Control-oriented approaches to dynamic decision making
ROCOM'08 Proceedings of the 8th WSEAS International Conference on Robotics, Control and Manufacturing Technology
Control-theoretic results on dynamic decision making
WSEAS Transactions on Systems and Control
An integrated human decision making model for evacuation scenarios under a BDI framework
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Adaptive attention allocation support: effects of system conservativeness and human competence
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
A framework for explaining reliance on decision aids
International Journal of Human-Computer Studies
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Appropriate trust in and reliance on automation are critical for safe and efficient system operation. This paper fills an important research gap by describing a quantitative model of trust in automation. We extend decision field theory (DFT) to describe the multiple sequential decisions that characterize reliance on automation in supervisory control situations. Extended DFT (EDFT) represents an iterated decision process and the evolution of operator preference for automatic and manual control. The EDFT model predicts trust and reliance, and describes the dynamic interaction between operator and automation in a closed-loop fashion: the products of earlier decisions can transform the nature of later events and decisions. The simulation results show that the EDFT model captures several consistent empirical findings, such as the inertia of trust and the nonlinear characteristics of trust and reliance. The model also demonstrates the effects of different types of automation on trust and reliance. It is possible to expand the EDFT model for multioperator multiautomation situations