Black Swans and the Arab Spring

Nassim Taleb and IPE Prof. Mark Blyth apply "Black Swans" to foreign policy and the Arab Spring:

Complex systems that have artificially suppressed volatility tend to become extremely fragile, while at the same time exhibiting no visible risks. In fact, they tend to be too calm and exhibit minimal variability as silent risks accumulate beneath the surface. Although the stated intention of political leaders and economic policymakers is to stabilize the system by inhibiting fluctuations, the result tends to be the opposite. These artificially constrained systems become prone to "Black Swans"--that is, they become extremely vulnerable to large-scale events that lie far from the statistical norm and were largely unpredictable to a given set of observers.

Such environments eventually experience massive blowups, catching everyone off-guard and undoing years of stability or, in some cases, ending up far worse than they were in their initial volatile state. Indeed, the longer it takes for the blowup to occur, the worse the resulting harm in both economic and political systems. ...

Take, for example, the recent celebrated documentary on the financial crisis, Inside Job, which blames the crisis on the malfeasance and dishonesty of bankers and the incompetence of regulators. Although it is morally satisfying, the film naively overlooks the tact that humans have always been dishonest and regulators have always been behind the curve. The only difference this time around was the unprecedented magnitude of the hidden risks and a misunderstanding of the statistical properties of the system. ...

Humans fear randomness--a healthy ancestral trait inherited from a different environment. Whereas in the past, which was a more linear world, this trait enhanced fitness and increased chances of survival, it can have the reverse effect in today's complex world, making volatility take the shape of nasty Black Swans hiding behind deceptive periods of "great moderation." This is not to say that any and all volatility should be embraced. Insurance should not be banned, for example.

But alongside the "catalysts as causes" confusion sit two mental biases: the illusion of control and the action bias (the illusion that doing something is always better than doing nothing). This leads to the desire to impose man-made solutions. Greenspans actions were harmful, but it would have been hard to justify inaction in a democracy where the incentive is to always promise a better outcome than the other guy, regardless of the actual, delayed cost.

I don't have too much to say about this right now, but I find the basic argument very interesting. (For those without institutional access to Foreign Affairs, Blyth describes the basic principles in this audio interview.) It's not especially new, especially for Taleb, but generalizing the argument that economies, polities, and other social systems are complex adaptive systems is important. The central claim -- the more we try to keep a lid on volatility, the bigger the inevitable explosion -- may or may not be strictly true. It's the sort of claim that needs more empirical support than they provide in this essay alone. But the bigger argument about complex systems is surely true, and internalizing it is important for social scientists and policy makers. I expect this sort of thinking to guide a lot of future research in the social sciences.

Quote of the weekend.

I posted a link a couple of days ago to Barry Eichengreen's recent article "The Last Temptation of Risk" in The National Interest. But I thought I'd isolate a key bloc of paragraphs for our readers, in case you hadn't had the time to read the piece or clicked on the link and decided it was far too long, especially on a nice early summer weekend to read: 

WITH THE pressure of social conformity being so powerful, are we economists doomed to repeat past mistakes? Will we forever follow the latest intellectual fad and fashion, swinging wildly—much like investors whose behavior we seek to model—from irrational exuberance to excessive despair about the operation of markets? Isn’t our outlook simply too erratic and advice therefore too unreliable to be trusted as a guide for policy?

Maybe so. But amid the pervading sense of gloom and doom, there is at least one reason for hope. The last ten years have seen a quiet revolution in the practice of economics. For years theorists held the intellectual high ground. With their mastery of sophisticated mathematics, they were the high-prestige members of the profession. The methods of empirical economists seeking to analyze real data were rudimentary by comparison. As recently as the 1970s, doing a statistical analysis meant entering data on punch cards, submitting them at the university computing center, going out for dinner and returning some hours later to see if the program had successfully run. (I speak from experience.) The typical empirical analysis in economics utilized a few dozen, or at most a few hundred, observations transcribed by hand. It is not surprising that the theoretically inclined looked down, fondly if a bit condescendingly, on their more empirically oriented colleagues or that the theorists ruled the intellectual roost.

But the IT revolution has altered the lay of the intellectual land. Now every graduate student has a laptop computer with more memory than that decades-old university computing center. And she knows what to do with it. Just like the typical twelve-year-old knows more than her parents about how to download data from the internet, for graduate students in economics, unlike their instructors, importing data from cyberspace is second nature. They can grab data on grocery-store spending generated by the club cards issued by supermarket chains and combine it with information on temperature by zip code to see how the weather affects sales of beer. Their next step, of course, is to download securities prices from Bloomberg and see how blue skies and rain affect the behavior of financial markets. Finding that stock markets are more likely to rise on sunny days is not exactly reassuring for believers in the efficient-markets hypothesis.

The data sets used in empirical economics today are enormous, with observations running into the millions. Some of this work is admittedly self-indulgent, with researchers seeking to top one another in applying the largest data set to the smallest problem. But now it is on the empirical side where the capacity to do high-quality research is expanding most dramatically, be the topic beer sales or asset pricing. And, revealingly, it is now empirically oriented graduate students who are the hot property when top doctoral programs seek to hire new faculty.

Not surprisingly, the best students have responded. The top young economists are, increasingly, empirically oriented. They are concerned not with theoretical flights of fancy but with the facts on the ground. To the extent that their work is rooted concretely in observation of the real world, it is less likely to sway with the latest fad and fashion. Or so one hopes.

The late twentieth century was the heyday of deductive economics. Talented and facile theorists set the intellectual agenda. Their very facility enabled them to build models with virtually any implication, which meant that policy makers could pick and choose at their convenience. Theory turned out to be too malleable, in other words, to provide reliable guidance for policy.

In contrast, the twenty-first century will be the age of inductive economics, when empiricists hold sway and advice is grounded in concrete observation of markets and their inhabitants. Work in economics, including the abstract model building in which theorists engage, will be guided more powerfully by this real-world observation. It is about time.

Should this reassure us that we can avoid another crisis? Alas, there is no such certainty. The only way of being certain that one will not fall down the stairs is to not get out of bed. But at least economists, having observed the history of accidents, will no longer recommend removing the handrail.

Ah, it was the Gaussian Copula Function!

Mispriced risk.  The global economic contagion was created by mispricing (read, underestimating) risk.  And apparently this is all a Canadian-educated Chinese mathematician's fault (way to externalize blame!)

So, for me the question is how do governments create policy regimes that encourage proper risk-management?  Obviously, powerful governments have a credible commitment problem since the costs of letting a financial institution dissolve in the face of risk bets gone bad are untenable.  But, as the parable of the Gaussian Copula Function tells us, there is little incentive for financial institutions to moderate or to be cautious.  So, our outcome seems doomed to be Pareto sub-optimal.

Even more concerning is industry and governments proclivity toward over-compensation.  When this financial mess bottoms out and we recommence our dogged climb toward ever-increasing prosperity, there will probably be a heck of a lot of regulation concerning the ways in which CMOs and credit default swaps can be created, rated, bought, and sold.  But, the underlying cause of financial blow up was not crazy mortgage-backed derivatives per se, rather risk mis-management stemmed from a deeply human tendency to discount "outliers" and treat events that occur under a certain threshold of probability as practically impossible.  And, until risk calculations take correlation seriously, financial firms in their legal obligation to produce the greatest returns for shareholders will continue to find ways to delude themselves and their clients into believing that you can make lots of money without taking on any risk.

Risky Business

Continuing the incredulous unraveling of the financial markets, AIG has become the latest recipient of US Fed and Treasury orchestrated and US tax-payer funded bailout (see here - NY Times free account needed - and here).  Adding to mass hysteria is knowledge that a prominent money market fund dipped below a $1 NAV yesterday (it sounds mundane, but basically realizes risk in the traditionally most riskless and liquid asset class available).

It's hard for most to conceptualize just how recent events on Wall Street will effect the global financial environment, mostly because the financial instruments that got us into this mess by failing to accurately assess risk are so hard to understand (see here for a basis primer on derivatives).  The proliferation of esoteric credit derivatives on the global financial market has made a lot of people a lot of money, but it's also contributed to increased opacity of information and a decrease in the ability of governments to regulate.  And now, the chickens are coming home to roost.

As Alex mentioned in his previous post, the events of the past few weeks (and really the past six months) beg the question of what appropriate regulation looks like and how to get there.  In an interconnected financial world, how much space are we willing to give to free market machinations?  When do you think the government, governments, or institutions should step in, if at all?  How will the effects of a systemic under-estimation of risk change investor behavior going forward (especially large investors like oil-rich countries and sovereign wealth funds)?