Geoffrey West
President, Santa Fe Institute

"Growth, Innovation, and the Pace of Life from Cells to Cities "

Thank you very much for inviting me to speak. One of the things I enjoy most about the Santa Fe Institute is the strong interaction we have with the business and corporate community.

As I talk tonight, try to translate what I am saying into your terms. Use what I say as a metaphor. I will try to give you a way of thinking that is quite different than the usual way of thinking about a whole host of problems. I will concentrate on biology in the first two thirds of my talk, and in the last third I will talk about social organizations, primarily cities because that is where we have the most data. The driving question for the last part of the talk is to what extent social systems – businesses, cities, and societies – are simply an extension of biology.

I will begin with some background. This work, by its very nature, is mathematical. I worked for thirty years exploring the fundamental questions of physics, and have migrated into biology in the last five to ten years. My focus has been to make biology as much of a mathematical, quantifiable, and predictive science as possible.

We will begin with a diagram of a lot of different animals of different sizes. It includes organisms of all sizes, from the largest two—the giant sequoia and the blue whale—down to microorganisms. Here’s the question: to what extent are all of these organisms simply scaled versions of each other? Many organisms look quite different, but are these differences superficial? Are the structure, organization, and dynamics of these creatures interrelated? Because this is a mathematical question, I am going to have to do a teeny bit of mathematics. I hope this does not frighten you!

When we’re talking about organisms of such different sizes, a logarithmic scale is more useful than a linear scale. On a logarithmic scale, each unit represents a factor of ten. Using this scale, we can put all of life in a single picture, from a mitochondrion at 10 -16 kg up to the whale at 10 5 kg (100,000 kg). The amoeba is 10 -7 kg, or 1/10,000,000 kg. We will be talking about this spectrum of life for the next few minutes.

Now let’s ask: to what extent are all of these organisms scaled versions of each other? We can begin by thinking about this in the most elementary way—by looking at squares and cubes and how they behave when we scale them up.

Imagine a square with a side of length = 1. If you double the length of the side, then you quadruple the area within the square—area increases by 2 2. Now imagine a cube with a side of length = 1. If you double the length of the side, then you increase the area of the cube eight-fold—2 3. More generally, the area of a shape grows by the square of the length of the side, while the volume grows by the cube of the length. If you keep the density of a substance constant, then mass is proportional to the volume.

Galileo, our first modern scientist, realized over 400 years ago that this trivial observation has extraordinarily profound consequences. He recognized that the strength of a beam or a column increases like its cross-sectional area. Strength is unrelated to the length of the beam, while mass is correlated to the volume of the beam. The strength of the beam increases like the length (of a side) squared, while the weight that the beam must bear increases like the length cubed. Therefore, if you simply scale up a beam, or any structure, isometrically, it will eventually collapse. Galileo discovered that there are fundamental limits to growth, unless you re-engineer, re-design, or change the materials.

This is the most primitive understanding of the source of innovation. It comes from dealing with certain physical constraints on the system that must change if you are to grow. Growth and innovation are intimately related.

Taking our previous equations, we can ask how area scales with the mass of the object. Area is proportional to mass to the two-thirds power. (A ~ M2/3 ) What does the two-thirds power mean? It means to square the number first, and then take the cube route. In simple language, this means that if I increase the mass by a factor of 1,000 (three orders of magnitude), then the area only increases by a factor of 100 (two orders of magnitude).

This is the simplest form of a power law. Power laws are ubiquitous throughout nature. You may not know this, but they are also ubiquitous throughout businesses and corporations. The 2/3 number is the exponent. This equation represents a non-linear relationship because of this exponent. This means that strength increases less-than-linearly in relation to mass. If we increase the mass of an organism by a factor of 1,000, then the strength of the organism will only increase by a factor of 100.

Galileo realized this. He included images of animal bones in his book. The bones of larger animals have to be thicker than the bones of smaller animals for precisely this reason—they need thicker bones to support larger mass. Here’s what he said: “If the size of a body be diminished, the strength of that body is not diminished in the same proportion. Indeed the smaller body, the greater its relative strength. Thus a small dog could probably carry on his back two or three dogs of his own size, but a horse could not carry even one of his own size.” Relatively speaking, then, a dog is stronger than a horse. I would like to elaborate on this idea by showing you some data.

First, here is some obscure data on the surface area of salamanders versus their weight. Plotted on a logarithmic manner, the slope of this ratio is exactly 2/3, which confirms Galileo’s hypothesis. This means that while surface area increases by two orders of magnitude, the mass increases by three orders of magnitude.

What about the strength? On another logarithmic scale, we have plotted the maximum weight lifted by champion weightlifters versus the body weight of the champion. The slope of this line is again 2/3. So while the strength of the weightlifter increases by two orders of magnitude, the mass increases by three, exactly as Galileo predicted.

If you then ask the question of who is the strongest among the weightlifters, it would not be the person who could lift the most absolute weight. Instead it is the person who had the largest ratio of weight lifted over body weight. The line on the graph tells us what weight a champion should be able to lift given their body weight.

There are many misconceptions about scaling. The first story about strength and weight has to do with Superman. When Superman was introduced in 1938, the first page of the comic book explains that since he was born on Krypton, which had greater gravity than the Earth, Superman developed greater strength—all of which is, of course, true and factual. At the bottom of the first page is a separate box entitled “A Scientific Explanation of Clark Kent’s Amazing Strength”. “Incredible? No! For even today on our world exist creatures of super-strength.” The first picture shows an ant lifting a twig many times its size. The caption reads, “The lowly ant can support weights hundreds of times its own.” Is that “super-strength” or not?

If we think of the ant as a scaled-down version of a champion weightlifter, we need only extend our previous graph. The graph tells us how much we should be able to lift given a certain body weight. This graph predicts that, indeed, the ant should be able to lift about one hundred times its own weight. There is nothing miraculous, then, about the strength of the ant—it lifts the amount that it should lift. The comic book exaggerates the ant’s strength a little bit, but not by too much.

The next frame in the comic book shows a grasshopper leaping. “The grasshopper leaps what to man would be the space of several city blocks.” It turns out that if you do a simple analysis, you will discover that animals of all weights can jump about the same height. The height we can jump is invariant with our mass. We can jump a few feet. Dogs can jump a few feet. A grasshopper jumps a few feet.

Now I would like to move from this introduction to some biology that’s a little bit more real. I would like to use this way of thinking to understand some aspects of biology. Life is certainly the most complex and diverse physical system in the universe. Understanding its organization, structure, and dynamics is a major challenge for the 21st century, and it will lead to all kinds of extraordinary discoveries.

Your metabolism, for example, is an extremely complex system. A roadmap of just the chemical interfaces is very difficult to read. Another illustration of the complexity of life is a forest. In a forest, there are trees and shrubs of all different sizes, in addition to all of the wildlife that remains hidden from view. There are several questions that we want to ask about the forest if we think about it as a system. Can we develop formulas to help us make predictions about the following questions:

• How far is it between trees of the same size?
• How many branches does a tree of a certain size have?
• How many plants are there of a given size?
• How tall are the different trees?
• How much energy is flowing in each of the branches?
• How can we better understand how an individual tree works, on average, and how can we understand the interrelationships among all of these different trees competing for limited resources?

Let me start with a philosophical comment. There will never be a set of “ Newton’s Laws” for either biology or the social sciences. There will never be a set of principles and equations that will allow you to determine most of the things that you want to know about in those systems. In contrast, these equations allow us to send satellites into space, and we know exactly where they are and how they are behaving, because we understand the basic science of physics and we can work out the equations to put things where we want them. I do not think that we will ever be able to do this in the biological, medical and social sciences.

But that does not mean to say that we cannot envision a quantitative, mathematical, predictive theory that tells us about a system’s coarse-grained behavior. Such a theory should allow us to answer questions like “How far does a man have to walk on average to find another tree of comparable size?” or “How many leaves will an average tree of this size have?”

Such a theory should also allow us to better understand why no one in this room will live longer than about 100 years. This question has intrigued me for a long time. No one in this room will live for a thousand years or a billion years, and most likely none of our children will die a natural death before they are three months old. Why is 100 years the timeframe that we can expect to live? Where does this number come from? We believe that everything is determined by molecular phenomena (genes, etc.), which have only molecular time scales associated with them. How do these molecules know that we should die at about the age of 100? Why do the genetic molecules let us live for a hundred years when they are inside of humans, but the same molecules inside a mouse only allow it to live for a couple of years? That is the sort of question we want to try to answer.

It turns out that scaling is a wonderful way to start to think about these kinds of questions. Despite the extraordinary complexity and diversity of life, life plotted in terms of scale appears to be absurdly simple. We look at the power required (energy per unit time) for an organism to stay alive. When we plot that metabolism rate against the animal’s mass on a log-log scale, we discover an absurdly simple line of all life. The line has a slope of ¾, which means that if you increase the mass of an organism by a factor of 10,000, then the metabolic rate increases by a factor of only 1,000.

This implies an extraordinary economy of scale. If you increase the mass of an animal by a factor of 10,000, you increase the number of cells by a factor of 10,000 as well. Since all cells are roughly the same, you would think that the energy required to sustain these cells would grow by a factor of 10,000 as well. But you don’t! You only need 1,000 times the amount of energy—this represents a remarkable economy of scale!

Furthermore, each organism plotted on this line evolved in its own environmental niche, competing for resources with every other organism in its environment. Intuitively, you might think that since each niche is unique, the plot of metabolism versus mass would have been all over the place—certainly not a straight line. Yet despite the extraordinary complexity among organisms and environments, all of life lines up nicely on this very straight line.

Parenthetically, humans require about 100 watts of power. We need the same amount of energy as a light bulb. This shows how extraordinarily efficient we are. So if we turned off one of the light bulbs that we leave on all the time, that energy could be used to support someone starving in Africa, for example. That is a very important observation, because if you ask how much energy we actually use as socialized creatures, the answer is not 100 watts, but 10,000 watts. Evolution dictated we require 100 watts in order to survive, yet in today’s great society with its lights, cars, air conditioning, and other paraphernalia of civilized life, we consume more energy per person than a blue whale.

Each one of us in this auditorium behaves biologically as if we are bigger than this entire room. This is a graphic way to show how dramatically we have screwed up our relationship with the natural environment and the process that led to our very being. I am not making a judgment; it is simply what we have done. The question is how to deal with it, because we are totally out of whack with the natural world.

It turns out that if you look at any plant or animal, it will scale on a line with a slope of ¾. Furthermore, almost any other physiological variable will exhibit the same behavior. For instance, the radius of your aorta scales against your mass with a slope of 3/8. (If you square this radius, you get the cross-sectional area, which scales at ¾ again.) This same aorta scaling holds true for tree trunks as well. Either this is a diabolical accident, or something profound is going on underneath all of this data.

The number four plays a very special role in life. The spiritual number of the universe is not seven or thirteen or one or the trinity—it is four. I have done a little research and discovered that very few religions have picked up on this.

It turns out that our life span scales with mass to the ¼. If our metabolism decreases as mass to the ¼, then the number of heartbeats in a lifespan is roughly constant for all living things. A mouse lives a short life, but its heart beats very fast. An elephant lives a long time, but its heart beats very slowly. This happens in a magical way such that the total number of heartbeats is roughly invariant. Again, either this is a diabolical accident, or there is something profound going on.

I pointed out earlier that there is an economy of scale in organisms. Metabolic rate varies as mass to the ¾ power, and the number of cells increases linearly with mass. Therefore the number of cells is increasing much faster than the metabolic rate. The metabolic rate of an average cell is therefore decreasing as the size of the organism increases. As you get bigger, your cells do not work as hard—this is a reflection of the economies of scale. A gram of mouse requires three times the energy of a gram of dog and nine times the energy of a gram of elephant.

If you took all of your cells and put them in a petri dish, it would take 10,000 watts to support them, rather than the 90 watts that is required to support your whole body. This difference represents the efficiency of your body as an integrated, working organism. Those 90 watts correlate with the 2,000 calories per day that the FDA recommends that you eat. I have tried to persuade the FDA for several years now to tell Americans that the amount of food they need to eat in a day is one light bulb.

I want to say a little bit about the origin of these laws. But before doing so, I want to show you one other thing. I will describe the theory mostly in words because it is almost entirely mathematical. This theory is interesting because it makes a lot of predictions, and one of them is relevant as we talk about social organizations. One of the predictions that our theory makes is that organisms of all types and sizes obey this scaling law of mass to the ¾ power.

We plotted the energy use of a tree against its diameter. The energy use is proportional to the diameter of the trunk, squared. The theory also predicts that, if you are looking at a forest, the number of trees of a given size decreases as 1/D 2. You know this instinctively—there are a lot of very tiny trees and very few big ones. The big ones use a lot of energy, and the little ones use a little bit of energy, but they do it in an exactly inverse fashion.

This means that if you multiply these two together, the diameters will cancel each other out of the equation, and that in any energy “bin” the same amount of energy is being used. If you add up the energy use of all of the tiniest shrubs in the forest, it will be the same amount of energy used by the single largest tree. We have recently done this for cities in France, and the results are roughly comparable. If you add up all of the energy used by little French towns between the population of 5,000 and 15,000, they use about the same amount of energy as Paris. (These numbers are rough guesses from memory—the actual figures may be somewhat different.)

Where do these laws come from? I have been working with James Brown, a very distinguished ecologist, and his student, one of the ten brightest young scientists under the age of 35 in this country. What is the problem that a complex organism faces? If you are made of 10 14 cells, your body must supply and service all of those cells in an equally efficient and democratic fashion.

The way that nature has addressed this problem is a series of hierarchical branching networks. Inside your skin, you are a cardiovascular system, a renal system, a respiratory system, a neural system, a bunch of bones, and so on and so forth. All of those systems are hierarchical, branching systems—they start with something big, like the aorta, and get smaller and smaller until they reach the capillaries.

We first postulated that such networks support life at all scales. Even within the cells themselves, such networks transport energy and resources. You see these hierarchical branching networks supporting life at the microscopic level, at the level of the organism, and at the ecosystem scale as well.

That said, an ecosystem has a very different type of network than an organism. The networks in an organism are much more like the plumbing in a house. An ecosystem’s networks are not physically connected, and therefore much more closely resemble the kinds of networks that we find in corporations and social systems. There are clearly networks in place to distribute information and resources.

The fundamental idea is that scaling laws all follow from the generic, universal properties of hierarchical, branching networks. We therefore need to define properties of networks that are independent from their specific design. Trees and mammals have completely different structures and functions, yet they seem to share some of the same three properties:

• The first property is that these hierarchical branching networks need to be space-filling. This is a technical term, but it means what it says—the networks must go everywhere and feed everybody in the system.
• The second property is that the terminal units of the networks—the capillaries in your circulatory system, the alveoli in the lungs, the mitochondria within cells—are identical no matter what the scale of the organism or the network. The capillaries for a shrew are identical to those of a blue whale. As new species evolve within a specific design, natural selection did not change the basic units. Instead selection builds off of the fundamental units and increases the scales of the networks. Natural selection uses the same building blocks to create a huge variety of shapes and sizes, just like architects and engineers use the same building blocks (e.g. electrical outlets) to create many different types and sizes of buildings.
• The last property is that space-filling and invariant-terminal-unit networks have evolved to be optimized. If I double the length of the fifth branch of our circulatory system, the heart would have to work harder. By the same token, if I reduced the length of the fifth branch by half, the heart would also have to work harder. Every single variable in the network (radius, branching ratio, etc.) has been optimized, and any change will make the system worse. These networks exist everywhere in nature.

Now, we want to put these ideas in mathematical form to start analyzing the network. I will not go into great detail about this process, but I will tell you the results. Your circulatory system has been optimized. When your heart beats, blood flows down the aorta in such a way that the wave is unattenuated—it does not disperse. The aorta has evolved to be big enough so that viscous drag forces have essentially no effect on the blood flow. The wave then comes to a branch point in the arterial system—some blood goes down one branch, some goes down the other. If it were an arbitrary circuitry system, then some of this blood would bounce back. If you are going to have an optimized system, you had better not be pumping against yourself—there had better not be any reflections. This tells us that the cross-sectional area of any parent branch is the sum of the cross-sectional areas of the daughters. This is indeed true in our bodies, and this ensures that we have virtually no reflections. This is called impedance-matching, and this approach was used to design the electricity networks that distribute power around the country. In an idealized way, your heart has to do almost no work to pump blood through the system.

However, there is a problem. You want to feed microscopic cells, and the circulatory branches get smaller and smaller, which increases the effect of viscosity on blood flow. It is much harder to push fluid through a small tube than a big tube. A pretty good proxy for metabolic rate is the rate of blood flow through the aorta, since the aorta supplies the food and other nutrients for all of the cells. Our theory allows you to determine the flow rate through the aorta as a function of the mass of the organism. The flow rate is mass to the ¾ power, but you also get a complete picture of the cardiovascular system. If you were perverse enough to want to know what the radius, length, or blood flow rate of the ninth branch of a rhinoceros is, then we have a formula to help you figure it out. If you plug in the numbers, our formulas will give you the right answers for the average rhinoceros.

This has been very useful for thinking about new designs for artificial hearts and so on. One of the long-term problems associated with artificial hearts is that they provide very even and steady blood flow, which is not the way that you evolved.

This approach also predicts that the efficiency of an organism will increase with its size, which we have seen in nature. It is a little bit weird that the efficiency of a cell will increase if it is inside a larger organism. This implies that the macroscopic network (the whole organism or corporation) somehow determines the microscopic level (the cells or individuals). In science this is very surprising, because we tend to build systems up from the properties of the fundamental objects (nuclei, atoms, molecules, etc.) to understand what is happening on the macroscopic level. This approach seems backward to a scientist.

But this idea gives us one very quick prediction. If you remove the network entirely—that is, if you take out cells that behave differently in different organisms—the cells would all behave the same way. The cells should all require the same amount of energy. Experiments have validated this theory.

I will stay in biology for just a moment to talk about growth, and then translate that approach over to social systems like cities. Growth is a scaling phenomenon. Pediatricians show parents a graph of how large a child should be based on its age. The curve starts off very steep, and then levels off when the child matures. This growth curve is a very similar shape for many types of organisms, from rats to mammals, to birds, to fish.

Why do we stop growing? Our circulatory system is a hierarchical branching network with the smallest branch (the capillaries) responsible for feeding all of our cells. Cells die periodically, and our body has to replace them. And if you are growing, then you are adding cells. We want to describe this process mathematically. The total energy (B) coming through the network into the capillaries goes into two generic sinks—maintenance of existing cells and growth. The maintenance energy is the number of cells in the body (Nc) multiplied by the metabolic rate of each cell (Bc). The growth energy is the energy required to create a cell (Ec) multiplied by the rate of increase in the number of cells (dNc /dt). I don’t mean to frighten you, but I have rewritten this as an equation:

B = Nc Bc + Ec dNc/dt

You can solve this equation to learn about growth. It is important to note that theory determines the parameters of the equation. The parameters are independent of growth—they are factors like overall metabolism, the mass of a cell, and the energy needed to create a cell.

So if incoming energy is growth plus maintenance, then growth is incoming energy minus maintenance. The energy required for maintenance, however, grows proportionally to the number of cells—linearly. The incoming energy (metabolic rate) only increases as mass to the ¾ power—less than linearly. Therefore at some point, the energy to maintain your existing cells consumes all of the energy of your metabolism, and there will be no energy left over for growth. This is why we stop growing.

Research has verified these equations with real growth rates, and they have been extended to apply to other kinds of growth as well, like tumors. Tumors are interesting medically and conceptually. You can think of a tumor as an organism unto itself with its own vasculature which interfaces with the vasculature of the host. There are many things in society like that. You can predict from this one of the interesting features of tumors— that they will have necrotic cores. There are parts of tumors that are not alive, but the tumor does not get rid of them. They do not play by the rules of normal organisms, including the rule that you’re supposed to have a disposal system. This theory predicts the ratio of live tissue in a tumor to overall tissue.

I would now like to turn to social organizations. Can we apply any of these scaling ideas to social systems? Can we use the generic properties of networks to talk about an average idealized organism in the social or corporate world? Are there analogs in the social world that would let us talk about an average idealized city? We want to understand how and why a city of a certain size must have certain characteristics. We want to understand how London, Baltimore, Calcutta, Santa Fe and Kyoto are, despite the obvious differences, simply scaled versions of one another on a coarse-grained level.

It is critical to have such a model in place if you want to try to address social and organizational problems. Unless you know what to expect from the average idealized system, you cannot deal with problems, which are really a function of how much the actual system deviates from the average idealized one.

The overall question is this: to what extent are social organizations an extension of biology? We have assembled a very diverse team to collaborate on trying to solve this question. Such collaborations are necessary to address the complex social challenges that we face today. When we began, we developed a list of sub-questions that we wanted to answer. These included the following:

• Can one construct a general theory of social organizations that is quantitative and predictive?
• Are there universal scaling laws that reveal underlying principles?
• Are there average idealized social organizations?
• Did they evolve under natural selection in a free market environment in competition?
• What is the nature of their hierarchies and generic network structures?
• Are there universal classes of networks?
• Is there an optimal maximum (or minimum) size?
• What drives mergers?
• How should we think about growth, maturity, aging, evolution?
• Is energy information which disseminates?

I will focus on the question of growth. One of the first things that we did was to ask how we could possibly find data on some of these concepts and plot them against city size. Are there analogs in cities for those scaling laws? Very little work had been done in this area! We discovered that total income versus population obeyed a power law with a slope of 1.1. If we plot energy versus population, we discovered a wider spread, but a power law nevertheless. The slope of this line was less than 1.0. We looked at patents versus city size and discovered a slope greater than 1.0. Tax receipts and police protection are also both greater than 1.0.

Unlike life and biology, the scaling laws associated with cities (and I suspect corporations) broke down into three groups of exponents. The presumption is that these exponents could all be derived once we understood the general principles under which cities have evolved. Here are the three groups:

• Super-linear group. All of the quantities with exponents greater than 1.0 are associated with the fact that we are social creatures, including patents, inventions, R&D establishments, and super-creatives. This group is much more sociological. It is innovative and driven by the production of wealth. The predominant quality of this group is increasing returns—the larger you are, the more you net per person or per unit.
• Linear group. Some trivial things scaled linearly, including total employment and total establishments.
• Sub-linear group. The third group of scaling laws is sub-linear with exponents less than 1.0, and they behave much more like biology. This group includes energy use and the number of gasoline stations. This group is driven by mechanical efficiency. Its innovation is also mechanical, and it has taken place over a very long time scale. The theme for this group is economies of scale.

We can now distinguish biological innovation driven by mechanical efficiency (sub-linear), from sociological innovation driven by the fact that we associate with each other and invent new ways of doing things (super-linear).

I talked about the metabolic rate of a cell decreasing as mass to the ¼ power. That quantity determines the pace of life. The bigger you are, the slower your rates are. The rate of life decreases with size—you live longer, your heart beats slower. The exact opposite is the case if the exponent is greater than 1.0. With a super-linear function, the larger you are, the faster life becomes. The data for innovative characteristics predicts that the pace of life must be faster with greater innovation.

Let’s look again at growth. Overall incoming energy is used for either maintenance or growth. Now if the incoming energy has an exponent less than one, then we get a sigmoidal curve like the growth rate of most animals. If the exponent is greater than one, the growth is no longer sigmoidal. Something quite different happens—you not only keep growing, but you grow asymptotically toward a singularity. This is very bad because you will run out of resources, and the organization or society will collapse. If you determine how long it takes from starting an organization to reach the singularity, you discover that the larger the population (of the city or organization) the shorter the time to collapse.

This is not what has happened in reality. We have avoided this scenario by going through cycles of innovation. Each innovation begins a new growth curve. Every time you have a major innovation, you reset the clock on reaching the singularity and collapse. The problem is that each innovation takes place in a larger population than the last because of ongoing, accelerating growth. Thus, the amount of extra time that each innovation buys you will be less and less. Therefore, you will quickly get to a point where you will have to innovate almost constantly in order to avoid the collapse.

We have started to test this model with some data. The population of New York City seems to be following a curve similar to this one, but the data is not definitive. The population of the world seems to follow this pattern a bit better.

We have developed a quantitative theory. The pace of life is faster in bigger cities. You can see this in the speed of walking, crime rates, disease rates, transaction rates and others. The pace of walking, for example, increases in a systematic and predictable way with the size of a city. This is very much a work in progress, but we seem to be on our way to developing a quantitative and predictive theory.

I wanted to show you these slides to introduce you to a way of thinking that is very complementary to traditional ways of thinking about social organizations. These traditional methods tend to be qualitative and simulation-based, but we want to use scaling to discover whether there are any generic, universal laws governing the way that we all interact. We want to find these and turn them into a mathematical, predictive framework.

The comments, opinions and any forward predictions presented about any particular security, the economy and "the market" are based on the analysis of the speaker. These are not necessarily the opinion of, and should not be construed as a recommendation on the part of Legg Mason Capital Managment or any of its affiliates.