[...]
One of the most promising options was the topic of a workshop in
Virginia at the end of June. The workshop was funded by America's
National Science Foundation and attended by a diverse bunch that
included economists from the Fed and the Bank of England, policy
advisers and computer scientists. They were there to explore the
potential of “agent-based models” (ABMs) of the economy to help learn
the lessons of this crisis and, perhaps, to develop an early-warning
system for the next one.
Agent-based modelling does not assume that the economy can achieve a
settled equilibrium. No order or design is imposed on the economy from
the top down. Unlike many models, ABMs are not populated with
“representative agents”: identical traders, firms or households whose
individual behaviour mirrors the economy as a whole. Rather, an ABM uses
a bottom-up approach which assigns particular behavioural rules to each
agent. For example, some may believe that prices reflect fundamentals
whereas others may rely on empirical observations of past price trends.
Crucially, agents' behaviour may be determined (and altered) by
direct interactions between them, whereas in conventional models
interaction happens only indirectly through pricing. This feature of
ABMs enables, for example, the copycat behaviour that leads to “herding”
among investors. The agents may learn from experience or switch their
strategies according to majority opinion. They can aggregate into
institutional structures such as banks and firms. These things are very
hard, sometimes impossible, to build into conventional models. But in an
agent-based model you simply run a computer simulation to see what
emerges, free from any top-down assumptions.
Although DSGE models are also based on microeconomic foundations,
they accept the traditional view that there exists some ideal
equilibrium towards which all prices are drawn. That this is often
approximately true is why DSGE models perform well enough in a
business-as-usual economy. They do badly in a crisis, however, because
their “dynamic stochastic” element only amounts to minor fluctuations
around a state of equilibrium, and there is no equilibrium during
crashes.
ABMs, in contrast, make no assumptions about the existence of
efficient markets or general equilibrium. The markets that they generate
are more like a turbulent river or the weather system, subject to
constant storms and seizures of all sizes. Big fluctuations and even
crashes are an inherent feature. That is because ABMs contain feedback
mechanisms that can amplify small effects, such as the herding and panic
that generate bubbles and crashes. In mathematical terms the models are
“non-linear”, meaning that effects need not be proportional to their
causes.
These non-linearities were clearly on show in the credit crunch. At
the workshop Andrew Lo of the Massachusetts Institute of Technology
presented a model of the American housing market, inspired by ABM
approaches, which showed how a fateful conjunction of rising house
prices, falling interest rates and easy access to refinancing created an
awesome burden of debt. John Geanakoplos of Yale University explained
how the debt cycle in remortgaging—high amounts of leverage during
booms, low amounts during recessions—can act like an out-of-control
pendulum to create instability. Sujit Kapadia of the Bank of England is
trying to model the web of interdependencies created by the use of
complex derivatives. These “network-based vulnerabilities” are just the
kind of thing that ABMs are good at capturing.
Model behaviour
Another big lesson of the crisis is the role of interactions between
different sectors of the economy—housing and finance, say. Although
conventional models can incorporate these, ABMs may be better tailored
to modelling specific sectors. The organisers of the Virginia
workshop—Doyne Farmer of the Santa Fe Institute and Robert Axtell of
George Mason University—wanted to explore the feasibility of
constructing an immense ABM of the entire global economy by “wiring”
many such modules together.
What might be required for such an enterprise? One vision is a
real-time simulation, fed by masses of data, that would operate rather
like the traffic-forecasting models now used in Dallas and in the North
Rhine-Westphalia region of Germany. But it might be more realistic and
useful to employ a suite of such models, in the manner of global climate
simulations, which project various possible futures. In either case,
the models would need much more data on the activities of individuals,
banks and companies.
Such data-gathering raises privacy fears but is essential.
Seismologists may not be able to forecast earthquakes precisely but it
would be deplorable if they were to resign themselves to modelling just
the regular, gradual movements of tectonic plates. Instead they have
developed ways of mapping the evolution of stress patterns, identifying
areas at risk and refining heuristics for hazard assessment. Why not do
the same for the economy?
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