In today’s high-tech age, one naturally
assumes that US President Barack
Obama’s economic team and its international
counterparts are using sophisticated
quantitative computer models
to guide us out of the current economic
crisis. They are not.
The best models they have are of two
types, both with fatal flaws. Type one is
econometric: empirical statistical models
that are fitted to past data. These successfully
forecast a few quarters ahead
as long as things stay more or less the
same, but fail in the face of great change.
Type two goes by the name of ‘dynamic
stochastic general equilibrium’. These
models assume a perfect world, and by
their very nature rule out crises of the
type we are experiencing now.
As a result, economic policy-makers
are basing their decisions on common
sense, and on anecdotal analogies to
previous crises such as Japan’s ‘lost
decade’ or the Great Depression (see
Nature 457, 957; 2009). The leaders of
the world are flying the economy by the
seat of their pants.
[...]
There is a better way: agent-based models.
An agent-based model is a computerized simulation
of a number of decision-makers (agents)
and institutions, which interact
through prescribed rules. The agents
can be as diverse as needed — from
consumers to policy-makers and Wall
Street professionals — and the institutional
structure can include everything
from banks to the government. Such
models do not rely on the assumption
that the economy will move towards
a predetermined equilibrium state, as other
models do. Instead, at any given time, each
agent acts according to its current situation, the
state of the world around it and the rules governing
its behaviour. An individual consumer,
for example, might decide whether to save or
spend based on the rate of inflation, his or her current optimism about the future, and behavioural
rules deduced from psychology experiments.
The computer keeps track of the many
agent interactions, to see what happens over
time. Agent-based simulations can handle a far
wider range of nonlinear behaviour than conventional
equilibrium models. Policy-makers
can thus simulate an artificial economy under
different policy scenarios and quantitatively
explore their consequences.
[...]
But there is a still larger problem. Even if
rational expectations are a reasonable model of
human behaviour, the mathematical machinery
is cumbersome and requires drastic simplifications
to get tractable results. The equilibrium
models that were developed, such as those used
by the US Federal Reserve, by necessity stripped
away most of the structure of a real economy.
There are no banks or derivatives, much less
sub-prime mortgages or credit default swaps
— these introduce too much nonlinearity and
complexity for equilibrium methods to handle.
When it comes to setting policy, the predictions
of these models aren’t even wrong, they are simply
non-existent (see Nature 455, 1181; 2008).
Agent-based models potentially present
a way to model the financial economy as a
complex system, as Keynes attempted to do,
while taking human adaptation and learning
into account, as Lucas advocated. Such models
allow for the creation of a kind of virtual universe, in which many players can
act in complex — and realistic —
ways. In some other areas of science,
such as epidemiology or traffic control,
agent-based models already help
policy-making.
[...]
Such economic models should be able to
provide an alternative tool to give insight
into how government policies could affect
the broad characteristics of economic performance,
by quantitatively exploring how
the economy is likely to react under different
scenarios. In principle it might even be possible
to create an agent-based economic model
capable of making useful forecasts of the real
economy, although this is ambitious.
Creating a carefully crafted agent-based
model of the whole economy is, like climate
modelling, a huge undertaking. It requires
close feedback between simulation, testing, data
collection and the development of theory. This
demands serious computing power and multidisciplinary
collaboration among economists,
computer scientists, psychologists, biologists
and physical scientists with experience in largescale
modelling.
Fonte:
The economy needs agent-based modelling- J Doyne Farmer, Duncan Foley-2009/8/6-Nature-Nature 460, 685-686 (6 August 2009) | doi:10.1038/460685a; Published online 5 August 2009
A expressão “sair do vermelho” significa na contabilidade fazer com que o resultado seja positivo. Se você colocar a expressão no Google irão aparecer muitos resultados com dicas para fazer com que seu orçamento pessoal seja mais equilibrado. Em inglês existe a expressão “be in the black” ou mostrar um balanço positivo. É interessante notar que ao contrário da expressão negativa tradicional, o “black” aqui adquire uma conotação positiva. E o vermelho seria uma cor que representa algo ruim. O inglês também admite a expressão “be in the red”, que pode ser traduzida como perder dinheiro. 
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