Neuroeconomics is a recent consilient discipline (that is, a discipline that combines the principles of other disciplines to produce a comprehensive analysis) that measures brain activity while experimental subjects make decisions. Because the brains of all animals are "economic," that is, they have limited resources to achieve necessary goals, neuroeconomics experiments are not limited to studies of human beings, but have also employed apes, monkeys, and rodents. Economics is the study of constrained decision making, and it uses both mathematical and statistical models of the decision goals and outcomes without considering the mechanisms leading to decisions. Neuroscience has focused primarily on cataloging mechanisms without considering the purpose of decisions. For this reason, neuroeconomics is a natural combination that draws from the best of, and extends, both fields.
Decisions can be modeled mathematically with three components: a decision maker's preferences, beliefs, and constraints. Such models produce empirically testable predictions. Each of these three factors can be measured using the methods of neuroscience. Many decision models in economics predict choices quite well--for example, individuals purchasing things in competitive markets. In other cases, standard models do not predict behavior well-for instance, as in some models of strategic decisions involving other people.
Neuroeconomists have investigated both individual and social decisions in order to understand the processes behind the models that predict behavior accurately, as well as to improve the models that do not predict behavior well. Because many economic models are specified mathematically and have been studied in both the laboratory and the field, they provide sharp predictions when seeking to find the brain mechanisms involved in decisions.
Utility Functions. One of the most fundamental ideas in economics is that a person's preferences are represented by a utility function. Such a function relates as individual's experience with things to that individual's own valuation of those things. Vanilla and chocolate ice cream may cost the same amount, but one buys chocolate because one gets more utility from it. One's consumption of ice cream is constrained, or limited, by a variety of factors, for example, how much money one has. If the price of chocolate ice cream is substantially higher than that of vanilla, one may switch to vanilla. How are such things decided? Direct measurement of brain activity in monkeys has shown that brain cells (neurons) calculate utility. Brain imaging experiments have replicated this work in humans, revealing a network of regions that appear to calculate the value of different choices. Utility calculations draw on both evolutionarily old regions in the midbrain and newer cortical regions on the outer surface of the brain. The older regions appear to get the individual to focus on finding options, while the cortical areas integrate this information with prices to guide the individual toward the "best" choices. "Best" in this case means the choices that were most advantageous for producing progeny over the evolutionary history of Homo sapiens. Some of these choices, though, may be maladaptive in the modern environment. An example of a maladaptive choice is the preference of high-fat foods. During the long history of the human species, such foods were rare and were greatly valued for their high caloric content. In today's developed societies, this preference for high-fat foods (and their low cost) is producing high rates of obesity.
Standard utility maximization models also predict that people are risk averse; that is, they typically prefer a sure thing to a risky choice, even if the risky choice has a larger average payoff. Risk aversion has been localized by a number of laboratory analyses to an area of the brain called the anterior insula. This brain region makes you feel queasy when you smell rotten food, and makes your palms sweat when you are riding a roller coaster. Knowing the brain region that causes risk aversion allows scientists to understand why people vary in their responses to risk, as well as to help treat those who are pathologically risk averse or who take excessive risk, like compulsive gamblers.
This article was taken from an entry I wrote for the McGraw-Hill Yearbook of Science & Technology 2009. It will be continued in a future posting.
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