Tag Archives: systemic risk

Seed’s global reset on tipping points and systematic risk

Seed magazine has a special issue on new approaches to interconnected and complex challenges. It also features interesting articles on TEEB and ecological economics, new modes of science, forecasting, tipping points and systematic risk.  As well as,  Carl Folke’s article on resilience, which I mentioned previously.

Economist Ian Goldin writes on On Systemic risks

Systemic risk is the underbelly of globalization and technical change. Intense integration of markets, trade, and finance has accompanied the latest tidal wave of globalization, facilitated by seismic policy shifts, like those associated with the fall of the Soviet Union, the formation of the European Union, and the opening of emerging economies. Between 1980 and 2005, global foreign-investment flows increased 18 times, and trade flows increased more than sevenfold, reflecting unprecedented integration.

… While the term “systemic risk” has historically referred mainly to collapses in finance, recent decades of globalization have created new and broader risks. There has been an exponential increase in the number of nodes and pathways through which materials, capital, information, and knowledge can be transmitted at lightning speeds and with global reach. These networks also have the potential to create and propagate risk. Interconnectedness, networks’ central property, can lead simultaneously to greater robustness and more fragility. Risk can decline as connectivity increases because as risk sharing increases, so does the number of nodes and links. This is true of financial systems, manufacturing services, intellectual property, and ecosystems. However, increased fragility is also a concern. Once a tipping point is triggered past its threshold, connectivity can amplify and spread risk instead of sharing it stably.

Looming systemic risks include pandemics, which may spread more rapidly across a densely connected world, and bio-terrorism risks, which are likely to become increasingly systemic in the 21st century. The ability to produce biological and other weapons of mass destruction is becoming more widespread, especially among non-state actors, due to technological innovation (not least with the development of DNA synthesizers). Increases in population density, urbanization, and the growth of connectivity, both physically and virtually, means that dangerous recipes and panic can be instantaneously transmitted globally. And climate change, a silent tsunami that crept up on us, presents major systemic environmental, social, and economic risks to humanity.

In an article On Early Warning Signs of tipping points ecologist George Sugihara writes:

A key phenomenon known for decades is so-called “critical slowing” as a threshold approaches. That is, a system’s dynamic response to external perturbations becomes more sluggish near tipping points. Mathematically, this property gives rise to increased inertia in the ups and downs of things like temperature or population numbers—we call this inertia “autocorrelation”—which in turn can result in larger swings, or more volatility. In some cases, it can even produce “flickering,” or rapid alternation from one stable state to another (picture a lake ricocheting back and forth between being clear and oxygenated versus algae-ridden and oxygen-starved). Another related early signaling behavior is an increase in “spatial resonance”: Pulses occurring in neighboring parts of the web become synchronized. Nearby brain cells fire in unison minutes to hours prior to an epileptic seizure, for example, and global financial markets pulse together. The autocorrelation that comes from critical slowing has been shown to be a particularly good indicator of certain geologic climate-change events, such as the greenhouse-icehouse transition that occurred 34 million years ago; the inertial effect of climate-system slowing built up gradually over millions of years, suddenly ending in a rapid shift that turned a fully lush, green planet into one with polar regions blanketed in ice.

The global financial meltdown illustrates the phenomenon of critical slowing and spatial resonance. Leading up to the crash, there was a marked increase in homogeneity among institutions, both in their revenue-generating strategies as well as in their risk-management strategies, thus increasing correlation among funds and across countries—an early warning. Indeed, with regard to risk management through diversification, it is ironic that diversification became so extreme that diversification was lost: Everyone owning part of everything creates complete homogeneity. Reducing risk by increasing portfolio diversity makes sense for each individual institution, but if everyone does it, it creates huge group or system-wide risk. Mathematically, such homogeneity leads to increased connectivity in the financial system, and the number and strength of these linkages grow as homogeneity increases. Thus, the consequence of increasing connectivity is to destabilize a generic complex system: Each institution becomes more affected by the balance sheets of neighboring institutions than by its own. The role of systemic risk monitoring, then, could simply be rapid detection and dissemination of potential imbalances, much as we allow frequent underbrush fires to burn in order to forestall catastrophic wildfires. Provided that these kinds of imbalances can be rapidly identified, maybe we will need no regulation beyond swift diffusion of information. Having frequent, small disruptions could even be the sign of a healthy, innovative financial system.

Further tactical lessons could be drawn from similarities in the structure of bank payment networks and cooperative, or “mutualistic,” networks in biology. These structures are thought to promote network growth and support more species. Consider the case of plants and their insect pollinators: Each group benefits the other, but there is competition within groups. If pollinators interact with promiscuous plants (generalists that benefit from many different insect species), the overall competition among insects and plants decreases and the system can grow very large.

Relationships of this kind are seen in financial systems too, where small specialist banks interact with large generalist banks. Interestingly, the same hierarchical structure that promotes biodiversity in plant-animal cooperative networks may increase the risk of large-scale systemic failures: Mutualism facilitates greater biodiversity, but it also creates the potential for many contingent species to go extinct, particularly if large, well-connected generalists—certain large banks, for instance—disappear. It becomes an argument for the “too big to fail” policy, in which the size of the company’s Facebook network matters more than the size of its balance sheet.

Systemic risk reflections

TED spreadSome recent reflections on systemic risk and the financial markets – ranging from details to the big picture.

First, Gretchen Morgenson in the New York Times writes Behind Insurer’s Crisis, Blind Eye to a Web of Risk:

“It is hard for us, without being flippant, to even see a scenario within any kind of realm of reason that would see us losing one dollar in any of those transactions.”— Joseph J. Cassano, a former A.I.G. executive, August 2007

…Although America’s housing collapse is often cited as having caused the crisis, the system was vulnerable because of intricate financial contracts known as credit derivatives, which insure debt holders against default. They are fashioned privately and beyond the ken of regulators — sometimes even beyond the understanding of executives peddling them.

Originally intended to diminish risk and spread prosperity, these inventions instead magnified the impact of bad mortgages like the ones that felled Bear Stearns and Lehman and now threaten the entire economy.

In the case of A.I.G., the virus exploded from a freewheeling little 377-person unit in London, and flourished in a climate of opulent pay, lax oversight and blind faith in financial risk models. It nearly decimated one of the world’s most admired companies, a seemingly sturdy insurer with a trillion-dollar balance sheet, 116,000 employees and operations in 130 countries.

Second, America’s National Public Radio’s Planet Money has a lot of recent indepth coverage of the financial crisis in this vein available of podcasts.  Including a recent one called the day America’s economy almost died.

Looking a more the big economic picture,  Predicting Crisis in the United States Economy a profile of Nouriel Roubini discusses the selective vision of models and the biases against discontinuities or nonlinear change.

Recessions are signal events in any modern economy. And yet remarkably, the profession of economics is quite bad at predicting them. A recent study looked at “consensus forecasts” (the predictions of large groups of economists) that were made in advance of 60 different national recessions that hit around the world in the ’90s: in 97 percent of the cases, the study found, the economists failed to predict the coming contraction a year in advance. On those rare occasions when economists did successfully predict recessions, they significantly underestimated the severity of the downturns. Worse, many of the economists failed to anticipate recessions that occurred as soon as two months later.

The dismal science, it seems, is an optimistic profession. Many economists, Roubini among them, argue that some of the optimism is built into the very machinery, the mathematics, of modern economic theory. Econometric models typically rely on the assumption that the near future is likely to be similar to the recent past, and thus it is rare that the models anticipate breaks in the economy. And if the models can’t foresee a relatively minor break like a recession, they have even more trouble modeling and predicting a major rupture like a full-blown financial crisis. Only a handful of 20th-century economists have even bothered to study financial panics. (The most notable example is probably the late economist Hyman Minksy, of whom Roubini is an avid reader.) “These are things most economists barely understand,” Roubini told me. “We’re in uncharted territory where standard economic theory isn’t helpful.”

Finally, Science Fiction writer Charlie Stross writes about the increasing difficulty of projecting the near future at all:

We are living in interesting times; in fact, they’re so interesting that it is not currently possible to write near-future SF.

… Put yourself in the shoes of an SF author trying to construct an accurate (or at least believable) scenario for the USA in 2019. Imagine you are constructing your future-USA in 2006, then again in 2007, and finally now, with talk of $700Bn bailouts and nationalization of banks in the background. Each of those projections is going to come out looking different. Back in 2006 the sub-prime crisis wasn’t even on the horizon but the big scandal was FEMA’s response (or lack thereof) to Hurricane Katrina. In 2007, the sub-prime ARM bubble began to burst and the markets were beginning to turn bearish. (Oh, and it looked as if the 2008 presidential election would probably be down to a fight between Hilary Clinton and Rudy Giuliani.) Now, in late 2008 the fiscal sky is falling; things may not end as badly as they did for the USSR, but it’s definitely an epochal, historic crisis.

Now extend the thought-experiment back to 1996 and 1986. Your future-USA in the 1986 scenario almost certainly faced a strong USSR in 2019, because the idea that a 70 year old Adversary could fall apart in a matter of months, like a paper tiger left out in a rain storm, simply boggles the mind. It’s preposterous; it doesn’t fit with our outlook on the way history works. (And besides, we SF writers are lazy and we find it convenient to rely on clichés — for example, good guys in white hats facing off against bad guys in black hats. Which is silly — in their own head, nobody is a bad guy — but it makes life easy for lazy writers.) The future-USA you dreamed up in 1996 probably had the internet (it had been around in 1986, in embryonic form, the stomping ground of academics and computer industry specialists, but few SF writers had even heard of it, much less used it) and no cold war; it would in many ways be more accurate than the future-USA predicted in 1986. But would it have a monumental fiscal collapse, on the same scale as 1929? Would it have Taikonauts space-walking overhead while the chairman of the Federal Reserve is on his knees? Would it have more mobile phones than people, a revenant remilitarized Russia, and global warming?

There’s a graph I’d love to plot, but I don’t have the tools for. The X-axis would plot years since, say, 1950. The Y-axis would be a scatter plot with error bars showing the deviation from observed outcomes of a series of rolling ten-year projections modeling the near future. Think of it as a meta-analysis of the accuracy of projections spanning a fixed period, to determine whether the future is becoming easier or harder to get right. I’m pretty sure that the error bars grow over time, so that the closer to our present you get, the wider the deviation from the projected future would be. Right now the error bars are gigantic.