Critics of conventional economic theory have never had it so good: the credit crunch has left orthodox thinking, embraced by most of the establishment, a sitting duck. But can alternative economic models help us to anticipate such crises, or better manage them? In short, can the “dismal science” be made truly scientific? In summer 2007 Frederic Mishkin, a governor of the Federal Reserve, forecast that the banking problems triggered by stagnation of the US housing market would be a minor blip. His prediction was based on the most orthodox theoretical framework: dynamic stochastic general equilibrium (DSGE) models. The subsequent near-collapse of the global market represents the kind of failure that would have buried any theory in the natural sciences. But it has not done so here. Why? Nobel laureate Robert Lucas explains that the theory is explicitly not designed to handle crashes, so of course it will not predict them. That’s not a shortcoming of the models, Lucas says, but a reflection of the fact that crashes are inherently unpredictable by this (or any other) theory. But there are models of financial markets that generate crashes. Fluctuations ranging from the quotidian to the catastrophic are an intrinsic feature of some models that dispense with the simplifying premises of DSGE, and instead try to construct market behaviour from the bottom up. Taking advantage of modern computing power, these models create simulations of interactive “agents”: individuals who trade with one another according to specified decision-making rules, while responding to each others’ decisions. The approach has been used successfully to understand and predict traffic flow and to improve models of contagion in epidemics. Now a handful of economists, along with interlopers from the natural sciences, believe that these agent-based models (ABMs) offer the best hope of understanding the economy in all its messy glory. At a workshop in Virginia in June, sponsored by the National Science Foundation, I heard how ABMs might help us learn the lessons of the crisis, anticipate and guard against the next one, and even offer a model of the entire economic system. Some aspects of ABMs are so obviously an improvement on conventional economic theories that it seems bizarre that they are still marginalised. Agents, like real traders, can behave in diverse ways. They can learn from experience. They are affected by each other’s actions, potentially leading to the herd behaviour that undoubtedly afflicts markets. ABMs, unlike DSGE models, can include institutions such as banks (a worrying omission, you might imagine, in models of financial markets). Some of these factors can be incorporated into orthodox theories, but not easily or transparently—and often they are not. What upsets traditional economists most is that ABMs are “non-equilibrium” models, which means they may never settle into a steady state where prices adjust to perfectly balance supply and demand. Conventional economic thinking has, more or less since Adam Smith, been based on this platonic ideal, which is ruffled by external “shocks” such as political events. In its most simplistic form, this perfect market demands laissez-faire free trade, and is hindered by any regulation or intervention. In ABMs, “imperfections” and “failures” are a natural, emergent feature, not an aberration. This posits a totally different view of how the economy works. The Virginia meeting, however, was relatively heedless of the battle-lines between conventional and alternative thinkers. Committed agent-based modellers mixed with researchers from the Federal Reserve, the Bank of England and the Rand Corporation, specialists in housing markets and policy advisers. The goal was both to unravel the lessons of the global crisis and to discuss the feasibility of making immense ABMs with predictive capability. That would be a formidable task, requiring the collaboration of many experts and costing tens of millions of dollars. Even then, it would probably take at least five years to have a model up and running. Once, that would have seemed a lot to gamble on. Now, with the bill from the crisis running to trillions, it would border on the irresponsible to refuse the investment. Could such a model predict the next crisis? That’s the wrong question to ask. We must instead identify where the systemic vulnerabilities lie, what regulations might mitigate them, and whether early-warning systems could spot danger signs. We’ve done it for climate change. Does anyone now doubt that economic meltdown poses comparable risks and costs?
