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Traditional economic theory assumes that human beings behave rationally. That is, that they understand their own preferences, make perfectly consistent choices over time, and try to maximize their own well-being. This peculiar assumption has its roots in dusty essays like “Exposition of a New Theory on the Measurement of Risk” (from 1738) by Daniel Bernoulli and scholarly tomes like Theory of Games and Economic Behavior by John von Neumann and Oskar Morgenstern (published in 1944). The idea has some validity: traditional economic theory is good at predicting some market behaviors, such as how the demand for products like gasoline will change after a tax hike. But it’s not very good at describing more-complex phenomena like stock-price fluctuations or why people gamble against the odds.

The problem, of course, is that people don’t always behave rationally. They make decisions based on fear, greed, and envy. They buy plasma TVs and luxury vehicles they can’t afford. They don’t save enough for retirement. They indulge in risky behavior such as gambling. Economists understand this as well as anyone, but in order to keep their mathematical models tractable, they make simplifying assumptions. Then they try to adjust their equations by adding terms that account for “irrational” behavior. But if economists could develop models that accounted for the subtleties of the human brain, they might be able to predict complex behaviors more accurately. This, in turn, might have any number of practical applications: investment bankers could hedge against financial euphoria like the Internet boom; advertisers could sell products more winningly.

The idea that understanding the brain can inform economics is controversial but not new; for 20 years, behavioral economists have argued that psychology should have a greater influence on the development of economic models. What is new is the use of technology: economists, like other researchers, now have at their disposal powerful tools for observing the brain at work. The most popular tool, functional magnetic resonance imaging (fMRI), has been around since the late 1980s; but only in the past few years has it been used to study decision-making, which is the crux of economic theory.

The result is the emerging field of “neuroeconomics.” A flurry of recent papers in scientific and economic journals – reviewed in the Journal of Economic Literature by Caltech economics professor Colin Camerer and colleagues – shows how researchers are using the neural basis of decision-making to develop new economic models. At the January meeting of the American Economic Association, the world’s largest economics conference, the neuroeconomics sessions were reportedly standing room only. The hope seems to be that biological research will finally help economists make sense of irrationality.

Take recent brain-imaging experiments by Princeton University psychologist Samuel McClure. In the journal Science, ­McClure and colleagues report that when subjects choose short-term monetary rewards, different regions of the brain are active than when they choose long-term ones. People don’t “discount” future rewards according to a simple scheme, as many economists have suggested. It seems the brain actually makes short-term and long-term forecasts in different ways. The challenge for economists lies in translating this sort of scientific insight into, say, predictive models of how people plan purchases or make retirement fund decisions.

If successful, neuroeconomics could help unify the social sciences and natural sciences – all with great societal impact. “We are at the very beginning of something radically new,” says Daniel Kahneman, the Princeton University psychologist who won the 2002 Nobel Prize in economics. “Technologically, we can expect that within the next decade or two there will be huge developments. The network of knowledge about the brain is expanding at a tremendous rate. That will certainly affect marketing and political psychology, and it could create a common database that nobody will want to ignore.”

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Tagged: Biomedicine

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