioned his investigations to a colleague, William Ditto, last summer, Ditto was struck by the ratio of the two frequencies driving the star’s pulsations. “I said, ‘Wait a minute, that’s the golden mean.’” This irrational number, which begins 1.618, is found in certain spirals, golden rectangles and now the relative speeds of two mysterious stellar processes. It meant that the blinking of KIC 5520878 wasn’t an extraterrestrial signal, Ditto realized, but something else that had never before been found in nature: a mathematical curiosity caught halfway between order and chaos called a “strange nonchaotic attractor.”


Dynamical systems — such as pendulums, the weather and variable stars — tend to fall into circumscribed patterns of behavior that are a subset of all the ways they could possibly behave. A pendulum wants to swing from side to side, for example, and the weather stays within a general realm of possibility (it will never be zero degrees in summer). Plotting these patterns creates a shape called an “attractor.”


Mathematicians in the 1970s used attractors to model the behavior of chaotic systems like the weather, and they found that the future path of such a system through its attractor is extremely dependent on its exact starting point. This sensitivity to initial conditions, known as the butterfly effect, makes the behavior of chaotic systems unpredictable; you can’t tell the forecast very far in advance if the flap of a butterfly’s wings today can make the difference, two weeks from now, between sunshine and a hurricane. The infinitely detailed paths that most chaotic systems take through their attractors are called “fractals.” Zoom in on a fractal, and new variations keep appearing, just as new outcrops appear whenever you zoom in on the craggy coastline of Great Britain. Attractors with this fractal structure are called “strange attractors.”


Then in 1984, mathematicians led by Celso Grebogi, Edward Ott and James Yorke of the University of Maryland in College Park discovered an unexpected new category of objects — strange attractors shaped not by chaos but by irrationality. These shapes formed from the paths of a system driven at two frequencies with no common multiple — that is, frequencies whose ratio was an irrational number, like pi or the golden mean. Unlike other strange attractors, these special “nonchaotic” ones did not exhibit a butterfly effect; a small change to a system’s initial state had a proportionally small effect on its resulting fractal journey through its attractor, making its evolution relatively stable and predictable.


“It was quite surprising to find these fractal structures in something that was totally nonchaotic,” said Grebogi, a Brazilian chaos theorist who is now a professor at the University of Aberdeen in Scotland.


Though no example could be positively identified, scientists speculated that strange nonchaotic attractors might be everywhere around and within us. It seemed possible that the climate, with its variable yet stable patterns, could be such a system. The human brain might be another.


The first laboratory demonstration of strange nonchaotic dynamics occurred in 1990, spearheaded by Ott and none other than William Ditto. Working at the Naval Surface Warfare Center in Silver Spring, Maryland, Ditto, Ott and several collaborators induced a magnetic field inside a metallic strip of tinsel called a “magnetoelastic ribbon” and varied the field’s strength at two different frequencies related by the golden ratio. The ribbon stiffened and relaxed in a strange nonchaotic pattern, bringing to life the mathematical discovery from six years earlier. “We were the first people to see this thing; we were pleased with that,” Ditto said. “Then I forgot about it for 20 years.


The study of variable stars entered boom times in 2009 with the launch of the Kepler telescope, which looked for small aberrations in starlight as a sign of distant planets. The telescope gathered a trove of unprecedented data on the pulsations of variable stars throughout the galaxy. Other, ground-based surveys have added further riches.


The data revealed subtle variations in many of the stars’ pulsations that hinted at stellar processes beyond those described by Eddington. The pulses of starlight could be separated into two main frequencies: a faster one like the beat of a snare drum and a slower one like a gong, with the two rhythms played out of sync. And in more than 100 of these variable stars — including those, like KIC 5520878, belonging to a subclass called “RRc” — the ratios defining the duration of one frequency relative to the other inexplicably fell between 1.58 and 1.64.


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Mastering the game of Go with deep neural networks and tree search

Mastering the game of Go with deep neural networks and tree search | collectibles from scoop.it | Scoop.it
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

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Earth Talk: Fritjof Capra - The Systems View of Life

A talk given at Schumacher College (UK), Dartington on May 7th 2014. The great challenge of our time is to build and nurture sustainable communities, designe...

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