Andrew Caplin

ISDM Deputy Director
Professor of Economics

My Ph.D. at Yale was supervised by Professors William Nordhaus, Herbert Scarf, and James Tobin. The diverse intellectual make-up of the committee reflects my broad combination of interests, which has driven me to investigate new approaches to measuring and modeling individual behavior and its aggregate consequences. In particular, Paul Glimcher and I have been jointly running the NYU “Seminar in Neuroeconomics’ for some 10 years now. This has enabled me to dig in an increasingly granular and data-centered fashion into the forces that underlie decision making in humans. At the same time, I remain firmly of the view that theoretical analysis is of central importance to progress. In holding to this largely shared vision, neuroeconomists at NYU have made great strides not only at the substantive but also at the methodological level. and I believe that this foundational work will bear great fruit in the decades to come.

At present my co-authors and I are engaged in three projects that advance the broad neuroeconomic agenda. The first involves modeling and measuring features of the perceptual process (e.g. tracking time to decide, how decisions change as a decision frame changes, etc). By enriching our measurements beyond classical choice data and developing complementary models, we gain traction into how humans make choices based on their subjective representation of the outside world.  The second involves the use of novel survey methods to enrich our understanding not only of what people actually do, but what they would do under changed circumstances. Counter-factual data of this kind is crucial to all forms of policy work. In the past, economists have been reluctant to ask direct questions of those whose behavior we are trying to understand. That is about to change. In the coming decades measurement techniques in this area will be revolutionized as we improve the design and testing of  “strategic survey questions” that investigate precisely such counter-factual behaviors. The third involves analyzing biological factors and their behavioral counterparts. Smoking behavior is of particular interest at present.

One common feature of all the work in which I am currently engaged is the massive size of the data sets needed to adequately capture behavior at the individual level, at the household, group, and even more so at the societal level. For that reason, there will be an intimate interaction between advances in neuroeconomics and the nascent “Big Data” revolution. Stay tuned. It should be a fun ride.