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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
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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

Dec
29th
Thu
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Edward Glaeser: ‘Cities Are Making Us More Human’
     
Illustration: “The elevated sidewalk: How it will solve city transportation problems”, Scientific American, vol. 109 (July-Dec 1913)

"As opposed to the conventional wisdom, Harvard economist Edward Glaeser believes urbanization to be a solution to many unanswered problems, such as pollution, depression and a lack of creativity. (…)

Living around trees and living in low density areas may end being actually quite harmful for the environment, whereas living in high-rise buildings and urban core may end up being quite kind to the environment. (…)

People who live in urban apartments all typically use less electricity at home and less energy at home heating than people who live in larger suburban or rural homes. A single family detached house uses on average 83% more electricity than urban apartments do within the United States. (…)

Q: How are cities making us smarter?

Glaeser: I think the most important thing cities do today is to allow the creation of new ideas. Chains of collaborative brilliance have always been responsible for human kind’s greatest hits. We have seen this in cities for millennia – Socrates and Plato bickered on an Athenian street corner; we saw it again in Florence with the ideas that went from Brunelleschi to Donatello to Masaccio to Filippino Lippi and to the Florentine Renaissance. It helps us to know each other, learn from each other and to collectively create something great. In some sense, cities are making us more human.

Our greatest asset as a species is the ability to learn from the people around us. We come out of the womb with this remarkable ability to take in information from those people – parents, peers, teachers – that are near us. Cities enable us to get smart by being around other smart people. I think this explains why cities have not become obsolete over the past thirty years. (…) We have just crossed the half-way point where more than 50% of humanity lives in cities. (…)

                   Source: Ethan Zuckerman, Desperately Seeking Serendipity, 12.V.2011

These facts are related to the role cities play today, a role very much tied to the generation of information. Globalization and new technologies did make the industrial city obsolete, at least in the West. But they also increased the idea of returns of human capital and innovation. You could sell something on the other side of the planet because you could produce it on the other side of the planet. By making knowledge more valuable, they made cities more important. That is why they continue to play the incredibly important role of connecting people, enabling them to learn from one another at close distances. (…)

I also want to emphasize that cities are often places of significant and often positive political change. One thing that those countries need is political change, which is much more likely to come out of an organized urban group than it is to come from a dispersed agricultural population. (…)

If you compare countries that are more than 50% urbanized with countries that are less than 50% urbanized, incomes are five times higher in the more urbanized countries and infant mortality rates are less than a third in the more urbanized countries. The path of rural poverty really is awful. (…)”

Edward Glaeser, economist at Harvard University, "Cities Are Making Us More Human", The European, 20.12.2011.

Nov
6th
Sun
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Fear, Greed, and Financial Crises: A Cognitive Neurosciences Perspective

                           
                                                                           NYT

"Far be it from me to say that we ever shall have the means of measuring directly the feelings of the human heart. A unit of pleasure or of pain is difficult even to conceive; but it is the amount of these feelings which is continually prompting us to buying and selling, borrowing and lending, labouring and resting, producing and consuming; and it is from the quantitative effects of the feelings that we must estimate their comparative amounts."

William Stanley Jevons, British economist, in 1871.

Abstract: 

"Historical accounts of financial crises suggest that fear and greed are the common denominators of these disruptive events: periods of unchecked greed eventually lead to excessive leverage and unsustainable asset-price levels, and the inevitable collapse results in unbridled fear, which must subside before any recovery is possible. The cognitive neurosciences may provide some new insights into this boom/bust pattern through a deeper understanding of the dynamics of emotion and human behavior.

In this chapter, I describe some recent research from the neurosciences literature on fear and reward learning, mirror neurons, theory of mind, and the link between emotion and rational behavior. By exploring the neuroscientific basis of cognition and behavior, we may be able to identify more fundamental drivers of financial crises, and improve our models and methods for dealing with them.”

To read full Andrew W. Lo's research paper click Fear, Greed, and Financial Crises: A Cognitive Neurosciences Perspective (pdf) pages: 50, MIT Sloan School of Management; MIT CSAIL; National Bureau of Economic Research (NBER), Oct 13, 2011.

Oct
26th
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Researchers find a country’s wealth correlates with its collective knowledge

"What causes the large gap between rich and poor countries has been a long-debated question. Previous research has found some correlation between a nation’s economic prosperity and factors such as how the country is governed, the average amount of formal education each individual receives, and the country’s overall competiveness. But now a team of researchers from Harvard and MIT has discovered that a new measure based on a country’s collective knowledge can account for the enormous income differences between the nations of the world better than any other factor. (…)

A country’s economy can be measured by a factor they call “economic complexity.” From this perspective, the more diverse and specialized jobs a country’s citizens have, the greater the country’s ability to produce complex products that few other countries can produce, making the country more prosperous.

“The total amount of knowledge embedded in a hunter-gatherer society is not very different from that which is embedded in each one of its members,” the researchers write in their book. “The secret of modern societies is not that each person holds much more productive knowledge than those in a more traditional society. The secret to modernity is that we collectively use large volumes of knowledge, while each one of us holds only a few bits of it. Society functions because its members form webs that allow them to specialize and share their knowledge with others.” (…)

Getting poorer countries to begin producing more complex products is not as simple as offering individuals a formal education in which they learn facts and figures - what the authors refer to as “explicit” knowledge. Instead, the most productive knowledge is the “tacit” kind (for example, how to run a business), which is much harder to teach. For this reason, countries tend to expand their production capabilities by moving from the products they already produce to others that require a similar set of embedded knowledge capabilities.”

— Lisa Zyga, Researchers find a country’s wealth correlates with its collective knowledge, Physorg, Oct 26, 2011 Illustration: This network shows the product space of the US. Image credit: The Atlas of Economic Complexity

“The essential theory … is that countries grow based on the knowledge of making things,” Mr. Hausmann said in a phone interview. “It’s not years of schooling. It’s what are the products that you know how to make. And what drives growth is the difference between how much knowledge you have and how rich you are.”

Thus, nations with extensive productive knowledge but relatively little wealth haven’t met their potential, and will eventually catch up, Mr. Hausmann said. Those countries will experience the most growth through 2020, according to the report.

That bodes well for China, which tops the list of expected growth in per-capita gross domestic product. According to the method outlined in the report, China’s growth in GDP per capita will be 4.32% though 2020. India and Thailand are second and third, respectively.

The U.S., however, is ranked 91, with expected growth in per-capita GDP at 2.01%. “The U.S. is very rich already and has a lot of productive knowledge, but it doesn’t have an excess of productive knowledge relative to its income,” Mr. Hausmann said.

The method, when applied to the years 1999-2009, proved to be much more accurate at predicting future growth than any other existing methods, including the World Economic Forum’s Global Competitiveness Index, according to the report.”

— Josh Mitchell, ‘Complexity’ Predicts Nations’ Future Growth, The Wall Street Journal, Oct 26, 2011

See also:

"The Atlas of Economic Complexity” (pdf). The 364-page report, a study led by Harvard’s Ricardo Hausmann and MIT’s Cesar A. Hidalgo, is the culmination of nearly five years of research by a team of economists at Harvard’s Center for International Development.
Economic inequality, Wiki
☞ Heiner Rindermann and James Thompson, Cognitive Capitalism: The Effect of Cognitive Ability on Wealth, as Mediated Through Scientific Achievement and Economic Freedom (pdf), Chemnitz University of Technology, University College London, 2011.

"Traditional economic theories stress the relevance of political, institutional, geographic, and historical factors for economic growth. In contrast, human-capital theories suggest that peoples’ competences, mediated by technological progress, are the deciding factor in a nation’s wealth. Using three large-scale assessments, we calculated cognitive-competence sums for the mean and for upper- and lower-level groups for 90 countries and compared the influence of each group’s intellectual ability on gross domestic product. In our cross-national analyses, we applied different statistical methods (path analyses, bootstrapping) and measures developed by different research groups to various country samples and historical periods.

Our results underscore the decisive relevance of cognitive ability—particularly of an intellectual class with high cognitive ability and accomplishments in science, technology, engineering, and math—for national wealth. Furthermore, this group’s cognitive ability predicts the quality of economic and political institutions, which further determines the economic affluence of the nation. Cognitive resources enable the evolution of capitalism and the rise of wealth.”

Jul
2nd
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When history was made - an alternative timeline for the past two millennia

"Some people recite history from above, recording the grand deeds of great men. Others tell history from below, arguing that one person’s life is just as much a part of mankind’s story as another’s. If people do make history, as this democratic view suggests, then two people make twice as much history as one. Since there are almost 7 billion people alive today, it follows that they are making seven times as much history as the 1 billion alive in 1811. The chart below shows a population-weighted history of the past two millennia. By this reckoning, over 28% of all the history made since the birth of Christ was made in the 20th century. Measured in years lived, the present century, which is only ten years old, is already “longer” than the whole of the 17th century. This century has made an even bigger contribution to economic history. Over 23% of all the goods and services made since 1AD were produced from 2001 to 2010, according to an updated version of Angus Maddison's figures.”

Two thousand years in one chart, The Economist, Jun 28th 2011

Mar
6th
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We will be here - Map of the Future
The Italian magazine WIRED asked us to draw a map based on the scenarios developed by the Institute for the Future in Palo Alto to help the reader in the net of ideas and hypothesis built by 7000 influencers from all over the world.
“The goal of the project is to engage a broad public in considering the dilemmas we face in our current, everyday lives and think together about resolutions that go beyond the familiar ways of dealing with problems” - Jane Mc Gonigal, Superstruct game designe

We will be here - Map of the Future

The Italian magazine WIRED asked us to draw a map based on the scenarios developed by the Institute for the Future in Palo Alto to help the reader in the net of ideas and hypothesis built by 7000 influencers from all over the world.

The goal of the project is to engage a broad public in considering the dilemmas we face in our current, everyday lives and think together about resolutions that go beyond the familiar ways of dealing with problems” - Jane Mc Gonigal, Superstruct game designe

Feb
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Jan
29th
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Where is America’s debt (foreign held treasury securities, in $US Billions)

Where is America’s debt (foreign held treasury securities, in $US Billions)

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On Mint Street by raj, Banking RBI Report 2007-2008
How does the central bank manage your wallet? Why do you use Rs 500 and Rs 1,000 notes frequently and are flashing the debit card more often? Here are some answers

On Mint Street by raj, Banking RBI Report 2007-2008

How does the central bank manage your wallet? Why do you use Rs 500 and Rs 1,000 notes frequently and are flashing the debit card more often? Here are some answers

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Savings, investments & spending, Money Today Insight by raj
Consumers’ saving behaviour, spending priorities and attitute towards investing have been affected by the slowdown. MT looks at two surveys for and insight.

Savings, investments & spending, Money Today Insight by raj

Consumers’ saving behaviour, spending priorities and attitute towards investing have been affected by the slowdown. MT looks at two surveys for and insight.

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Who’s Buying What | Euromonitor International
A look at where people around the world are directing some of their purchasing power.

Who’s Buying What | Euromonitor International

A look at where people around the world are directing some of their purchasing power.

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The most expensive cities by raj
A recent UBS survey, Prices and Earnings 2009, compared purchasing power around the globe, to arrive at the most and least expensive cities. Excerpts from the survey.

The most expensive cities by raj

A recent UBS survey, Prices and Earnings 2009, compared purchasing power around the globe, to arrive at the most and least expensive cities. Excerpts from the survey.

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The Best Jobs In America | CNNMoney.com. US Bureau of Labour Statistics

The Best Jobs In America | CNNMoney.com. US Bureau of Labour Statistics