The Crash-Test Solution We now know that few people saw the downturn coming. Scientists are working to make sure that never happens again.

Anyone who read Nassim Nicholas Taleb's bestseller The Black Swan will probably regard the global financial meltdown as proof of Taleb's point. He argued that it's not the normal events-the mundane and expected "white swans"-that drive socioeconomic history but the magnificent outliers, the completely unexpected "black swans." Think September 11, 2001, or the invention of the internet. Human history pivots on the rare seismic shifts that no one predicts or even has a chance of predicting.

Life is good for New York University's party-boy economist.
In recent months, we've seen the real estate bust, the implosion of hedge funds, and the demise of investment-banking giants like Lehman Brothers, Merrill Lynch, and Bear Stearns. Surely this economic collapse was a black swan, as unpredictable as it was rare?

Maybe not.

A small but growing cadre of scientists are arguing that our current crisis was in fact predictable and that the technology exists to make sure that it won't happen again. The problem may be that we've used only economists to try to solve our economic predicaments. Instead, the solution may be found by physicists and other scientists accustomed to studying complex systems.

To anticipate the next crisis and find our way out of this one, we may have to cast off economic and financial dogma and adopt ideas inspired by physics and other natural sciences, disciplines in which the notion of unstable and unpredictable systems is nothing new. For instance, the technology now exists to go beyond economics to build a massive, complete computer model of the modern economy, from the corner store to the city bank and the Federal Reserve.

With such a model, physicists would be able to track changes in the economy dynamically. There have even been calls for an ambitious effort akin to the Manhattan Project, which built the atomic bomb, to bring the most sophisticated mathematics and computer modeling to bear on managing the world's economies more aggressively than has ever been attempted.

Some of the work has already begun. When the state of Illinois decided to deregulate its electricity market, it wanted to avoid the disastrous outcome California suffered after Enron Corp. manipulated prices, created shortages, and spurred rolling blackouts. So the state hired scientists at Argonne National Laboratories to build a sophisticated model of Illinois' power market, incorporating suppliers, consumers, regulators, and the like. The model showed that Illinois' initial market design was vulnerable to Enron-like manipulation. Having learned this lesson in virtual reality, the state was able to change its approach before embarking on deregulation in the real world. The state made its changes and so far has avoided even a hint of California-style problems.

Generally, economists have been loath to use such techniques. "We're not currently using the best capabilities of science," says physicist Dirk Helbing, who leads a new division devoted to social modeling at the Swiss Federal Institute of Technology in Zurich. "We need to bring together scientists from different fields and put together tools that can be used as a kind of wind tunnel for testing out social and economic policies."

The idea is that by studying so-called complex systems-traffic flow, ecosystems, organisms, weather-we can begin to make sense of an increasingly unpredictable economic world. Didier Sornette, for instance, is a world expert on earthquakes. Now he's heading up a lab in Zurich called the Financial Crisis Observatory, examining how frothy markets show the same signs of stress that the earth shows before an earthquake. Sornette's group is trying to develop the ability to provide economic warnings, in part by monitoring the stocks of the 500 largest U.S. companies.

In addition, the group has studied the real estate market in hopes of finding signs of coming collapse. By looking at the prices of new homes sold in the U.S. in 2005, the group's models predicted the bubble that eventually formed, particularly in the Northeast and the West.

At the Santa Fe Institute, Yale economist John Geanakoplos has teamed up with two physicists to look at the natural competition that emerged among hedge funds as they competed to attract investors. The group is examining how hedge funds took on additional leverage during that process-and how the state of the market changed fundamentally as a result of the added debt.

This way of thinking is foreign to mainstream economic theory, which assumes that people, firms, and other economic agents act rationally. Markets are presumed to exist in economic equilibrium, a more or less stable balance achieved through the players' varying aims. "It's completely unrealistic," says finance professor Andrew Lo of the Massachusetts Institute of Technology. "Economists like the model because it can be solved, but people would have to be superhuman in their intelligence to fit the assumptions."

The evident failure of these assumptions put former Federal Reserve chairman Alan Greenspan into an intellectual tailspin, as his recent testimony before Congress shows. "Those of us," Greenspan admitted, "who have looked to the self-interest of lending institutions to protect shareholders' equity (myself especially) are in a state of shocked disbelief."

Other economists are finally beginning to acknowledge that basic notions such as equilibrium-an idea originally imported into economics from 19th-century physics-aren't adequate to understanding complex markets.

Consider financial derivatives, for example. It's taken for granted by economists that derivatives make markets more stable. They are designed to give market participants more flexibility by allowing them to take highly specific market positions. But some economists-notably William Brock of the University of Wisconsin and colleagues-have suggested that this view may be backward. They are exploring the consequences of adding one rather obvious fact to standard economic models: that people learn as they participate in markets and may quickly copy other investment strategies if they seem to be working. The result is a pile-on that makes the initial strategy ineffective. Brock's results show that such adaptive learning leads to derivatives actually destabilizing markets.

A similar conclusion emerges from mathematical physics too. Over the past 30 years, physicists have developed methods for calculating the properties of what they call disordered systems, which have a range of linked components. Adapting these techniques to markets, statistical physicist Matteo Marsili of the Abdus Salam International Centre for Theoretical Physics in Trieste, Italy, showed recently that the proliferation of derivatives inevitably produces an unstable market-a finding that traditional economists have acknowledged only in hindsight.

Marsili emphasizes that we still know next to nothing about the overall consequences for markets from the use of derivatives. Both Brock and Marsili point out that derivatives can be used beneficially to hedge risk, even if they're often used in practice to leverage positions and thereby increase risk. To know when and how derivatives can be used safely, investors must, at the very least, take the issue of systemic risk seriously.

Mauro Gallegati, of the University of Ancona, in Italy, says that even without the subprime problem, instability in the global credit markets would have eventually prompted a meltdown of some sort. The credit network had become so highly interlinked that all participants were, as he put it, "very fragile with respect to the possible collapse of their partners." Traditional central-bank controls and banking regulations just weren't up to controlling such instability.

Gallegati believes that governments will need to take a much more ambitious approach in the future. Rather than just looking at individual banks' lending practices to see if their risks are at acceptable levels, regulators will need to take a more holistic view, monitoring the nature of the links between institutions and the overall stability of the credit network.

"These networks have to be checked," Gallegati says.