Lapidarium notes RSS

Amira Skomorowska's notes

"Everything you can imagine is real."— Pablo Picasso

Lapidarium

Tags:

Africa
Age of information
Ancient
Anthropology
Art
Artificial intelligence
Astronomy
Atheism
Beauty
Biography
Books
China
Christianity
Civilization
Cognition, perception, relativity
Cognitive science
Collective intelligence
Communication
Consciousness
Creativity
Culture
Curiosity
Cyberspace
Democracy
Documentary
Drawing
Earth
Economy
Evolution
Friendship
Funny
Future
Genetics
Globalization
Happiness
History
Human being
Illustrations
Imagination
Individualism
Infographics
Information
Inspiration
Internet
Knowledge
Language
Learning
Life
Literature
Logic
Love
Mathematics
Media
Metaphor
Mind & Brain
Multiculturalism
Music
Networks
Neuroscience
Painting
Paradoxes
Patterns
Philosophy
Poetry
Politics
Physics
Psychology
Rationalism
Religions
Science
Science & Art
Self improvement
Semantics
Society
Sociology
Storytelling
Technology
The other
Time
Timeline
Traveling
Unconsciousness
Universe
USA
Video
Violence
Visualization


Pensieri a caso
Photography
A Box Of Stories
Reading Space
Homepage

Twitter
Facebook

Contact

Archive

Feb
10th
Sun
permalink

Universality: In Mysterious Pattern, Math and Nature Converge

image

"In 1999, while sitting at a bus stop in Cuernavaca, Mexico, a Czech physicist named Petr Šeba noticed young men handing slips of paper to the bus drivers in exchange for cash. It wasn’t organized crime, he learned, but another shadow trade: Each driver paid a “spy” to record when the bus ahead of his had departed the stop. If it had left recently, he would slow down, letting passengers accumulate at the next stop. If it had departed long ago, he sped up to keep other buses from passing him. This system maximized profits for the drivers. And it gave Šeba an idea. (…)

The interaction between drivers caused the spacing between departures to exhibit a distinctive pattern previously observed in quantum physics experiments. (…) “We felt here some kind of similarity with quantum chaotic systems.” (…) A “spy” network makes the decentralized bus system more efficient. As a consequence, the departure times of buses exhibit a ubiquitous pattern known as “universality.” (…)

Subatomic particles have little to do with decentralized bus systems. But in the years since the odd coupling was discovered, the same pattern has turned up in other unrelated settings. Scientists now believe the widespread phenomenon, known as “universality,” stems from an underlying connection to mathematics, and it is helping them to model complex systems from the internet to Earth’s climate. (…)

                image

The red pattern exhibits a precise balance of randomness and regularity known as “universality,” which has been observed in the spectra of many complex, correlated systems. In this spectrum, a mathematical formula called the “correlation function” gives the exact probability of finding two lines spaced a given distance apart. (…)

The pattern was first discovered in nature in the 1950s in the energy spectrum of the uranium nucleus, a behemoth with hundreds of moving parts that quivers and stretches in infinitely many ways, producing an endless sequence of energy levels. In 1972, the number theorist Hugh Montgomery observed it in the zeros of the Riemann zeta function, a mathematical object closely related to the distribution of prime numbers. In 2000, Krbálek and Šeba reported it in the Cuernavaca bus system. And in recent years it has shown up in spectral measurements of composite materials, such as sea ice and human bones, and in signal dynamics of the Erdös–Rényi model, a simplified version of the internet named for Paul Erdös and Alfréd Rényi. (…)

Each of these systems has a spectrum — a sequence like a bar code representing data such as energy levels, zeta zeros, bus departure times or signal speeds. In all the spectra, the same distinctive pattern appears: The data seem haphazardly distributed, and yet neighboring lines repel one another, lending a degree of regularity to their spacing. This fine balance between chaos and order, which is defined by a precise formula, also appears in a purely mathematical setting: It defines the spacing between the eigenvalues, or solutions, of a vast matrix filled with random numbers. (…)

It seems to be a law of nature,” said Van Vu, a mathematician at Yale University who, with Terence Tao of the University of California, Los Angeles, has proven universality for a broad class of random matrices.

Universality is thought to arise when a system is very complex, consisting of many parts that strongly interact with each other to generate a spectrum. The pattern emerges in the spectrum of a random matrix, for example, because the matrix elements all enter into the calculation of that spectrum. But random matrices are merely “toy systems” that are of interest because they can be rigorously studied, while also being rich enough to model real-world systems, Vu said. Universality is much more widespread. Wigner’s hypothesis (named after Eugene Wigner, the physicist who discovered universality in atomic spectra) asserts that all complex, correlated systems exhibit universality, from a crystal lattice to the internet.

     

Mathematicians are using random matrix models to study and predict some of the internet’s properties, such as the size of typical computer clusters. (Illustration: Matt Britt)

The more complex a system is, the more robust its universality should be, said László Erdös of the University of Munich, one of Yau’s collaborators. “This is because we believe that universality is the typical behavior.”

— Natalie Wolchover, In Mysterious Pattern, Math and Nature Converge, Wired, Feb 6, 2013. (Photo: Marco de Leija)

See also:

Mathematics of Disordered Quantum Systems and Matrices, IST Austria.

Jan
22nd
Tue
permalink

Kevin Slavin: How algorithms shape our world

“But the Turing test cuts both ways. You can’t tell if a machine has gotten smarter or if you’ve just lowered your own standards of intelligence to such a degree that the machine seems smart. If you can have a conversation with a simulated person presented by an AI program, can you tell how far you’ve let your sense of personhood degrade in order to make the illusion work for you?

People degrade themselves in order to make machines seem smart all the time. Before the crash, bankers believed in supposedly intelligent algorithms that could calculate credit risks before making bad loans. We ask teachers to teach to standardized tests so a student will look good to an algorithm. We have repeatedly demonstrated our species’ bottomless ability to lower our standards to make information technology look good. Every instance of intelligence in a machine is ambiguous.

The same ambiguity that motivated dubious academic AI projects in the past has been repackaged as mass culture today. Did that search engine really know what you want, or are you playing along, lowering your standards to make it seem clever? While it’s to be expected that the human perspective will be changed by encounters with profound new technologies, the exercise of treating machine intelligence as real requires people to reduce their mooring to reality.”

Jaron Lanier, You are Not a Gadget (2010)

Kevin Slavin argues that we’re living in a world designed for — and increasingly controlled by — algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture.

"We’re writing things (…) that we can no longer read. And we’ve rendered something illegible, and we’ve lost the sense of what’s actually happening in this world that we’ve made. (…)

“We’re running through the United States with dynamite and rock saws so that an algorithm can close the deal three microseconds faster, all for a communications framework that no human will ever know; that’s a kind of manifest destiny.”

Kevin Slavin, Entrepreneur, Raconteur Assistant Professor of Media Arts and Sciences, MIT Media Lab, Kevin Slavin: How algorithms shape our world, TED, July 2011.

See also:

☞ Jane Wakefield, When algorithms control the world, BBC, Aug 23, 2011.

Jul
24th
Tue
permalink

Dirk Helbing on A New Kind Of Socio-inspired Technology

The big unexplored continent in science is actually social science, so we really need to understand much better the principles that make our society work well, and socially interactive systems. Our future information society will be characterized by computers that behave like humans in many respects. In ten years from now, we will have computers as powerful as our brain, and that will really fundamentally change society. Many professional jobs will be done much better by computers. How will that change society? How will that change business? What impacts does that have for science, actually?

There are two big global trends. One is big data. That means in the next ten years we’ll produce as many data, or even more data than in the past 1,000 years. The other trend is hyperconnectivity. That means we have networking our world going on at a rapid pace; we’re creating an Internet of things. So everyone is talking to everyone else, and everything becomes interdependent. What are the implications of that? (…)

But on the other hand, it turns out that we are, at the same time, creating highways for disaster spreading. We see many extreme events, we see problems such as the flash crash, or also the financial crisis. That is related to the fact that we have interconnected everything. In some sense, we have created unstable systems. We can show that many of the global trends that we are seeing at the moment, like increasing connectivity, increase in the speed, increase in complexity, are very good in the beginning, but (and this is kind of surprising) there is a turning point and that turning point can turn into a tipping point that makes the systems shift in an unknown way.

It requires two things to understand our systems, which is social science and complexity science; social science because computers of tomorrow are basically creating artificial social systems. Just take financial trading today, it’s done by the most powerful computers. These computers are creating a view of the environment; in this case the financial world. They’re making projections into the future. They’re communicating with each other. They have really many features of humans. And that basically establishes an artificial society, which means also we may have all the problems that we are facing in society if we don’t design these systems well. The flash crash is just one of those examples that shows that, if many of those components — the computers in this case — interact with each other, then some surprising effects can happen. And in that case, $600 billion were actually evaporating within 20 minutes.

Of course, the markets recovered, but in some sense, as many solid stocks turned into penny stocks within minutes, it also changed the ownership structure of companies within just a few minutes. That is really a completely new dimension happening when we are building on these fully automated systems, and those social systems can show a breakdown of coordination, tragedies of the commons, crime or cyber war, all these kinds of things will happen if we don’t design them right.

We really need to understand those systems, not just their components. It’s not good enough to have wonderful gadgets like smartphones and computers; each of them working fine in separation. Their interaction is creating a completely new world, and it is very important to recognize that it’s not just a gradual change of our world; there is a sudden transition in the behavior of those systems, as the coupling strength exceeds a certain threshold.

A traffic flow in a circle

I’d like to demonstrate that for a system that you can easily imagine: traffic flow in a circle. Now, if the density is high enough, then the following will happen: after some time, although every driver is trying hard to go at a reasonable speed, cars will be stopped by a so-called ‘phantom traffic jam.’ That means smooth traffic flow will break down, no matter how hard the drivers will try to maintain speed. The question is, why is this happening? If you would ask drivers, they would say, “hey, there was a stupid driver in front of me who didn’t know how to drive!” Everybody would say that. But it turns out it’s a systemic instability that is creating this problem.

That means a small variation in the speed is amplified over time, and the next driver has to brake a little bit harder in order to compensate for a delayed reaction. That creates a chain reaction among drivers, which finally stops traffic flow. These kinds of cascading effects are all over the place in the network systems that we have created, like power grids, for example, or our financial markets. It’s not always as harmless as in traffic jams. We’re just losing time in traffic jams, so people could say, okay, it’s not a very serious problem. But if you think about crowds, for example, we have this transition towards a large density of the crowd, then what will happen is a crowd disaster. That means people will die, although nobody wants to harm anybody else. Things will just go out of control. Even though there might be hundreds or thousands of policemen or security forces trying to prevent these things from happening.

This is really a surprising behavior of these kinds of strongly-networked systems. The question is, what implication does that have for other network systems that we have created, such as the financial system? There is evidence that the fact that now every bank is interconnected with every other bank has destabilized the system. That means that there is a systemic instability in place that makes it so hard to control, or even impossible to control. We see that the big players, and also regulators, have large difficulties to get control of these systems.  

That tells us something that we need to change our perspective regarding these systems. Those complex systems are not characterized anymore by the properties of their components. But they’re characterized by what is the outcome of the interactions between those components. As a result of those interactions, self-organization is going on in these systems. New emergent properties come up. They can be very surprising, actually, and that means we cannot understand those systems anymore, based on what we see, which is the components.

We need to have new instruments and tools to understand these kinds of systems. Our intuition will not work here. And that is what we want to create: we want to come up with a new information platform for everybody that is bringing together big data with exa-scale computing, with people, and with crowd sourcing, basically connecting the intelligence of the brains of the world.

One component that is going to measure the state of the world is called the Planetary Nervous System. That will measure not just the physical state of the world and the environmental situation, but it is also very important actually that we learn how to measure social capital, such as trust and solidarity and punctuality and these kinds of things, because this is actually very important for economic value generation, but also for social well-being.

Those properties as social capital, like trust, they result from social network interactions. We’ve seen that one of the biggest problems of the financial crisis was this evaporation of trust. It has burned tens of thousands of billion dollars. If we would learn how to stabilize trust, or build trust, that would be worth a lot of money, really. Today, however, we’re not considering the value of social capital. It can happen that we destroyed it or that we exploit it, such as we’ve exploited and destroyed our environment. If we learn how much is the value of social capital, we will start to protect it. Also we’ll take it into account in our insurance policies. Because today, no insurance is taking into account the value of social capital. It’s the material damage that we take into account, but not the social capital. That means, in some sense, we’re underinsured. We’re taking bigger risks than we should.

This is something that we want to learn, how to quantify the fundaments of society, to quantify the social footprint. It means to quantify the implications of our decisions and actions.

The second component, the Living Earth Simulator will be very important here, because that will look at what-if scenarios. It will take those big data generated by the Planetary Nervous System and allow us to look at different scenarios, to explore the various options that we have, and the potential side effects or cascading effects, and unexpected behaviors, because those interdependencies make our global systems really hard to understand. In many cases, we just overlook what would happen if we fix a problem over here: It might have unwanted side effects; in many cases, that is happening in other parts of our world.

We are using supercomputers today in all areas of our development. Like if we are developing a car, a plane or medical tracks or so, supercomputers are being used, also in the financial world. But we don’t have a kind of political or a business flight simulator that helps us to explore different opportunities. I think this is what we can create as our understanding of society progresses. We now have much better ideas of how social coordination comes about, what are the preconditions for cooperation. What are conditions that create conflict, or crime, or war, or epidemicspreading, in the good and the bad sense?

We’re using, of course, viral marketing today in order to increase the success of our products. But at the same time, also we are suffering from a quick spreading of emerging diseases, or of computer viruses, and Trojan horses, and so on. We need to understand these kinds of phenomena, and with the data and the computer power that is coming up, it becomes within reach to actually get a much better picture of these things.

The third component will be the Global Participatory Platform [pdf]. That basically makes those other tools available for everybody: for business leaders, for political decision-makers, and for citizens. We want to create an open data and modeling platform that creates a new information ecosystem that allows you to create new businesses, to come up with large-scale cooperation much more easily, and to lower the barriers for social, political and economic participation.

So these are the three big elements. We’ll furthermore  build exploratories of society, of the economy and environment and technology, in order to be able to anticipate possible crises, but also to see opportunities that are coming up. Those exploratories will bring these three elements together. That means the measurement component, the computer simulation component, and the participation, the interactiveness.

In some sense, we’re going to create virtual worlds that may look like our real world, copies of our world that allow us to explore polices in advance or certain kinds of planning in advance. Just to make it a little bit more concrete, we could, for example, check out a new airport or a new city quarter before it’s being built. Today we have these architectural plans, and competitions, and then the most beautiful design will have win. But then, in practice, it can happen that it doesn’t work so well. People have to stand in line in queues, or are obstructing each other. Many things may not work out as the architect imagined that.                 

What if we populated basically these architectural plans with real people? They could check it out, live there for some months and see how much they like it. Maybe even change the design. That means, the people that would use these facilities and would live in these new quarters of the city could actually participate in the design of the city. In the same sense, you can scale that up. Just imagine Google Earth or Google Street View filled with people, and have something like a serious kind of Second Life. Then we could have not just one history; we can check out many possible futures by actually trying out different financial architectures, or different decision rules, or different intellectual property rights and see what happens.                 

We could have even different virtual planets, with different laws and different cultures and different kinds of societies. And you could choose the planet that you like most. So in some sense, now a new age is opening up with almost unlimited resources. We’re, of course, still living in a material world, in which we have a lot of restrictions, because resources are limited. They’re scarce and there’s a lot of competition for these scarce resources. But information can be multiplied as much as you like. Of course, there is some cost, and also some energy needed for that, but it’s relatively low cost, actually. So we can create really almost infinite new possibilities for creativity, for productivity, for interaction. And it is extremely interesting that we have a completely new world coming up here, absolutely new opportunities that need to be checked out.

But now the question is: how will it all work? Or how would you make it work? Because the information systems that we have created are even more complex than our financial system. We know the financial system is extremely difficult to regulate and to control. How would you want to control an information system of this complexity? I think that cannot be done top-down. We are seeing now a trend that complex systems are run in a more and more decentralized way. We’re learning somehow to use self-organization principles in order to run these kinds of systems. We have seen that in the Internet, we are seeing t for smart grids, but also for traffic control.

I have been working myself on these new ways of self-control. It’s very interesting. Classically one has tried to optimize traffic flow. It’s so demanding that even our fastest supercomputers can’t do that in a strict sense, in real time. That means one needs to make simplifications. But in principle, what one is trying to do is to impose an optimal traffic light control top-down on the city. The supercomputer believes to know what is best for all the cars, and that is imposed on the system.                 

We have developed a different approach where we said: given that there is a large degree of variability in the system, the most important aspect is to have a flexible adaptation to the actual traffic conditions. We came up with a system where traffic flows control the traffic lights. It turns out this makes much better use of scarce resources, such as space and time. It works better for cars, it works better for public transport and for pedestrians and bikers, and it’s good for the environment as well.                 

The age of social innovation

There’s a new kind of socio-inspired technology coming up, now. Society has many wonderful self-organization mechanisms that we can learn from, such as trust, reputation, culture. If we can learn how to implement that in our technological system, that is worth a lot of money; billions of dollars, actually. We think this is the next step after bio-inspired technology.

The next big step is to focus on society. We’ve had an age of physics; we’re now in an age of biology. I think we are entering the age of social innovation as we learn to make sense of this even bigger complexity of society. It’s like a new continent to discover. It’s really fascinating what now becomes understandable with the availability of Big Data about human activity patterns, and it will open a door to a new future.

What will be very important in order to make sense of the complexity of our information society is to overcome the disciplinary silos of science; to think out of the box. Classically we had social sciences, we had economics, we had physics and biology and ecology, and computer science and so on. Now, our project is trying to bring those different fields together, because we’re deeply convinced that without this integration of different scientific perspectives, we cannot anymore make sense of these hyper-connected systems that we have created.                 

For example, computer science requires complexity science and social science to understand those systems that have been created and will be created. Why is this? Because the dense networking and to the complex interaction between the components creates self-organization, and emergent phenomena in those systems. The flash crash is just one example that shows that unexpected things can happen. We know that from many systems.

Complexity theory is very important here, but also social science. And why is that? Because the components of these information communication systems are becoming more and more human-like. They’re communicating with each other. They’re making a picture of the outside world. They’re projecting expectations into the future, and they are taking autonomous decisions. That means if those computers interact with each other, it’s creating an artificial social system in some sense.                 

In the same way, social science will need complexity science and computer science. Social science needs the data that computer science and information communication technology can provide. Now, and even more in the future, those data traces about human activities allow us eventually to detect patterns and kind of laws of human behavior. It will be only possible through the collaboration with computer science to get those data, and to make sense of what is happening actually in society. I don’t need to mention that obviously there are complex dynamics going on in society; that means complexity science is needed for social science as well.

In the same sense, we could say complexity science needs social science and computer science to become practical. To go a step beyond talking about butterfly effects and chaos and turbulence. And to make sure that the thinking of complexity science will pervade our thinking in the natural engineering and social sciences and allow us to understand the real problems of our world. That is kind of the essence: that we need to bring these different scientific fields together. We have actually succeeded to build up these integrated communities in many countries all over the world, ready to go, as soon as money becomes available for that.        

Big Data is not a solution per se. Even the most powerful machine learning algorithm will not be sufficient to make sense of our world, to understand the principles according to which our world is working. This is important to recognize. The great challenge is to marry data with theories, with models. Only then will we be able to make sense of the useful bits of data. It’s like finding a needle in the haystack. The more data you have, the more difficult it may be to find this needle, actually, to a certain extent. And there is this danger of over-fitting, of being distracted from important details. We are certainly already in an age where we’re flooded with information, and our attention span can actually not process all that information. That means there is a danger that this undermines our wisdom, if our attention is attracted by the wrong details of information. So we are confronted with the problem of finding the right institutions and tools and instruments for decision-making.        

The Living Earth Simulator will basically take the data that is gathered by the Internet, search requests, and created by sensor networks, and feed it into big computer simulations that are based on models of social and economic and technological behavior. In this way, we’ll be able to look at what-if scenarios. We hope to get a better understanding, for example, of financial systems and some answers to controversial questions such as how much leverage effect is good? Under what conditions is ‘naked short-selling’ beneficial? When does it destabilize markets? To what extent is high frequency trading good, or it can it also have side effects? All these kinds of questions, which are difficult to answer. Or how to deal best with the situation in Europe, where we have trouble, obviously, in Greece, but also kind of contagious effects on other countries and on the rest of the financial system. It would be very good to have the models and the data that allow us actually to simulate these kinds of scenarios and to take better-informed decisions. (…)

The idea is to have an open platform to create a data and model commons that everybody can contribute to, so people could upload data and models, and others could use that. People would also judge the quality of the data and models and rate them according to their criteria. And we also point out the criteria according to which they’re doing the rating. But in principle, everybody can contribute and everybody can use it. (…)                            

We have much better theories, also, that allows us to make sense of those data. We’re entering into an age where we can understand society and the economy much better, namely as complex self-organizing systems.           

It will be important to guide us into the future because we are creating very powerful systems. Information society will transform our society fundamentally and we shouldn’t just let it happen. We want to understand how that will change our society, and what are the different pathes that our society may take, and decide for the one that we want it to take. For that, we need to have a much better understanding.

Now a lot of social activity data are becoming available through Facebook and Twitter and Google search requests and so on. This is, of course, a huge opportunity for business. Businesses are talking about the new oil, personal data as new asset class. There’s something like a gold rush going on. That also, of course, has huge opportunities for science, eventually we can make sense of complex systems such as our society. There are different perspectives on this. They range from some people who think that information communication technologies eventually will create a God’s-eye view: systems that make sense of all human activities, and the interactions of people, while others are afraid of a Big Brother emerging.                 

The question is how to handle that situation. Some people say we don’t need privacy in society. Society is undergoing a transformation, and privacy is not anymore needed. I don’t actually share this point of view, as a social scientist, because public and private are two sides of the same coin, so they cannot exist without the other side. It is very important, for a society to work, to have social diversity. Today, we know to appreciate biodiversity, and in the same way we need to think about social diversity, because it’s a motor of innovation. It’s also an important factor for societal resilience. The question now is how all those data that we are creating, and also recommender system and personalized services are going to impact people’s decision-making behavior, and society overall.                 

This is what we need to look at now. How is people’s behavior changing through these kinds of data? How are people changing their behavior when they feel they’re being observed? Europe is quite sensitive about privacy. The project we are working on is actually trying to find a balance between the interest of companies and Big Data of governments and individuals. Basically we want to develop technologies that allow us to find this balance, to make sure that all the three perspectives actually are taken into account. That you can make big business, but also at the same time, the individual’s privacy is respected. That individuals have more control over their own data, know what is happening with them, have influence on what is happening with them. (…)           

In some sense, we want to create a new data and model commons, a new kind of language, a new public good that allows people to do new things. (…)

My feeling is that actually business will be made on top of this sea of data that’s being created. At the moment data is kind of the valuable resource, right? But in the future, it will probably be a cheap resource, or even a free resource to a certain extent, if we learn how to deal with openness of data. The expensive thing will be what we do with the data. That means the algorithms, the models, and theories that allow us to make sense of the data.”

Dirk Helbing, physicist, and professor of sociology at ETH Zurich – Swiss Federal Institute of Technology, in particular for modelling and simulation, A New Kind Of Socio-inspired Technology, Edge Conversation, June 19, 2012. (Illustration: WSF)

See also:

☞ Dirk Helbing, New science and technology to understand and manage our complex world in a more sustainable and resilient way (pdf) (presentation), ETH Zurich
Why does nature so consistently organize itself into hierarchies? Living Cells Show How to Fix the Financial System
Geoffrey West on Why Cities Keep Growing, Corporations and People Always Die, and Life Gets Faster
The Difference Between Online Knowledge and Truly Open Knowledge. In the era of the Internet facts are not bricks but networks
Networks tag on Lapidarium notes

Jul
13th
Fri
permalink

Why does nature so consistently organize itself into hierarchies? Living Cells Show How to Fix the Financial System

    

"Over the past three decades, the global financial system has become more dynamic and interconnected, more concentrated and complicated than ever before. Financial engineering seems to know no limits to creating new instruments that link institutions in new ways.

Is that a good thing? Or could the resulting financial network be too complex? Or, perhaps, complex in the wrong way?

A look at biology — which has been tinkering with network designs for billions of years — suggests that the answer to the last question is most likely yes.

In “The Architecture of Complexity [pdf], an extraordinarily original paper published 50 years ago, the economist, psychologist and artificial-intelligence pioneer Herbert Simon asked the question, Why does nature so consistently organize itself into hierarchies? Why, that is, are so many of its creations designed as systems of systems?

In biology, for example, cells organize into tissues, tissues into organs, organs into larger systems. The cell itself contains a nucleus and a cell membrane, ribosomes and mitochondria. Our human organizations obviously also follow hierarchies, as do our buildings, technological devices, even our writing — words make sentences, which build paragraphs, which then make up essays or chapters.

Scientists and philosophers since Aristotle have noted as much, but Simon, one of the most creative minds of the 20th century (he died in 2001), was perhaps the first to ask why. He also proposed an answer.

Hierarchical Design

For one thing, he pointed out, structures like this are easier to make and also more amenable to beneficial alteration. We might, in principle, build computers as enormously complex assemblies of billions of individual transistors, linked in some exquisite design. Then, however, every device would have to be built as a whole. We simplify construction by designing computers as assemblies of subunits that can be linked — a memory chip, central processor and keyboard, for example. The units can be built and tested separately, and they can be linked in different ways to make different kinds of computers. We can reach in and alter one component — changing the memory — without worrying that we have wrecked the keyboard. As a result, computers become easier to improve.

Hierarchy, in other words, is a way of limiting complexity in the interest of both stability and evolvability. Simon argued that systems structured in this way possess a basic, competitive simplicity.

We are only beginning to appreciate how much, as living beings, we rely on this architecture. Take ordinary bone, for example, which is remarkably tough, yet lightweight, with properties that our technology still cannot match. The secret is hierarchy. Within bone, small molecules bind together into proteins, which then link into filaments, which in turn organize into larger structures. When a bone suffers a blow, the hierarchy provides a variety of mechanisms by which it can pass along the excess energy it absorbs, without creating lasting damage. Bone, like most other structures in biology, is not just complex, but complex in a highly organized way. What about structures in economics and finance?

The growth of modern finance seems to have violated the principle of hierarchical structures, and with gusto. Two trends in the past 30 years — the merging of banks into huge institutions and the explosion of derivatives that link them around the globe — have made the network much less modular. We have created a vast web of interconnections with extreme complexity but little organization. And this does appear to have made the system less resilient.

Failures Cascade

For example, in a study last year, economists from the University of Auckland, New Zealand and the Bank of England used computer simulations to explore how failures might cascade through the interbank network, the system that banks use to manage their day-to-day financing demands by making loans to one another. This network normally functions fairly well, with funds flowing easily, but it can experience sharp liquidity crises — as it did following the collapse of Lehman Brothers Holdings Inc. in 2008. For short-term cash, banks rely heavily on “repos” — overnight sales of stocks or other assets, which they agree to repurchase later. How much cash a bank can get for a specific asset depends on the “haircut” — a reduction in the cash lent against it, which lenders demand to protect themselves against risks, or losses they may face if, in the case of default, they have to sell the asset themselves.

Haircuts fluctuate with time and perceptions, and the simulations show that the interbank network’s resilience to such fluctuations depends on its architecture. The more the network is concentrated in and dominated by big banks and the higher the overall density of links among banks, the less modular the system is, and the less stable. That is, both these trends make it more likely for financial distress to cascade through the network.

Specifically, huge banks that account for a disproportionate share of all links act as potential epicenters for trouble. This is a way of describing “too big to fail,” although it would be more accurate to say “too central to fail.” Meanwhile, a high density of interconnections in the network creates ever more channels along which contagion can move. This problem encourages banks to “hoard” funds in times of stress — the least desirable behavior in a network of banks trying to share resources to meet their momentary funding demands.

Unlike organisms, of course, financial systems haven’t undergone evolutionary competition from which only the fit have emerged. We have little reason to expect that what exists would be anything like optimal, or even reasonable.

To counter these developments, we could try to manage the way lending occurs — control the amount of leverage used and the haircuts involved — so as to prevent dangerous contagion. More boldly, we might try to set up constraints on the very concentration of our networks, on who is linked with whom and how strongly.

Both high concentration and high interconnectedness contribute to an “everything is linked to everything” outcome that is the very opposite of modularity, and a likely recipe for instability. Financial engineering should learn to avoid this architecture, just as surely as biology has.”

Mark Buchanan, a theoretical physicist, Living Cells Show How to Fix the Financial System, Bloomberg, July 11, 2012.

See also:

☞ Herbert Simon, The Architecture of Complexity (pdf), 1962.
The Rise of Complexity. Scientists replicate key evolutionary step in life on earth, Lapidarium notes
Networks tag on Lapidarium notes

May
20th
Sun
permalink

The Difference Between Online Knowledge and Truly Open Knowledge. In the era of the Internet facts are not bricks but networks

    
                                                             Image: Library of Congress

“Knowledge is not a result merely of filtering or algorithms. It results from a far more complex process that is social, goal-driven, contextual, and culturally-bound. We get to knowledge — especially “actionable” knowledge — by having desires and curiosity, through plotting and play, by being wrong more often than right, by talking with others and forming social bonds, by applying methods and then backing away from them, by calculation and serendipity, by rationality and intuition, by institutional processes and social roles. Most important in this regard, where the decisions are tough and knowledge is hard to come by, knowledge is not determined by information, for it is the knowing process that first decides which information is relevant, and how it is to be used.”

David Weinberger, The Problem with the Data-Information-Knowledge-Wisdom Hierarchy, Harvard Business Review, Feb 2, 2010.

"The digitization of 21st-century media, Weinberger argues, leads not to the creation of a “global village" but rather to a new understanding of what knowledge is, to a change in the basic epistemology governing the universe. And this McLuhanesque transformation, in turn, reveals the general truth of the Heideggarian vision. Knowledge qua knowledge, Weinberger claims, is increasingly enmeshed in webs of discourse: culture-dependent and theory-free.

The causal force lying behind this massive sea change is, of course, the internet. Google search results — “9,560,000 results for ‘Heidegger’ in .71 seconds”) — taunt you with the realization that there are still another 950,000-odd pages of results to get through before you reach the end. The existence of hyperlinks is enough to convince even the most stubborn positivist that there is always another side to the story. And on the web, fringe believers can always find each other and marinate in their own illusions. The “web world” is too big to ever know. There is always another link. In the era of the Internet, Weinberger argues, facts are not bricks. They are networks. (…)

The most important aspect of Heidegger’s thought for our purposes is his understanding that human beings (or rather “Dasein,” “being-in-the-world”) are always thrown into a particular context, existing within already existing language structures and pre-determined meanings. In other words, the world is like the web, and we, Dasein, live inside the links. (…)

If our starting point is that all knowledge is networked, and always has been, then we are in a far better point to start talking about what makes today’s epistemological infastructure different from the infrastrucure in 1983. But we are also in a position to ask: if all knowledge was networked knowledge, even in 1983, than how did we not behave as if it was so? How did humanity carry on? Why did civilization not collapse into a morass of post-modern chaos? Weinberger’s answer is, once again, McLuhanesque. It was the medium in which knowledge was contained that created the difference. Stable borders around knowledge were built by books.

I would posit a different answer: if knowledge has always been networked knowledge, than facts have never had stable containers. Most of the time, though, we more or less act as if they do. Within philosophical subfield known as Actor-Network Theory (ANT) this “acting-as-if-stability-existed” is referred to as “black boxing.” One of the black boxes around knowledge might very well be the book. But black boxes can also include algorithms, census bureaus, libraries, laboratories, and news rooms. Black boxes emerge out of actually-existing knowledge networks, stabilize for a time, and unravel, and our goal as thinkers and scholars ought to be understanding how these nodes emerge and disappear. In other words, understanding changes to knowledge in this way leaves us far more sensitive to the operations of power than does the notoriously power-free perspective of Marshall McLuhan. (…)

Why don’t I care that the Google results page goes on towards infinity? If we avoid Marshall McLuhan’s easy answers to these complex questions, and retain the core of Heidegger’s brilliant insights while also adding a hefty dose of ontology to his largely immaterial philosophy, we might begin to understand the real operations of digital knowledge/power in a networked age.

Weinberger, however, does not care about power, and more or less admits this himself in a brilliant essay 2008 on the distinction between digital realists, utopians, and dystopians. Digital utopians, a group in which he includes himself, “point to the ways in which the Web has changed some of the basic assumptions about how we live together, removing old obstacles and enabling shiny new possibilities.” The realists, on the other hand, are rather dull: They argue that “the Web hasn’t had nearly as much effect as the utopians and dystopians proclaim. The Web carries with it certain possibilities and limitations, but (the realists say) not many more than other major communications medium.” Politically speaking, digital utopianism tantalizes us with the promise of what might be, and pushes us to do better. The political problem with the realist position, Weinberger argues, is that it “is … [a] decision that leans toward supporting the status quo because what-is is more knowable than what might be.”

The realist position, however, is not necessarily a position of quietude. Done well, digital realism can sensitize us to the fact that all networked knowledge systems eventually become brick walls, that these brick walls are maintained through technological, political, cultural, economic, and organizational forms of power. Our job, as thinkers and teachers, is not to stand back and claim that the all bricks have crumbled. Rather, our job is to understand how the wall gets built, and how we might try to build it differently.”

C.W. Anderson, Ph.D, an assistant professor in the Department of Media Culture at the College of Staten Island (CUNY), researcher at the Columbia University Graduate School of Journalism, The Difference Between Online Knowledge and Truly Open Knowledge, The Atlantic, Feb 3, 2012.

David Weinberger: ‘I think the Net generation is beginning to see knowledge in a way that is closer to the truth about knowledge’

"I think the Net generation is beginning to see knowledge in a way that is closer to the truth about knowledge — a truth we’ve long known but couldn’t instantiate. My generation, and the many generations before mine, have thought about knowledge as being the collected set of trusted content, typically expressed in libraries full of books. Our tradition has taken the trans-generational project of building this Library of Knowledge book by book as our God-given task as humans. Yet, for the coming generation, knowing looks less like capturing truths in books than engaging in never-settled networks of discussion and argument. That social activity — collaborative and contentious, often at the same time — is a more accurate reflection of our condition as imperfect social creatures trying to understand a world that is too big and too complex for even the biggest-headed expert.

This new topology of knowledge reflects the topology of the Net. The Net (and especially the Web) is constructed quite literally out of links, each of which expresses some human interest. If I link to a site, it’s because I think it matters in some way, and I want it to matter that way to you. The result is a World Wide Web with billions of pages and probably trillions of links that is a direct reflection of what matters to us humans, for better or worse. The knowledge networks that live in this new ecosystem share in that property; they are built out of, and reflect, human interest. Like our collective interests, the Web and the knowledge that resides there is at odds and linked in conversation. That’s why the Internet, for all its weirdness, feels so familiar and comfortable to so many of us. And that’s the sense in which I think networked knowledge is more “natural.” (…)

To make a smart room — a knowledge network — you have to have just enough diversity. And it has to be the right type of diversity. Scott Page in The Difference says that a group needs a diversity of perspectives and skill sets if it is going to be smarter than the smartest person in it. It also clearly needs a set of coping skills, norms, and procedures that enable it to deal with diversity productively. (…)

We humans can only see things from a point of view, and we can only understand things by appropriating them into our already-existing context. (…)

In fact, the idea of objectivity arose in response to the limitations of paper, as did so much of our traditional Western idea of knowledge. Paper is a disconnected medium. So, when you write a news story, you have to encapsulate something quite complex in just a relatively small rectangle of print. You know that the reader has no easy way to check what you’re saying, or to explore further on her own; to do so, she’ll have to put down the paper, go to a local library, and start combing through texts that are less current than the newspaper in which your article appears. The reporter was the one mediator of the world the reader would encounter, so the report had to avoid the mediator’s point of view and try to reflect all sides of contentious issues. Objectivity arose to address the disconnected nature of paper.

Our new medium is, of course, wildly connective. Now we can explore beyond the news rectangle just by clicking. There is no longer an imperative to squeeze the world into small, self-contained boxes. Hyperlinks remove the limitations that objectivity was invented to address.

Hyperlinks also enable readers to understand — and thus perhaps discount — the writer’s point of view, which is often a better way of getting past the writer’s prejudices than asking the writer to write as if she or he had none. This, of course, inverts the old model that assumed that if we knew about the journalist’s personal opinions, her or his work would be less credible. Now we often think that the work becomes more credible if the author is straightforward about his or her standpoint. That’s the sense in which transparency is the new objectivity.

There is still value in trying to recognize how one’s own standpoint and assumptions distort one’s vision of the world; emotional and conceptual empathy are of continuing importance because they are how we embody the truth that we share a world with others to home that world matters differently. But we are coming to accept that we can’t really get a view from nowhere, and if we could, we would have no idea what we’re looking at. (…)

Our new ability to know the world at a scale never before imaginable may not bring us our old type of understanding, but understanding and knowledge are not motivated only by the desire to feel that sudden gasp of insight. The opposite and ancient motive is to feel the breath of awe in the face of the almighty unknowability of our universe. A knowing that recognizes its object is so vast that it outstrips understanding makes us more capable of awe. (…)

Technodeterminism is the claim that technology by itself has predictable, determinant effects on people or culture. (…) We still need to be able to discuss how a technology is affecting a culture in general. Generalizations can be a vehicle of truth, so long as they are understood to be only generally true. (…) The new knowledge continues to find generalities that connect individual instances, but because the new ecosystem is hyperlinked, we can go from the generalities back to the individual cases. And those generalizations are themselves linked into a system of difference and disagreement.”

David Weinberger, Ph.D. from the University of Toronto, American technologist, professional speaker, and commentator, interviewed by Rebecca J. Rosen, What the Internet Means for How We Think About the World, The Atlantic, Jan 5 2012.

See also:

To Know, but Not Understand: David Weinberger on Science and Big Data, The Atlantic, Jan 3, 2012 
When science becomes civic: Connecting Engaged Universities and Learning Communities, University of California, Davis, September 11 - 12, 2001
The Filter Bubble: Eli Pariser on What the Internet Is Hiding From You
A story about the Semantic Web (Web 3.0) (video)
Vannevar Bush on the new relationship between thinking man and the sum of our knowledge (1945)
George Lakoff on metaphors, explanatory journalism and the ‘Real Rationality’
The Relativity of Truth - a brief résumé, Lapidarium notes

Apr
11th
Wed
permalink

The Cognitive Limit of Organizations. The structure of a society is connected to its total amount of information
                                               Click image to enlarge

The vertical axis of this slide represents the total stock of information in the world. The horizontal axis represents time.

In the early days, life was simple. We did important things like make spears and arrowheads. The amount of knowledge needed to make these items, however, was small enough that a single person could master their production. There was no need for a large division of labor and new knowledge was extremely precious. If you got new knowledge, you did not want to share it. After all, in a world where most knowledge can fit in someone’s head, stealing ideas is easy, and appropriating the value of the ideas you generate is hard.

At some point, however, the amount of knowledge required to make things began to exceed the cognitive limit of a single human being. Things could only be done in teams, and sharing information among team members was required to build these complex items. Organizations were born as our social skills began to compensate for our limited cognitive skills. Society, however, kept on accruing more and more knowledge, and the cognitive limit of organizations, just like that of the spearmaker, was ultimately reached. (…)

Today, however, most products are combinations of knowledge and intellectual property that resides in different organizations. Our world is less and less about the single pieces of intellectual property and more and more about the networks that help connect these pieces. The total stock of information used in these ecosystems exceeds the capacity of single organizations because doubling the size of huge organizations does not double the capacity of that organization to hold knowledge and put it into productive use.

In a world in which implementing the next generation of ideas will increasingly require pulling resources from different organizations, barriers to collaboration will be a crucial constraint limiting the development of firms. Agility, context, and a strong network are becoming the survival traits where assets, control, and power used to rule. John Seely Brown refers to this as the “Power of Pull.”“

The Cognitive Limit of Organizations, MIT Media Lab, Oct 7, 2011.

Jan
17th
Tue
permalink

The Rise of Complexity. Scientists replicate key evolutionary step in life on earth

                        
         Green cells are undergoing cell death, a cellular division-of-labor—fostering new life.

More than 500 million years ago, single-celled organisms on Earth’s surface began forming multi-cellular clusters that ultimately became plants and animals. (…)

The yeast “evolved” into multi-cellular clusters that work together cooperatively, reproduce and adapt to their environment—in essence, they became precursors to life on Earth as it is today. (…)

The finding that the division-of-labor evolves so quickly and repeatedly in these ‘snowflake’ clusters is a big surprise. (…) The first step toward multi-cellular complexity seems to be less of an evolutionary hurdle than theory would suggest.” (…)

"To understand why the world is full of , including humans, we need to know how one-celled organisms made the switch to living as a group, as multi-celled organisms.” (…)

"This study is the first to experimentally observe that transition," says Scheiner, "providing a look at an event that took place hundreds of millions of years ago." (…)

The scientists chose Brewer’s yeast, or Saccharomyces cerevisiae, a species of yeast used since ancient times to make bread and beer because it is abundant in nature and grows easily.

They added it to nutrient-rich culture media and allowed the cells to grow for a day in test tubes.

Then they used a centrifuge to stratify the contents by weight.

As the mixture settled, cell clusters landed on the bottom of the tubes faster because they are heavier. The biologists removed the clusters, transferred them to fresh media, and agitated them again.

                   
    First steps in the transition to multi-cellularity: ‘snowflake’ yeast with dead cells stained red.

Sixty cycles later, the clusters—now hundreds of cells—looked like spherical snowflakes.

Analysis showed that the clusters were not just groups of random cells that adhered to each other, but related cells that remained attached following cell division.

That was significant because it meant that they were genetically similar, which promotes cooperation. When the clusters reached a critical size, some cells died off in a process known as apoptosis to allow offspring to separate.

The offspring reproduced only after they attained the size of their parents. (…)

                       
     Multi-cellular yeast individuals containing central dead cells, which promote reproduction.

"A cluster alone isn’t multi-cellular," William Ratcliff says. "But when cells in a cluster cooperate, make sacrifices for the common good, and adapt to change, that’s an evolutionary transition to multi-cellularity."

In order for multi-cellular organisms to form, most cells need to sacrifice their ability to reproduce, an altruistic action that favors the whole but not the individual. (…)

For example, all cells in the human body are essentially a support system that allows sperm and eggs to pass DNA along to the next generation.

Thus multi-cellularity is by its nature very cooperative.

"Some of the best competitors in nature are those that engage in cooperation, and our experiment bears that out. (…)

Evolutionary biologists have estimated that multi-cellularity evolved independently in about 25 groups.”

Scientists replicate key evolutionary step in life on earth, Physorg, Jan 16, 2012.

Evolution: The Rise of Complexity

"Let’s rewind time back about 3.5 billion years. Our beloved planet looks nothing like the lush home we know today – it is a turbulent place, still undergoing the process of formation. Land is a fluid concept, consisting of molten lava flows being created and destroyed by massive volcanoes. The air is thick with toxic gasses like methane and ammonia which spew from the eruptions. Over time, water vapor collects, creating our first weather events, though on this early Earth there is no such thing as a light drizzle. Boiling hot acid rain pours down on the barren land for millions of years, slowly forming bubbling oceans and seas. Yet in this unwelcoming, violent landscape, life begins.

The creatures which dared to arise are called cyanobacteria, or blue-green algae. They were the pioneers of photosynthesis, transforming the toxic atmosphere by producing oxygen and eventually paving the way for the plants and animals of today. But what is even more incredible is that they were the first to do something extraordinary – they were the first cells to join forces and create multicellular life. (…)

William Ratcliff and his colleagues at the University of Minnesota. In a PNAS paper published online this week, they show how multicellular yeast can arise in less than two months in the lab. (…)

All of their cultures went from single cells to snowflake-like clumps in less than 60 days. “Although known transitions to complex multicellularity, with clearly differentiated cell types, occurred over millions of years, we have shown that the first crucial steps in the transition from unicellularity to multicellularity can evolve remarkably quickly under appropriate selective conditions,” write the authors. These clumps weren’t just independent cells sticking together for the sake of it – they acted as rudimentary multicellular creatures. They were formed not by random cells attaching but by genetically identical cells not fully separating after division. Furthermore, there was division of labor between cells. As the groups reached a certain size, some cells underwent programmed cell death, providing places for daughter clumps to break from. Since individual cells acting as autonomous organisms would value their own survival, this intentional culling suggests that the cells acted instead in the interest of the group as a whole organism.

Given how easily multicellular creatures can arise in test tubes, it might then come as no surprise that multicellularity has arisen at least a dozen times in the history of life, independently in bacteria, plants and of course, animals, beginning the evolutionary tree that we sit atop today. Our evolutionary history is littered with leaps of complexity. While such intricacies might seem impossible, study after study has shown that even the most complex structures can arise through the meandering path of evolution. In Evolution’s Witness, Ivan Schwab explains how one of the most complex organs in our body, our eyes, evolved. (…)

Eyes are highly intricate machines that require a number of parts working together to function. But not even the labyrinthine structures in the eye present an insurmountable barrier to evolution.

Our ability to see began to evolve long before animals radiated. Visual pigments, like retinal, are found in all animal lineages, and were first harnessed by prokaryotes to respond to changes in light more than 2.5 billion years ago. But the first complex eyes can be found about 540 million years ago, during a time of rapid diversification colloquially referred to as the Cambrian Explosion. It all began when comb jellies, sponges and jellyfish, along with clonal bacteria, were the first to group photoreceptive cells and create light-sensitive ‘eyespots’. These primitive visual centers could detect light intensity, but lacked the ability to define objects. That’s not to say, though, that eyespots aren’t important – eyespots are such an asset that they arose independently in at least 40 different lineages. But it was the other invertebrate lineages that would take the simple eyespot and turn it into something incredible.

According to Schwab, the transition from eyespot to eye is quite small. “Once an eyespot is established, the ability to recognize spatial characteristics – our eye definition – takes one of two mechanisms: invagination (a pit) or evagination (a bulge).” Those pits or bulges can then be focused with any clear material forming a lens (different lineages use a wide variety of molecules for their lenses). Add more pigments or more cells, and the vision becomes sharper. Each alteration is just a slight change from the one before, a minor improvement well within bounds of evolution’s toolkit, but over time these small adjustments led to intricate complexity.

In the Cambrian, eyes were all the rage. Arthropods were visual trendsetters, creating compound eyes by using the latter approach, that of bulging, then combining many little bulges together. One of the era’s top predators, Anomalocaris, had over 16,000 lenses! So many creatures arose with eyes during the Cambrian that Andrew Parker, a visiting member of the Zoology Department at the University of Oxford, believes that the development of vision was the driver behind the evolutionary explosion. His ‘Light-Switch’ hypothesis postulates that vision opened the doors for animal innovation, allowing rapid diversification in modes and mechanisms for a wide set of ecological traits. Even if eyes didn’t spur the Cambrian explosion, their development certainly irrevocably altered the course of evolution.

                          
                     Fossilized compound eyes from Cambrian arthropods (Lee et al. 2011)

Our eyes, as well as those of octopuses and fish, took a different approach than those of the arthropods, putting photo receptors into a pit, thus creating what is referred to as a camera-style eye. In the fossil record, eyes seem to emerge from eyeless predecessors rapidly, in less than 5 million years. But is it really possible that an eye like ours arose so suddenly? Yes, say biologists Dan-E. Nilsson and Susanne Pelger. They calculated a pessimistic guess as to how long it would take for small changes – just 1% improvements in length, depth, etc per generation – to turn a flat eyespot into an eye like our own. Their conclusion? It would only take about 400,000 years – a geological instant.

How does complexity arise in the first place

But how does complexity arise in the first place? How did cells get photoreceptors, or any of the first steps towards innovations such as vision? Well, complexity can arise a number of ways.

Each and every one of our cells is a testament to the simplest way that complexity can arise: have one simple thing combine with a different one. The powerhouses of our cells, called mitochondria, are complex organelles that are thought to have arisen in a very simple way. Some time around 3 billion years ago, certain bacteria had figured out how to create energy using electrons from oxygen, thus becoming aerobic. Our ancient ancestors thought this was quite a neat trick, and, as single cells tend to do, they ate these much smaller energy-producing bacteria. But instead of digesting their meal, our ancestors allowed the bacteria to live inside them as an endosymbiont, and so the deal was struck: our ancestor provides the fuel for the chemical reactions that the bacteria perform, and the bacteria, in turn, produces ATP for both of them. Even today we can see evidence of this early agreement – mitochondria, unlike other organelles, have their own DNA, reproduce independently of the cell’s reproduction, and are enclosed in a double membrane (the bacterium’s original membrane and the membrane capsule used by our ancestor to engulf it).

Over time the mitochondria lost other parts of their biology they didn’t need, like the ability to move around, blending into their new home as if they never lived on their own. The end result of all of this, of course, was a much more complex cell, with specialized intracellular compartments devoted to different functions: what we now refer to as a eukaryote.

Complexity can arise within a cell, too, because our molecular machinery makes mistakes. On occasion, it duplicates sections of DNA, entire genes, and even whole chromosomes, and these small changes to our genetic material can have dramatic effects. We saw how mutations can lead to a wide variety of phenotypic traits when we looked at how artificial selection has shaped dogs. These molecular accidents can even lead to complete innovation, like the various adaptations of flowering plants that I talked about in my last Evolution post. And as these innovations accumulate, species diverge, losing the ability to reproduce with each other and filling new roles in the ecosystem. While the creatures we know now might seem unfathomably intricate, they are the product of billions of years of slight variations accumulating.

Of course, while I focused this post on how complexity arose, it’s important to note that more complex doesn’t necessarily mean better. While we might notice the eye and marvel at its detail, success, from the viewpoint of an evolutionary lineage, isn’t about being the most elaborate. Evolution only leads to increases in complexity when complexity is beneficial to survival and reproduction.

Indeed, simplicity has its perks: the more simple you are, the faster you can reproduce, and thus the more offspring you can have. Many bacteria live happy simple lives, produce billions of offspring, and continue to thrive, representatives of lineages that have survived billions of years. Even complex organisms may favor less complexity – parasites, for example, are known for their loss of unnecessary traits and even whole organ systems, keeping only what they need to get inside and survive in their host. Darwin referred to them as regressive for seemingly violating the unspoken rule that more complex arises from less complex, not the other way around. But by not making body parts they don’t need, parasites conserve energy, which they can invest in other efforts like reproduction.

When we look back in an attempt to grasp evolution, it may instead be the lack of complexity, not the rise of it, that is most intriguing.”

See also:

Scientists recreate evolution of complexity using ‘molecular time travel’
Nature Has A Tendency To Reduce Complexity
Emergence and Complexity - prof. Robert Sapolsky’s lecture, Stanford University (video)

Dec
27th
Tue
permalink

'To understand is to perceive patterns'

                  

"Everything we care about lies somewhere in the middle, where pattern and randomness interlace."

James Gleick, The Information: A History, a Theory, a Flood, Pantheon, 2011

"Humans are pattern-seeking story-telling animals, and we are quite adept at telling stories about patterns, whether they exist or not."

Michael Shermer

"The pattern, and it alone, brings into being and causes to pass away and confers purpose, that is to say, value and meaning, on all there is. To understand is to perceive patterns. (…) To make intelligible is to reveal the basic pattern.”

Isaiah Berlin, British social and political theorist, philosopher and historian, (1909-1997), The proper study of mankind: an anthology of essays, Chatto & Windus, 1997, p. 129.

"One of the most wonderful things about the emerging global superbrain is that information is overflowing on a scale beyond what we can wrap our heads around. The electronic, collective, hive mind that we know as the Internet produces so much information that organizing this data — and extracting meaning from it — has become the conversation of our time.

Sanford Kwinter’s Far From Equilibrium tackles everything from technology to society to architecture under the thesis that creativity, catharsis, transformation and progressive breakthroughs occur far from equilibrium. So even while we may feel overwhelmed and intimidated by the informational overload and radical transformations of our times, we should, perhaps, take refuge in knowing that only good can come from this. He writes:

“(…) We accurately think of ourselves today not only as citizens of an information society, but literally as clusters of matter within an unbroken informational continuum: "We are all," as the great composer Karlheinz Stockhausen once said, "transistors, in the literal sense. We send, receive and organize [and] so long as we are vital, our principle work is to capture and artfully incorporate the signals that surround us.” (…)

Clay Shirky often refers to the “Cognitive Surplus,” the overflowing output of the billion of minds participating in the electronic infosphere. A lot of this output is silly, but a lot of it is meaningful and wonderful. The key lies in curation; which is the result of pattern-recognition put into practice. (…)

Matt Ridley’s TED Talk, “When Ideas Have Sex” points to this intercourse of information and how it births new thought-patterns. Ideas, freed from the confines of space and time by the invisible, wireless metabrain we call The Internet, collide with one another and explode into new ideas; accelerating the collective intelligence of the species. Creativity thrives when minds come together. The last great industrial strength creative catalyst was the city: It is no coincidence than when people migrate to cities in large numbers, creativity and innovation thrives.  

Now take this very idea and apply it to the web:  the web  essentially is a planetary-scale nervous system where individual minds take on the role of synapses, firing electrical pattern-signals to one another at light speed — the net effect being an astonishing increase in creative output. (…)

Ray Kurzweil too, expounds on this idea of the power of patterns:

“I describe myself as a patternist, and believe that if you put matter and energy in just the right pattern you create something that transcends it. Technology is a good example of that: you put together lenses and mechanical parts and some computers and some software in just the right combination and you create a reading machine for the blind. It’s something that transcends the semblance of parts you’ve put together. That is the nature of technology, and it’s the nature of the human brain.

Biological molecules put in a certain combination create the transcending properties of human intelligence; you put notes and sounds together in just the rightcombination, and you create a Beethoven symphony or a Beatles song. So patterns have a power that transcends the parts of that pattern.”

R. Buckminster Fuller refers to us as “pattern integrities.” “Understanding order begins with understanding patterns,” he was known to say E.J. White, who worked with Fuller, says that:

“For Fuller, the thinking process is not a matter of putting anything into the brain or taking anything out; he defines thinking as the dismissal of irrelevancies, as the definition of relationships” — in other words, thinking is simultaneously a form of filtering out the data that doesn’t fit while highlighting the things that do fit together… We dismiss whatever is an “irrelevancy” and retain only what fits, we form knowledge by ‘connecting the dots’… we understand things by perceiving patterns — we arrive at conclusions when we successfully reveal these patterns. (…)

Fuller’s primary vocation is as a poet. All his disciplines and talents — architect, engineer, philosopher, inventor, artist, cartographer, teacher — are just so many aspects of his chief function as integrator… the word “poet" is a very general term for a person who puts things together in an era of great specialization when most people are differentiating or taking things apart… For Fuller, the stuff of poetry is the patterns of human behavior and the environment, and the interacting hierarchies of physics and design and industry. This is why he can describe Einstein and Henry Ford as the greatest poets of the 20th century.” (…)

In a recent article in Reality Sandwich, Simon G Powell proposed that patterned self-organization is a default condition of the universe: 

“When you think about it, Nature is replete with instances of self-organization. Look at how, over time, various exquisitely ordered patterns crystallise out of the Universe. On a macroscopic scale you have stable and enduring spherical stars, solar systems, and spiral galaxies. On a microscopic scale you have atomic and molecular forms of organization. And on a psychological level, fed by all this ambient order and pattern, you have consciousness which also seems to organise itself into being (by way of the brain). Thus, patterned organisation of one form or another is what nature is proficient at doing over time

This being the case, is it possible that the amazing synchronicities and serendipities we experience when we’re doing what we love, or following our passions — the signs we pick up on when we follow our bliss- represent an emerging ‘higher level’ manifestation of self-organization? To make use of an alluring metaphor, are certain events and cultural processes akin to iron filings coming under the organising influence of a powerful magnet? Is serendipity just the playing out on the human level of the same emerging, patterned self-organization that drives evolution?

Barry Ptolemy's film Transcendent Man reminds us that the universe has been unfolding in patterns of greater complexity since the beginning of time. Says Ptolemy:

First of all we are all patterns of information. Second, the universe has been revealing itself as patterns of information of increasing order since the big bang. From atoms, to molecules, to DNA, to brains, to technology, to us now merging with that technology. So the fact that this is happening isn’t particularly strange to a universe which continues to evolve and unfold at ever accelerating rates.”

Jason Silva, Connecting All The Dots - Jason Silva on Big think, Imaginary Fundation, Dec 2010

"Networks are everywhere. The brain is a network of nerve cells connected by axons, and cells themselves are networks of molecules connected by biochemical reactions. Societies, too, are networks of people linked by friendships, familial relationships and professional ties. On a larger scale, food webs and ecosystems can be represented as networks of species. And networks pervade technology: the Internet, power grids and transportation systems are but a few examples. Even the language we are using to convey these thoughts to you is a network, made up of words connected by syntactic relationships.”

'For decades, we assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together. In the past decade, an avalanche of research has shown that many real networks, independent of their age, function, and scope, converge to similar architectures, a universality that allowed researchers from different disciplines to embrace network theory as a common paradigm.”

Albert-László Barabási , physicist, best known for his work in the research of network theory, and Eric Bonabeau, Scale-Free Networks, Scientific American, April 14, 2003.

Coral reefs are sometimes called “the cities of the sea”, and part of the argument is that we need to take the metaphor seriously: the reef ecosystem is so innovative because it shares some defining characteristics with actual cities. These patterns of innovation and creativity are fractal: they reappear in recognizable form as you zoom in and out, from molecule to neuron to pixel to sidewalk. Whether you’re looking at original innovations of carbon-based life, or the explosion of news tools on the web, the same shapes keep turning up. (…) When life gets creative, it has a tendency to gravitate toward certain recurring patterns, whether those patterns are self-organizing, or whether they are deliberately crafted by human agents.”

— Steven Johnson, author of Where Good Ideas Come From, cited by Jason Silva

"Network systems can sustain life at all scales, whether intracellularly or within you and me or in ecosystems or within a city. (…) If you have a million citizens in a city or if you have 1014 cells in your body, they have to be networked together in some optimal way for that system to function, to adapt, to grow, to mitigate, and to be long term resilient."

Geoffrey West, British theoretical physicist, The sameness of organisms, cities, and corporations: Q&A with Geoffrey West, TED, 26 July 2011.

“Recognizing this super-connectivity and conductivity is often accompanied by blissful mindbody states and the cognitive ecstasy of multiple “aha’s!” when the patterns in the mycelium are revealed. That Googling that has become a prime noetic technology (How can we recognize a pattern and connect more and more, faster and faster?: superconnectivity and superconductivity) mirrors the increased speed of connection of thought-forms from cannabis highs on up. The whole process is driven by desire not only for these blissful states in and of themselves, but also as the cognitive resource they represent.The devices of desire are those that connect,” because as Johnson says “chance favors the connected mind”.

Google and the Myceliation of Consciousness, Reality Sandwich, 10-11-2007

Jason Silva, Venezuelan-American television personality, filmmaker, gonzo journalist and founding producer/host for Current TV, To understand is to perceive patterns, Dec 25, 2011 (Illustration: Color Blind Test)

[This note will be gradually expanded]

See also:

The sameness of organisms, cities, and corporations: Q&A with Geoffrey West, TED, 26 July 2011.
☞ Albert-László Barabási and Eric Bonabeau, Scale-Free Networks, Scientific American, April 14, 2003.
Google and the Myceliation of Consciousness, Reality Sandwich, 10.11.2007
The Story of Networks, Lapidarium notes
Geoffrey West on Why Cities Keep Growing, Corporations and People Always Die, and Life Gets Faster
☞ Manuel Lima, visualcomplexity.com, A visual exploration on mapping complex networks
Constructal theory, Wiki
☞ A. Bejan, Constructal theory of pattern formation (pdf), Duke University
Pattern recognition, Wiki
Patterns tag on Lapidarium
Patterns tag on Lapidarium notes

Nov
23rd
Wed
permalink

The maps of the Internet

                                                          Click image to enlarge

The Opte Project was created to make a visual representation of a space that is very much one-dimensional, a metaphysical universe. The data represented and collected here serves a multitude of purposes: Modeling the Internet, analyzing wasted IP space, IP space distribution, detecting the result of natural disasters, weather, war, and esthetics/art.

"Within two weeks the self-described technologist and entrepreneur Barrett Lyon had created a program that could output a detailed visualization of Internet connectivity in a few hours. Seven years and billions more Internet-connected devices later, Lyon is still at it. This cosmic-looking image, one of his newest creations, traces the millions of routes along which data can travel and pinpoints the hubs receiving the most traffic. Internet giants such as AT&T and Google manage the most heavily used networks, which appear here as glowing yellow orbs; they tend to concentrate in the center of the sphere. The less popular local networks (red) sit on the periphery. Although Lyon’s visualizations have appeared in computing textbooks and at the Museum of Modern Art in New York.”

The Internet Looks Like a Fractal Dandelion, DISCOVER Magazine, Nov 11, 2011

                                                         Click image to enlarge

"This map is built off of our database using two different graphing engines: Large Graph Layout (LGL) by Alex Adai and Graphviz by Peter North at AT&T Labs Research.

This graph is by far our most complex. It is using over 5 million edges and has an estimated 50 million hop count.
Graph Colors:
Asia Pacific - Red
Europe/Middle East/Central Asia/Africa - Green
North America - Blue
Latin American and Caribbean - Yellow
RFC1918 IP Addresses - Cyan
Unknown - White
Date: Nov 22 2003

Today the image has been used free of charge across the globe and is part of the permanent collection at The Museum of Modern Art (MoMA) and the Boston Museum of Science. It has been used in countless books, media, and even movies.”

The Opte Project

Internet Mapping Project

                                                              Click image to enlarge

Image colored by IP address in 16 August 1998. More: The Internet Mapping Project.

See also:

The Cooperative Association for Internet Data Analysis
Cyber Geography Research
The Rocketfuel ISP topology mapping engine

Nov
3rd
Thu
permalink

The ‘rich club’ that rules your brain

        
The connectome with its 12 “rich club” hubs. Green means fewer connections, red means more connections (Image: Martijn van den Heuvel/University Medical Center in Utrecht)

"Not all brain regions are created equal – instead, a “rich club” of 12 well-connected hubs orchestrates everything that goes on between your ears. This elite cabal could be what gives us consciousness, and might be involved in disorders such as schizophrenia and Alzheimer’s disease.

As part of an ongoing effort to map the human “connectome” – the full network of connections in the brain – Martijn van den Heuvel of the University Medical Center in Utrecht, the Netherlands, and Olaf Sporns of Indiana University Bloomington scanned the brains of 21 people as they rested for 30 minutes.

The researchers used a technique called diffusion tensor imaging to track the movements of water through 82 separate areas of the brain and their interconnecting neurons. They found 12 areas of the brain had significantly more connections than all the others, both to other regions and among themselves.

"These 12 regions have twice the connections of other brain regions, and they’re more strongly connected to each other than to other regions," says Van den Heuvel. “If we wanted to look for consciousness in the brain, I would bet on it turning out to be this rich club,” he adds.

Members of the elite

The elite group consists of six pairs of identical regions, with one of each pair in each hemisphere of the brain. Each member is known to accept only preprocessed, high-order information, rather than raw incoming sensory data.

Best connected of all is the precuneus, an area at the back of the brain. Van den Heuvel says its function is not well understood, but thinks that it acts as an “integrator region” collating high-level information from all over the brain.

Another prominent hub is the superior frontal cortex, which plans actions in response to events and governs where you should focus your attention. The superior parietal cortex – the third hub – is linked to the visual cortex and registers where different objects in your immediate vicinity are.

To bring memory into the equation, the hippocampus is another hub – that’s where memories are processed, stored and consolidated. The fifth member of the club is the thalamus, which, among other things, interlinks visual processes; the last member, the putamen, coordinates movement.

Together the hubs enable the brain to constantly assess, prioritise and filter incoming information, and then puts it all together to make decisions about what to do next.

This network makes the way the brain functions more robust overall, but it could also leave the entire system vulnerable to breakdown if key hubs are damaged or disabled, says Van den Heuvel.

Downfall of the rich

After mapping the connections, Van den Heuvel’s team manipulated the data to see what might happen if parts of the rich club were damaged. The simulated brain lost three times as much function if the elite hubs were taken out than if random parts of the brain were lost.

"If [one of these] regions goes down, it can take the others down too, just like when banks failed in the global economic crisis,” says Van den Heuvel. (…)

"The human brain is extraordinarily complex, yet it works efficiently, and a major challenge has been to discover principles of brain wiring and organisation that explain this," says Randy Buckner, a neuroscientist at Harvard University.

"What Van den Heuvel and Sporns show is that some regions of the brain are embedded in densely connected networks – so-called rich clubs – that may act together as a functional unit," says Buckner. "Such an organisation might help explain how complex networks of brain regions can work together efficiently.""

Andy Coghlan, The ‘rich club’ that rules your brain, New Scientist, 2 Nov 2011

See also:

Human Connectome Project - understanding how different parts of the brain communicate to each other
Revealed – the capitalist network that runs the world, New Scientist, Oct 19, 2011

Jul
26th
Tue
permalink

Minority rules: Scientists discover tipping point for the spread of ideas

“The same mathematics of networks that governs the interactions of molecules in a cell, neurons in a brain, and species in an ecosystem can be used to understand the complex interconnections between people, the emergence of group identity, and the paths along which information, norms, and behavior spread from person to person to person.” James Fowler is a political scientist at the University of California

"Scientists at Rensselaer Polytechnic Institute have found that when just 10 percent of the population holds an unshakable belief, their belief will always be adopted by the majority of the society. The scientists, who are members of the Social Cognitive Networks Academic Research Center (SCNARC) at Rensselaer, used computational and analytical methods to discover the tipping point where a minority belief becomes the majority opinion. The finding has implications for the study and influence of societal interactions ranging from the spread of innovations to the movement of political ideals.

"When the number of committed opinion holders is below 10 percent, there is no visible progress in the spread of ideas. It would literally take the amount of time comparable to the age of the universe for this size group to reach the majority," said SCNARC Director Boleslaw Szymanski, the Claire and Roland Schmitt Distinguished Professor at Rensselaer. "Once that number grows above 10 percent, the idea spreads like flame."

         

In this visualization, we see the tipping point where minority opinion (shown in red) quickly becomes majority opinion. Over time, the minority opinion grows. Once the minority opinion reached 10 percent of the population, the network quickly changes as the minority opinion takes over the original majority opinion (shown in green). Credit: SCNARC/Rensselaer Polytechnic Institute

As an example, the ongoing events in Tunisia and Egypt appear to exhibit a similar process, according to Szymanski. “In those countries, dictators who were in power for decades were suddenly overthrown in just a few weeks.”

The findings were published in the July 22, 2011, early online edition of the journal Physical Review E in an article titled Social consensus through the influence of committed minorities.”

An important aspect of the finding is that the percent of committed opinion holders required to shift majority opinion does not change significantly regardless of the type of network in which the opinion holders are working. In other words, the percentage of committed opinion holders required to influence a society remains at approximately 10 percent, regardless of how or where that opinion starts and spreads in the society.

To reach their conclusion, the scientists developed computer models of various types of social networks. One of the networks had each person connect to every other person in the network. The second model included certain individuals who were connected to a large number of people, making them opinion hubs or leaders. The final model gave every person in the model roughly the same number of connections. The initial state of each of the models was a sea of traditional-view holders. Each of these individuals held a view, but were also, importantly, open minded to other views.

Once the networks were built, the scientists then “sprinkled” in some true believers throughout each of the networks. These people were completely set in their views and unflappable in modifying those beliefs. As those true believers began to converse with those who held the traditional belief system, the tides gradually and then very abruptly began to shift.

"In general, people do not like to have an unpopular opinion and are always seeking to try locally to come to consensus. We set up this dynamic in each of our models," said SCNARC Research Associate and corresponding paper author Sameet Sreenivasan. To accomplish this, each of the individuals in the models “talked” to each other about their opinion. If the listener held the same opinions as the speaker, it reinforced the listener’s belief. If the opinion was different, the listener considered it and moved on to talk to another person. If that person also held this new belief, the listener then adopted that belief.

"As agents of change start to convince more and more people, the situation begins to change," Sreenivasan said. “People begin to question their own views at first and then completely adopt the new view to spread it even further. If the true believers just influenced their neighbors, that wouldn’t change anything within the larger system, as we saw with percentages less than 10.”

The research has broad implications for understanding how opinion spreads. “There are clearly situations in which it helps to know how to efficiently spread some opinion or how to suppress a developing opinion,” said Associate Professor of Physics and co-author of the paper Gyorgy Korniss. “Some examples might be the need to quickly convince a town to move before a hurricane or spread new information on the prevention of disease in a rural village.”“

Minority rules: Scientists discover tipping point for the spread of ideas, EurekaAlert, 25 July 2011

See also:

The Story of Networks
☞ Manuel Castells, Network Theories of Power - video lecture, USCAnnenberg

Feb
16th
Wed
permalink

Genes and social networks: new research links genes to friendship networks

James Fowler, a professor at UC-San Diego, is engaged in highly innovative and important research at the crossroads of political science and biology. His recent paper in the Proceedings of the National Academy of Sciences, “Correlated Genotypes in Friendship Networks”, represents an important new study in an emerging research field that is exploring the genetic and biological foundations for our political and social behavior. (…)

Genotypic clustering in social networks”, by statistically examining the association between markers for six different genes and the reported friendship networks from respondents in data from the National Longitudinal Study of Adolescent Health and the Framingham Heart Study Social Network. They show that one of these genes (DRD2) is positively associated with in friendship networks, meaning that those who have this gene are more likely to be friends with others who have this gene, controlling for demographic similarities and population stratification; another gene, CYP2A6 has a negative association in friendship networks. (…)

What is the most important implication of demonstrating that specific genes are associated with who we affiliate with in our friendship networks?

James Fowler: What happens to us may depend not only on our own genes but also on the genes of our friends. This has been shown already in hens, whose feathers change depending on the genetic constitution of the hens that are caged near them. But something similar may happen in humans. We each live in a sea of the genes of others. In fact, we are metagenomic. (…)

An important caveat is that there may be processes besides friendship choice that create correlated genotypes. Our genes may cause us to be drawn to certain environments where we are more likely to meet similar people. For example, people with the same DRD2 genotype might both find themselves in a bar where they then become friends. But this cannot explain *negative* correlation. The “opposites attrac” result with CYP2A6 is more likely to be due to friendship choice. (…)

But it is true there can be feedback effects — our genes not only influence us, but they may influence the genes of our friends, which in turn has an additional effect on us. For example, the DRD2 gene variant we study has been associated with alcoholism, and if you have this gene variant, your friends are likely to have it, too. So you are not only more susceptible to alcoholism yourself, but you are likely to be surrounded by friends who are susceptible, too. Thus, ignoring genes means we might miss important heterogeneity in the network that would obscure strong social effects. (…)

We have discovered some regularities in our studies of human social networks that suggest their structure may be universal, such as the tendency for many of our friends also to be friends with one another, and the tendency for influence to spread to about three degrees of separation. We conjecture that we have coevolved with these networks as our brains have gotten bigger, and genetic variation might give us a clue about which systems have undergone the most recent evolutionary changes.”

A conversation with James Fowler by R. Michael Alvarez, Genes and social networks: new research links genes to friendship networks, Psychology Today, February 14, 2011. See also: Daniel MacArthur, On sharing genes with friends, Wired, Jan 19, 2011

Sep
28th
Tue
permalink

The Story of Networks


                                                  (Illustration: Creative Networking)

“The same mathematics of networks that governs the interactions of molecules in a cell, neurons in a brain, and species in an ecosystem can be used to understand the complex interconnections between people, the emergence of group identity, and the paths along which information, norms, and behavior spread from person to person to person.”

James Fowler answering the question "If you only had a single statement to pass on to others summarizing the most vital lesson to be drawn from your work, what would it be?" in Starting Over, SEED, Aprill 22, 2011.

Seven Bridges of Königsberg

“It all starts in Königsberg, now Kalingrad, a small strip of Russian territory. In the 18th century, the philosopher Immanuel Kant, lived there and was famous for taking walks so regularly that it was said that people could set their clocks by him.

Most likely, on these walks, he would encounter one of its seven famous bridges.

                   
When Kant was still a young boy, the bridges had become the center of a popular riddle: Is it possible to walk across all seven bridges without crossing the same one twice? It was an enigma that defied an easy solution until it caught the eye of the Leonhard Euler, the greatest mathematician of that age.

To solve the Königsberg bridge problem, Euler developed a new type of mathematics called graph theory. He designated the four land masses that the bridges connected as nodes and the bridges themselves as links.

                                     

From there it was fairly easy to see that the only way someone could walk across all the bridges only once would be if there were an even number of bridges. It was a nice trick, but at the time, nobody realized how important Euler’s invention of graph theory would become.

Random Networks

One of the people who got interested in graphs was Paul Erdős.

Erdős was famous for showing up at mathematicians doors and announcing “my brain is open” (meaning that he was ready to collaborate). He did this so often, that mathematicians often rank themselves by their Erdős number, or how many links away they are from collaborating with him.

What Erdős realized is that if networks develop randomly, they are highly efficient. Even with a lot of nodes, you need relatively few links. Moreover, the larger the network, the less links you need, proportionately, to connect everything together.

The Milgram Small World Experiment: 6 Degrees of Separation

In 1967, the psychologist Stanley Milgram randomly selected people living in Wichita, Kansas and Omaha, Nebraska and asked them to get a letter to a stockbroker in Boston they had never met. This became known as the small world experiment.

The subjects were given no information except the man’s name and occupation and were only allowed to send the package to people they knew on a first name basis. Amazingly, the letters got there in about six steps on average. Just six relationships separated people across an entire continent!

(More modern e-mail experiments have confirmed most of Milgram’s findings).

Just as Erdős predicted, even in the huge network of people comprising the entire USA, it took an amazingly small amount of links to connect them all. There seemed to be mysterious forces at work that bind disparate parts into a coherent whole.

The Strength of Weak Ties

Mark Granovetter, a sociologist, was aware of Milgram’s work and decided to study the matter further. In the late 1960’s and Early 1970’s, he began studying how people found jobs in communities around Boston.

He soon found that successful job searches revolved around a strange combination of acquaintance and chance. Granovetter found that over 80% of the people in his study who found a job through a contact did not have a close relationship with that person.

Our friends have a lot more friends than we do, so we’ll often find what we’re looking for through the friends of our friends (besides, we share so much of our experiences with those close to us that they tend to have the same information we do). Granovetter called this phenomenon, The Strength of Weak Ties (pdf).”

The Story of Networks, Digital Tonto, Sep 26, 2010

From mapping systems to controlling them


A Universe of Hubs: Mauro Martino, of Barabasi’s lab, illustrates how hubs act as an organizing principle within complex networks by plotting the 325,729 Web pages in the University of Notre Dame Web domain [green points]. He also mapped the 1,497,134 links that connect those pages [white lines]; for clarity, he showed only the strongest connections. Pages with many connections are hubs. Less-connected nodes cluster around them like planets gather around a star Mauro Martino/Barabási Lab

"In 1736 the Swiss mathematician Leonhard Euler ended a debate among the citizens of Königsberg, Prussia, by drawing a graph. The Pregel River divided the city, now Kaliningrad, Russia, into four sections. Seven bridges connected them. Could a person cross all seven without walking over the same one twice?

Euler began with a map that cleared away everything—the homes and streets and coffeehouses—irrelevant to the question at hand. Then he translated that map into something even more abstract, a depiction not of a physical place but of an interconnected system. The four sections became dots, and the seven bridges became lines. By transforming Königsberg into simple nodes and edges (as mathematicians have come to call such abstractions), Euler could subject the system to mathematical analysis. In doing so, he proved that a person could not cross all seven bridges without walking over the same one twice. More important, he mapped a network for the first time.

Over the next two centuries, scientists built on Euler’s work to develop graph theory, a branch of mathematics that would eventually serve as the basis for network science. But it wasn’t until 1959—when the Hungarian mathematicians Paul Erdös and Alfréd Rényi proposed a means by which complex networks evolve—that a defined theory of networks began to emerge. And it was only in the mid-1990s that scientists began to apply that theory to really complex problems. Before then, large data sets were difficult to obtain and even more difficult to process. But as data became more accessible and processing power cheaper, researchers began applying graph theory to everything from protein interactions to the workings of the power grid.

Albert-László Barabási a Romanian-born physicist at the University of Notre Dame, was one of those researchers. In the past decade and a half, has transformed the way his colleagues understand networks at least twice. His theories have influenced important developments in engineering, marketing, medicine and spycraft. And his research may soon allow engineers, marketers, doctors and spies to not just understand and predict network behavior, but also to control it. (…)

Barabási mapped several other large and complex systems, including the connections between transistors on computer chips and the collaborations between actors in Hollywood. In each case, highly linked nodes, which he called hubs, were the defining characteristic of the network, not just an anomaly but an organizing principle for engineered and natural systems alike. With his student Réka Albert, Barabási updated the Erdős–Rényi model to reflect the existence of hubs in real-world networks. In doing so, he created a tool for scientists to map and explore all manner of complex systems in ways they had never thought to before. (…)

Engineers use control theory to predict how systems will respond to various inputs, which in turn helps them make robots that can catch baseballs, cars that take sharp corners with ease, and planes that don’t fall from the sky. (…)

Like prediction, control required evaluating an object as a system with nodes of varying importance. A car for instance: “It is made of 5,000 components,” Barabási says, “yet you as a driver have access to only three to five nodes”—the steering wheel, the gas pedal, the brake, and maybe the clutch and shifter. “With those three to five knobs, you can make this system go anywhere a car can go.” What he wanted to know was if he could look at any network, not just engineered ones, and find those control nodes. Among the thousands of proteins operating within a cell, could he find the steering wheel, the gas pedal and the brake? (…)

Control nodes take instructions or signals from outside the network (for example, a foot on the gas pedal) and transmit them to nodes within the network (the fuel-injection system). To find them, Liu borrowed an algorithm, developed by Erdös and Rényi five decades prior, that acts as a signal moving through the network. It starts at one node and follows a random edge to another node, at which point it “erases” every other edge but the one it came in on and the one it will go out on. The algorithm runs through the entire network over and over until it finds the minimum set of starting points needed to reach every node in the system. Control these starting points, and you control the entire network. (…)

Whereas the neuronal map of C. elegansis complete, scientists have determined only about 5 percent of the connections in the yeast cell’s gene network. The more data scientists feed into the model, the better they can map connections in the network and the fewer control nodes they might need to operate the system. “We know these maps are incomplete,” Barabási says. “But they’re getting richer every day.” He also says his theory applies to total control of a network. Scientists who want partial control—say, to elicit a particular protein expression within a cell—would need to master far fewer nodes. (…)

“What we have to realize is that control is a natural progression of understanding processes,” he says. “But control is a question of will, and will can be controlled by laws. We have to come together as a society to figure out how far we can push it.”

Gregory Mone, This Man Could Rule the World - How Albert-László Barabási went from mapping systems to controlling them, 11.02.2011.

The hidden influence of social networks: Nicholas Christakis on TED.com

"We’re all embedded in vast social networks of friends, family, co-workers and more. Nicholas Christakis tracks how a wide variety of traits — from happiness to obesity — can spread from person to person, showing how your location in the network might impact your life in ways you don’t even know.”

Nicholas Christakis, Greek American physician and sociologist known for his research on social networks and on the socioeconomic and biosocial determinants of health, longevity, and behavior. Speech at TED2010, February 2010 in Long Beach, CA.

Manuel Lima: The Power of Networks. Mapping an increasingly complex world | TED

Manuel Lima is a Fellow of the Royal Society of Arts, nominated by Creativity magazine as “one of the 50 most creative and influential minds of 2009”, Manuel Lima is a Senior UX Design Lead at Microsoft Bing and founder of VisualComplexity.com - A visual exploration on mapping complex networks. TEDxBuenosAires, April 2011

The Power of Networks — Animated by RSA

See also:

☞ C. J. Stam, J. C. Reijneveld, Graph theoretical analysis of complex networks in the brain, Nonlinear Biomedical Physics, 2007 (research paper)

"Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the ‘synchronizability’ of complex networks of coupled oscillators can be determined by graph spectral analysis.

These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity.

This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer’s disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.”

Nicholas Christakis: How social networks predict epidemics, TED video, June 2010
Minority rules: Scientists discover tipping point for the spread of ideas
The ‘rich club’ that rules your brain
☞ Eshel Ben-Jacob, Learning from Bacteria about Social Networks, Google Tech Talk, Sept 30, 2011 Video
Genes and social networks: new research links genes to friendship networks
☞ Manuel Castells, Network Theories of Power - video lecture, USCAnnenberg
Networks tag on Lapidarium