Dynamic Decision-Making in Multi-Task Environments: Theory and Experimental Results

摘要

The recent trend towards higher levels of automation in complex systems, such as in nuclear power plants, air-traffic control and flight management, is changing the role of the human operator from one of a controller to one of a supervisory decision-maker. The operator's primary responsibility in this new role is to extract information from his environment, and to integrate it for action selection and its implementation. The present analytic and experimental research has sought to understand human monitoring, information-processing and task selection procedures in dynamic multi-task environments, as a preliminary step towards analyzing and evaluating the human component of a supervisory control system. A simple yet realistic computer representation of the supervisory decision situation is developed. The experimental paradigm retains the essence of the multi-task decision problem by presenting the human with a dynamic situation wherein tasks of different value, time requirement and deadline compete for his attention. Via this framework, the effects of various task related variables on the human decision-processes are studied. In order to validate the model, several time-history and scalar measures of performance are proposed. Excellent model-data agreement is obtained for all the experimental conditions studied. Moreover, the model has been shown to represent human decision behavior significantly better than several heuristic sequencing rules of scheduling theory. The model has the potential for use in computer-aiding, and could form a significant step towards the modeling of multi-human behavior in complex, multi-level, multi-task systems.