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academia resist new approaches such as agent-based models.
It’s a sentiment shared by Bank of England Chief Economist
Andrew Haldane, who’s helping lead the push for alternative
research. In 2017, Haldane called his profession “insular” for its
subpar track record in citing work from other disciplines in its
academic journal papers. Farmer says the prestigious top five
economic journals rarely if ever publish worthy agent-based model
papers, including his own. The American Economic Review says
papers are evaluated on their merits without bias.
Many macroeconomists shrug off Farmer’s criticism. He’s
a physicist, they say, not an economist, and agent-based models
have yet to provide real-world verifiable results.
“In science you need a little bit of the rebel, and Doyne is
definitely that,” says Georg, who’s also the South African Reserve
Bank chair in financial stability studies at the University of Cape
Town. “He challenges you to think about your assumptions.”SINCE MISSING WARNING SIGNS of the Great Recession, economists
have improved their DSGE (dynamic stochastic general equilib-
rium) models, which remain the workhorses of central bank
forecasting. The approach is top-down, aggregating the behavior
of the economy into a few representative agents—a household,
a firm, and a government.
Most precrisis versions at central banks didn’t include a
financial sector because it wasn’t considered relevant in making
aggregate forecasts for measures such as gross domestic product
and inflation, says Frank Schorfheide, chair of the economics
department at the University of Pennsylvania and a visiting scholar
with the Federal Reserve Banks in Philadelphia and New York.
Since then, some economists have added a financial sector
to capture its impact on the economy. “There were certainly many
aspects of the financial crisis that were not on the radar screen of
things to monitor by central banks that turned out to be important,”
Schorfheide says. “People quickly learned what kind of data theyxford University professor Doyne Farmer traces his
research exposing risks in the financial system to the
roulette wheels of Las Vegas.
In the 1970s, Farmer and two fellow physics
students at the University of California at Santa Cruz built a com-
puter small enough to hide in a shoe that helped them predict
roughly where roulette balls would land. At casinos in Vegas, they
communicated with toe-controlled switches and transmitters,
also in their shoes, about what bets to make. The gadget was legal,
but they feared their winnings—about a 20% return on their
wagers—would lead to trouble. So they quit after a couple of years.
“We were nervous about getting our kneecaps broken,”
he explains.
Today, in a more bucolic setting—the Institute for New Eco-
nomic Thinking at the Oxford Martin School—Farmer is drawing
on decades of complexity research that began with roulette. After
winning acclaim as a pioneer of chaos theory, which helps explain
the unpredictability of complex systems such as the weather, he
jumped into markets, co-founding one of the early quantitative
investment firms in the 1990s. Now, Farmer and a band of central
bank researchers are focusing on the tangled web of global finance,
using a tool of the natural sciences called agent-based models to find
dangers lurking in the system and uncover ways to avoid them.
Agent-based models, used in fields from biology to sociology,
are bottom-up, simulating the messy interactions of hundreds and
even millions of agents—human cells or attitudes or financial
firms—to explain the behavior of a complex system. The nonlin-
ear interplay can produce unexpected phenomena, such as eco-
nomic booms and busts, providing insights into the causes of events
and the best responses. Epidemiologists have successfully deployed
the models for years to test strategies to control everything from
obesity to the spread of infectious diseases, including the flu.
Central banks worldwide began experimenting with the
agent-based approach after macroeconomists and their standard
models were blindsided by the 2008 financial crisis. The European
Central Bank, where Farmer is a consultant, as well as central
banks in Canada, Germany, South Africa, and the U.K. have taken
the lead in building the models to research financial risk. The U.S.
Federal Reserve’s regulatory staff is also exploring their use.
Today, central banks mostly stress-test financial firms indi-
vidually. But agent-based models are giving regulators a better read
by accounting for the systemwide impact of shocks. In the simula-
tions, a shock to a single firm cascades through the network of banks
and asset managers, creating feedback loops that significantly amplify
the initial losses. It’s the kind of contagion that a decade ago spread
from the U.S. subprime mortgage market through lenders, money
managers, and insurers, creating a liquidity crisis that doomed
Lehman Brothers Holdings Inc. and infected the global economy.
“There are efforts at central banks to integrate systemic
stress testing,” says Co-Pierre Georg, a research economist at
Germany’s central bank (but he doesn’t speak for it). “The banks
are highly interconnected and highly leveraged. We now know
from Lehman that if something happens to one big bank it can be
devastating to the entire economy.”
Farmer, 67, a gray-bearded scientist whose papers have
garnered more than 34,500 citations, can be a provocateur. He
sees central banks as a beachhead for a bigger challenge to
mainstream economics. In a coming book, he says economists in
Forecast vs. Reality
Federal Reserve growth forecasts have become more accurateRange of the Fed’s GDP forecasts*
Published in July, preceding year
Published in February, indicated yearReal GDP36%2000 2019-364 BLOOMBERG MARKETS