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.

翻译：人类决策的计算机建模对于可持续交通、城市发展和在线建议系统等非常重要。在本文中,我们提出了一个模型,用以预测个人在风险、收益和时间压力不同程度的二进制游戏中的行为。模型以量子决定理论(QDT)为基础,已证明它能够模拟决策的非理性和主观方面,而传统的累积前景理论(CPT)没有考虑到这一点。对两个不同的数据集的实验表明,基于QDT的方法超过了基于CPT的方法和数据驱动的方法,如进食-向神经网络和随机森林。