Using a neuroeconomic model of the decision process improves out-of-sample predictions

John A. Clithero
Pomona College, Claremont, CA

A basic problem in empirical economics involves using data from one domain to make out-of-sample 
predictions for a different, but related environment. When the choice data are binary, a canonical 
method for making these types of predictions is the logistic choice model. We investigate if it is possible 
to improve out-of-sample predictions by changing two aspects of the canonical approach: 1) Using 
response times in addition to the choice data, and 2) Combining them using a model from the 
neuroeconomics literature, called the Drift-Diffusion Model (DDM). We compare the out-of-sample 
prediction accuracies using real experimental data. Across several choice experiments, we find that the 
DDM method outperforms the logistic prediction method. Furthermore, although the improvement in 
prediction accuracy is small for the case in which items have very similar or very different values, the 
DDM method improves prediction accuracy substantially for intermediate cases.