State-Space-Based Analysis of Human Decision Making in Air Traffic Control

Abstract

The paper presents new methods for the analysis of human decision making using state space data derived from Coloured Petri Net simulation models. It is argued that most existing environments for human in the loop simulations are good at tracking actual decisions. However, they allow an insufficient understanding of all available choices at hand in dynamic situations. Alternative system states that could have been reached often remain unknown. The presented state-space methods can provide a deeper understanding of human decision making data and support a better understanding of human errors in the interaction with complex systems.