A dual-process cognitive model for testing resilient control systems

Abstract

We describe an agent-based model of individual human behavior that combines a dual-process architecture with reactive planning and mental models in order to capture a wide range of human behavior, including both behavioral and conceptual errors. Human operator behavior is an important factor in resilient control of systems that has received relatively little attention. Models of human behavior and decision making are needed in order to test existing control systems under a range of conditions or analyze possible new approaches. While the model we describe has been developed and applied in the area of cyber security, it is relevant to a wide range of resilient control systems that include human operation. We discuss an application to modeling operator behavior in a nuclear power plant.