  A large body of work in behavioral fields attempts to develop models that
describe the way people, as opposed to rational agents, make decisions. A
recent Choice Prediction Competition (2015) challenged researchers to suggest a
model that captures 14 classic choice biases and can predict human decisions
under risk and ambiguity. The competition focused on simple decision problems,
in which human subjects were asked to repeatedly choose between two gamble
options.
  In this paper we present our approach for predicting human decision behavior:
we suggest to use machine learning algorithms with features that are based on
well-established behavioral theories. The basic idea is that these
psychological features are essential for the representation of the data and are
important for the success of the learning process. We implement a vanilla model
in which we train SVM models using behavioral features that rely on the
psychological properties underlying the competition baseline model. We show
that this basic model captures the 14 choice biases and outperforms all the
other learning-based models in the competition. The preliminary results suggest
that such hybrid models can significantly improve the prediction of human
decision making, and are a promising direction for future research.
