Currency Wars: The Making of the Next Global Crisis (31 page)
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Authors: James Rickards
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To understand how a complex system operates, it is necessary to think about the strength of each of these four elements. Imagine each one has a dial that can be turned from settings of zero to ten. At a setting of one, the system is uninteresting. It may have the elements of complexity, but nothing much is going on. Diversity is low, connectedness and interdependence are weak and there is almost no learning or adaptation taking place. At a setting of ten, the system is chaotic. Agents receive too much information from too many sources and are stymied in their decision making by conflicting and overwhelming signals.
Where complexity is most intriguing is in what Scott Page of the University of Michigan calls the “interesting in-between.” This means the dials are set somewhere between three and seven, with each dial different from the others. This allows a good flow of information, interaction and learning among diverse agents, but not so much that the system becomes chaotic. This is the heart of complexity—a system that continuously produces surprising results without breaking down.
Two further characteristics of complex systems are of the utmost importance in our consideration of their application to currency markets and the dollar. These are emergent properties and phase transitions.
Saying a system has an emergent property is like saying the whole is more than the sum of its parts. Tasting a delicious, warm apple pie is more interesting than looking at the dough, sugar, apples and butter that went into it. When systems are highly complex, emergent properties are far more powerful and unexpected. Climate is one of the most complex systems ever studied. It is extremely difficult to model, and reliable weather forecasts can be made only about four days in advance. Hurricanes are emergent properties of climate. Their ingredients, such as low air pressure, warm water, convection and the like, are all easily observed, but the exact timing and location at which hurricanes will emerge is impossible to predict. We know them when we see them.
The best example of an emergent property is probably human consciousness. The human body is composed of oxygen, carbon and hydrogen, with traces of copper and zinc thrown in for good measure. If one were to combine these ingredients in a vat, stir carefully and even jolt the mixture with electricity, nothing would happen. The same ingredients combined through DNA coding, however, produces a human being. There’s nothing in a carbon molecule that suggests thought and nothing in an oxygen molecule that suggests speech or writing. Yet the power of complexity produces exactly those capabilities using exactly those ingredients. Thought emerges from the human mind in the same complex, dynamic way that hurricanes emerge from the climate.
Phase transitions are a way to describe what happens when a complex system changes its state. When a volcano erupts, its state goes from dormant to active. When the stock market drops 20 percent in one day, its state goes from well behaved to disorderly. If the price of gold were to double in one week, the state of the dollar would go from stable to free fall. These are all examples of phase transitions in complex systems.
Not every complex system is poised for a phase transition—the system itself must be in a “critical state.” This means that the agents in the system are assembled in such a way that the actions of one trigger the actions of another until the whole system changes radically. A good example of a phase transition in a critical state system is an avalanche. A normal snowfield on a flat surface is fairly stable, yet the same amount of snow on a steep incline may be in a critical state. New snow may fall for a while, but eventually one snowflake will disturb a few others. Those others will disturb more adjacent flakes until a small slide begins that takes more snow with it, getting larger along the way until the entire mountainside comes loose. One could blame the snowflake, but it is more correct to blame the unstable state of the mountainside of snow. The snowfield was in a critical state—it was likely to collapse sooner or later, and if one snowflake did not start the avalanche, the next one could have.
The same process occurs in a stock market crash. Buy and sell orders hit the market all the time just like snowflakes on the mountain. Sometimes the buyers and sellers are arranged in highly unstable ways so that one sell order triggers a few others, which are then reported by the exchange, triggering even more sell orders by nervous investors. Soon the cascade gets out of control, and more sell orders placed in advance and triggered by “stop-loss” rules are automatically executed. The process feeds on itself. Sometimes the process dies out; after all there are many small disturbances in the snow that do little harm. Sometimes the process grows exponentially until something outside the system intervenes. This intervention can take the form of trading halts, efforts by buying syndicates to reverse the flow or even closing the exchange. Once the cascade stops, the complex system can return to a stable, noncritical state—until the next time.
The recent multiple catastrophes near Sendai, Japan, perfectly illustrate how phase transitions occur in nature and society and how collapse can spread from one system to another when all are in the critical state. Tectonic plates, oceans, uranium and stock markets are all examples of separate complex systems. However, they can interact in a kind of metasystemic collapse. On March 11, 2011, shifting tectonic plates under the Pacific Ocean off the eastern coast of Japan caused an extremely violent 9.0 earthquake. The thrusting of the ocean floor then transferred energy from one system, the earth’s crust, to another system, the ocean, causing a ten-meter-high tsunami. The tsunami smashed into several nuclear reactors, again transferring energy and causing another catastrophe, this time a partial meltdown in uranium and plutonium fuel rods used in the reactors. Finally, the fear induced by the meltdown in the reactors contributed to a meltdown in the Tokyo stock market, which crashed over 20 percent in two days. The earthquake and tsunami were natural systems. The reactor was a hybrid of natural uranium and man-made design, while the stock exchange is totally man-made. Yet they all operated under the same critical state dynamics embedded in complex systems.
Importantly, phase transitions can produce catastrophic effects from small causes—a single snowflake can cause a village to be destroyed by an avalanche. This is one secret behind so-called black swans
.
Nassim Nicholas Taleb popularized the term “black swan” in his book of the same name. In that book, Taleb rightly demolished the normal distribution—the bell curve—as a way of understanding risk. The problem is that he demolished one paradigm but did not produce another to replace it. Taleb expressed some disdain for mathematical modeling in general, preferring to take on the mantle of a philosopher. He dubbed all improbably catastrophic events “black swans,” as if to say, “Stuff happens,” and he left it at that. The term is widely used by analysts and policy makers who understand the “Stuff happens” part but don’t understand the critical state dynamics and complexity behind it. Yet it is possible to do better than throw up one’s hands.
.
Nassim Nicholas Taleb popularized the term “black swan” in his book of the same name. In that book, Taleb rightly demolished the normal distribution—the bell curve—as a way of understanding risk. The problem is that he demolished one paradigm but did not produce another to replace it. Taleb expressed some disdain for mathematical modeling in general, preferring to take on the mantle of a philosopher. He dubbed all improbably catastrophic events “black swans,” as if to say, “Stuff happens,” and he left it at that. The term is widely used by analysts and policy makers who understand the “Stuff happens” part but don’t understand the critical state dynamics and complexity behind it. Yet it is possible to do better than throw up one’s hands.
A forest fire caused by lightning is a highly instructive example. Whether the fire destroys a single tree or a million acres, it is caused by
a single bolt of lightning.
Simple intuition might hold that large bolts cause large fires and small bolts cause small fires, but that is not true. The same bolt of lightning can cause no fire or a catastrophic fire
depending on the critical state.
This is one reason why black swans take us by surprise. They are called extreme events, but it would be more accurate to call them extreme results from everyday events. Extreme results will happen with some frequency; it is the everyday events that trigger them that we don’t see coming precisely because they are so mundane. Studying the system shows us how the everyday event morphs into the black swan. As in the case of the avalanche, what really matters is not the snowflake but the snow.
a single bolt of lightning.
Simple intuition might hold that large bolts cause large fires and small bolts cause small fires, but that is not true. The same bolt of lightning can cause no fire or a catastrophic fire
depending on the critical state.
This is one reason why black swans take us by surprise. They are called extreme events, but it would be more accurate to call them extreme results from everyday events. Extreme results will happen with some frequency; it is the everyday events that trigger them that we don’t see coming precisely because they are so mundane. Studying the system shows us how the everyday event morphs into the black swan. As in the case of the avalanche, what really matters is not the snowflake but the snow.
Two more concepts are needed to round out our understanding of complexity theory. The first involves the frequency of extreme events relative to mild events in a complex system, referred to as a degree distribution. The second is the concept of scale.
The bell-curve degree distribution used in financial economics says that mild events happen all the time and highly extreme events practically never. Yet the bell curve is only one kind of degree distribution; there are many others. The degree distribution that describes many events in complex systems is called a power law. A curve that corresponds to a power law is shown below as Figure 2.
FIGURE 2: A curve illustrating a power-law degree distribution
In this degree distribution, the frequency of events appears on the vertical axis and the severity of events appears on the horizontal axis. As in a bell curve, extreme events occur less frequently than mild events. This is why the curve slopes downward (less frequent events) as it moves off to the right (more extreme events). However, there are some crucial differences between the power law and the bell curve. For one thing, the bell curve (see Figure 1) is “fatter” in the region close to the vertical axis. This means that mild events happen more frequently in bell curve distributions and less frequently in power law distributions. Crucially, this power law curve never comes as close to the horizontal axis as the bell curve. The “tail” of the curve continues for a long distance to the right and remains separated from the horizontal axis. This is the famous “fat tail,” which in contrast with the tail on the bell curve does not appear to touch the horizontal axis. This means that
extreme events happen more frequently
in power law distributions.
extreme events happen more frequently
in power law distributions.
Television and blogs are filled with discussions of fat tails, although the usage often seems more like cliché than technical understanding. What is even less understood is the role of scale. The curve shown above in Figure 2 ends at some point for convenience. Yet in theory it could continue forever to the right without hitting the horizontal axis. This continuation would take the extent of possible catastrophes into unimaginable realms, like a 10.0 earthquake, something never recorded.
Is there a limit to the length of the tail? Yes, at some point the fat tail drops vertically to the horizontal axis. This truncation marks the limit of the system. The size of the greatest catastrophe in a system is limited by the scale of the system itself. An example would be an active volcano on a remote island. The volcano and the island make up a complex dynamic system in a critical state. Eruptions may take place over centuries, doing various degrees of damage. Finally the volcano completely explodes and the island sinks, leaving nothing behind. The event would be extreme, but limited by the scale of the system—one island.
The catastrophe cannot be bigger than the system in which it occurs.
The catastrophe cannot be bigger than the system in which it occurs.
That’s the good news. The bad news is that man-made systems increase in scale all the time. Power grids get larger and more connected, road systems are expanded, the Internet adds nodes and switches. The worse news is that the relationship between catastrophic risk and scale is exponential. This means that if the size of a system is doubled, the risk does not merely double—it increases by a factor of ten. If the system size is doubled again, risk increases by a factor of a hundred. Double it again and risk increases by a factor of a thousand, and so forth.
Financial markets are complex systems nonpareil. Millions of traders, investors and speculators are the autonomous agents. These agents are diverse in their resources, preferences and risk appetites. They are bulls and bears, longs and shorts. Some will risk billions of dollars, others only a few hundred. These agents are densely connected. They trade and invest within networks of exchanges, brokers, automated execution systems and information flows.
Interdependence is also characteristic of markets. When the subprime mortgage crisis struck in early August 2007, stocks in Tokyo fell sharply. Some Japanese analysts were initially baffled about why a U.S. mortgage crisis should impact Japanese stocks. The reason was that Japanese stocks were liquid and could be sold to raise cash for margin calls on the U.S. mortgage positions. This kind of financial contagion is interdependence with a vengeance.
Finally, traders and investors are nothing if not adaptive. They observe trading flows and group reactions; learn on a continuous basis through information services, television, market prices, chat rooms, social media and face-to-face; and respond accordingly.
Capital and currency markets exhibit other indicia of complex systems. Emergent properties are seen in the recurring price patterns that technicians are so fond of. The peaks and valleys, “double tops,” “head and shoulders” and other technical chart patterns are examples of emergence from the complexity of the overall system. Phase transitions—rapid extreme changes—are present in the form of market bubbles and crashes.
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