A Subjective Model of Human Decision Making Based on Quantum Decision Theory

TL;DR

This paper introduces a quantum decision theory-based model for predicting individual human choices in risky binary games, capturing irrational and subjective factors better than classical models and machine learning methods.

Contribution

The paper presents a novel quantum decision theory model that improves prediction accuracy of human decision making under risk and time pressure.

Findings

QDT model outperforms CPT-based models.

QDT surpasses neural networks and random forests.

Model effectively captures irrational decision aspects.

Abstract

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.