A Subjective Model of Human Decision Making Based on Quantum Decision Theory
This work addresses the need for more accurate computer modeling of human decision making, which is important for applications such as sustainable transport and online recommendations, by incorporating irrational and subjective aspects not captured by existing models.
The authors tackled the problem of predicting individual behavior in binary games under varying risk, gain, and time pressure by developing a model based on Quantum Decision Theory (QDT), which outperformed classical Cumulative Prospect Theory (CPT) and data-driven methods like neural networks and random forests on two datasets.
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.