Update: a shortened version of this post was published as an op-ed piece in the Philadelphia Inquirer on November 18th. See it here!
Remember the story of Dr. Frankenstein and his monster? In Mary Shelley’s original book, the doctor isn’t evil—just a brilliant scientist out to prove his talent through innovation. Quite unintentionally, by building something more complex that he can manage, Frankenstein creates the means of his own destruction, and destroys many other lives in the process. This may turn out to be the same narrative structure upon which our current financial crisis turns.
While there may indeed have been “greedy CEOs” and “reckless speculators” running amok on Wall Street, it is more plausible that those who created and benefited from recent financial innovations were just rational capitalists rather than evil geniuses bent on defrauding the public. That is, the people who brought us the Byzantine structures of the subprime market (credit default swaps, anyone?) were too busy pursuing their own self-interest and maximizing profits to realize that they were building a system that exceeded their understanding or control.
If, instead of imaginary monsters, you want to be scared of something real, try this: the biggest problem Americans face right now is that no one really understands what’s happening in the markets. The system is so complex, it overwhelms even the financial professionals and policy experts who are paid to understand it. And this suggests a very different strategy for addressing the global market crisis than those we’ve been offered: instead of using the same tools that created the mess in order to fix it, or declaring open season on CEOs and hedge fund managers, we should be applying complex systems analysis to the problem.
The science of complex systems has been around for decades, but has only recently been applied to questions about financial markets. Research centers like the Santa Fe Institute in New Mexico have pioneered the use of complex systems models to explain biological phenomena like aging and gene expression. These are instances of what the late mathematician and computing visionary Warren Weaver called “organized complexity:” they involve seemingly random events, but only because they are governed by rules and interconnections that elude current models and measurement tools. These phenomena are often misclassified as cases of “disorganized complexity” and analyzed—incorrectly and with misleading results—using tools designed for understanding random activity. Foremost among these tools are the statistical methods favored by physics and its imitators in the social sciences: economics and finance.
There is good reason to suspect that our financial experts and policy makers are not the right people to fix the current market mess. It might seem as though those who created the problems would have the most insight on what went wrong; but instead, it’s likely that the system crashed in part because of their inadequate grasp of the social forces underpinning markets. Finance is dominated by a misplaced faith in the “efficient markets hypothesis:” the theory that people don’t make prices, markets do; as a result, prices move randomly, much like particles move under the Second Law of Thermodynamics. Uncritical belief in randomness has created an impasse, such that academic research either consigns great swathes of financial behavior to the dustbin of “irrationality,” or concedes the inadequacy of their models by adopting the behavioral assumptions of sociology and psychology. In practice, the randomness-based theories embraced by finance scholars and professionals have produced debacles like Long Term Capital Management, brought to us in part by two Nobel Prize winners in economics. If that caliber of expert could do so much damage working with inappropriate models and analytical tools, why should we expect any better from their less-illustrious colleagues?
If we really want to get our financial system up and running again, we’re going to have to wade into issues that have nothing to do with random movements of particles or prices, and everything to do with the kind of “messy” social behavior excluded from analysis in economics and finance. Issues such as trust, which we need to understand in order to solve the problem of banks who won’t lend to one another. Or the challenge of pricing—something that, as we saw in last week’s sell-off and this week’s record-setting recovery, is neither random nor in any sense governed by an invisible hand. These are very practical, brass-tacks issues for which economists, finance professionals and policy-makers have dubious models, at best.
You probably know the saying, “if all you have is a hammer, everything looks like a nail.” To solve a complex problem like the market crisis, we need people with a wide array of tools at their disposal. If the US government thinks it worthwhile to employ a Chief Economist, why not someone who understands the human factors that move markets—a Chief Sociologist, or a Chief Psychologist? Historically, the argument has been that economics is a “practical,” applied social science with outstanding predictive powers, while sociology and psychology are too focused on exploration and explanation. But this view is long out of date: economics has changed dramatically since the computing revolution of the mid-20th century, becoming so theory-oriented that data from the real world is largely irrelevant.
Sociologists and psychologists, however, are almost entirely data-driven. We tackle messy practical problems head-on, without excluding the factors which make social systems complex: emotions like fear, greed or confidence that drive so much of economic behavior; trust in institutions like banks and courts of law; and the fragile, unspoken agreements we make allowing us to exchange pieces of paper for essentials like food and housing. This is the kind of expert needed to address the breakdown of a system grown too complex for its own creators.