CPC18

The Idea The need for accurate quantitative predictions of human decision making is becoming more and more prominent in many real world applications. For example, to increase revenue and provide value for the customers, online retailers need to predict what type of products users may be interested to purchase. Similarly, to increase safety and efficacy,... However, Plonsky, Erev, Hazan, and Tennenholtz (2017) have since shown that a model that integrates insights from the behavioral decision research with a machine learning algorithm can provide state-of-the-art predictive performance for the CPC15 data. Specifically, the model, called Psychological Forest, employs the basic theoretical building blocks of BEAST (as well as the predictions of BEAST) as features (covariates or predictors) to be used in a supervised learning setting with a random forest algorithm (Breiman, 2001). Notice however, that Psychological Forest, unlike BEAST, was developed after the test data was already..