  Computer modeling of human decision making is of large importance for, e.g.,
sustainable transport, urban development, and online recommendation systems. In
this paper we present a model for predicting the behavior of an individual
during a binary game under different amounts of risk, gain, and time pressure.
The model is based on Quantum Decision Theory (QDT), which has been shown to
enable modeling of the irrational and subjective aspects of the decision
making, not accounted for by the classical Cumulative Prospect Theory (CPT).
Experiments on two different datasets show that our QDT-based approach
outperforms both a CPT-based approach and data driven approaches such as
feed-forward neural networks and random forests.
