Oxford 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 computer 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 Economic 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 nonlinear interplay can produce unexpected phenomena, such as economic 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.
Diese Geschichte stammt aus der October - November 2019-Ausgabe von Bloomberg Markets.
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