Two quantum game models based on the Eisert-Wilkens-Lewenstein protocol are applied to automated driving decision-making; the gate-based QG-G4 variant reports lower collision rates and higher success rates in merging and roundabout simulations.
A Subjective Model of Human Decision Making Based on Quantum Decision Theory
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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.
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Quantum game models for interaction-aware decision-making in automated driving
Two quantum game models based on the Eisert-Wilkens-Lewenstein protocol are applied to automated driving decision-making; the gate-based QG-G4 variant reports lower collision rates and higher success rates in merging and roundabout simulations.