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This program studied the relationship between subjective workload and human behavior and proposed a model of the dynamics of this relationship. Results of three simulation experiments are detailed in this report and show that simple linear identification algorithms are robust in online identification of noisy, nonlinear versions of the model. This model and the associated algorithms have the potential to enable online inferences of workload and could be used to prompt/invoke human aiding or automated systems to help reduce workload. Applications for such systems exist in aiding aircraft pilots; command, control, communication decision makers; and other personnel in dynamic, time constrained environments.