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’ choices for money. Those parameter estimates were modestly correlated (across subjects) with the activity in the right LOFC observed in fMRI brain imaging; that is, the brains of subjects with low parametric ambiguity aversion showed less activity in the LOFC. Furthermore, by extrapolating the correlation between the choice parameter and the brain imaging activity to patients with no activity in the relevant area (LOFC), the researchers theorized that patients with lesions in that area would behave as if they had a particular numerical parameter—namely, 0.85. Later experiments involving patients with damage in the same area, as revealed by fMRI, yielded a numerical estimate of 0.82 based on their choices. In short, the fMRI measurement used activity in a region to predict a choice parameter value for lesion patients that actually predicted the later choices of a separate group of subjects.

WHAT CAN NEUROECONOMICS DO FOR ECONOMICS?

I am optimistic about the potential of neuroeconomics in the long run. Why? For one thing, a variety of methods can be used in research in this area, and many of these can be combined. For example, psychophysiological measurements (e.g., skin conductance), eye-tracking, electroencephalography, fMRI, tools for studying brain connectivity, and tools for causally influencing behavior (e.g., temporarily disrupting function using transcranial magnetic stimulation, analyzing pharmacological changes) are strongly complementary. The variety of approaches usually means that an obvious limit of one method can be overcome by another method.

Another reason for my optimism is that successful collaborations between neuroscientists and economists are already flourishing across remarkable boundaries. Many neuroscientists are interested in economic paradigms as models of higher-order cognition and social interaction, and as tools for studying pathology and disorder. The editorial process in neuroscience also encourages exploration and tends to be rapid and effective. (In contrast, publishing in economics journals seems to be slower and is often more frustrating.)

My optimism is also fueled by the fact that skeptical reactions are either expressions of perceived boundaries between fields (as defined by their traditional tools) or claims about what cannot be done that will eventually be overcome by empirical results and theory development.

I should add, however, that several elements of economic valuation and exchange, while clearly important, have been left aside because they do not fit the preferences-information-constraint framework. These topics include attention, emotion, conceptual association (as in advertising) and categorization, personal interaction in service purchases, fear and panic in insurance, health, and macroeconomic activity. Knowing more about these basic processes from a cognitive neuroscience perspective could inspire interesting extensions of economic theory.

Howard Kunreuther during his career has led by example in using a variety of tools, including surveys, naturalistic studies of business practice (surprisingly rare in economics), and experiments—whatever it takes to learn something. Neuroscience is similarly opportunistic.

RECOMMENDED READING

Camerer, C. F. (2003). Behavioral Game Theory: Experiments on Strategic Interaction. Princeton: Princeton University Press.

Crawford, V. P. (2008). “Look-Ups as the Windows of the Strategic Soul: Studying Cognition via Information Search in Game Experiments.” In A. Caplin and A. Schotter, eds. Perspectives on the Future of Economics: Positive and Normative Foundations . Oxford: Oxford University Press.

Hsu, Ming, Meghana Bhatt, Ralph Adolphs, Daniel Tranel, and Colin F. Camerer (2005). “Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making.” Science 9 (December): 1680-1683.

Johnson, Eric, Colin F. Camerer, Sankar Sen, and Talia Rymon (2002). “Detecting Failures of Backward Induction: Monitoring Information Search in Sequential Bargaining.” Journal of Economic Theory 104, no. 1: 16-47.

Rangel, A., C. Camerer,