Goal setting mechanism in Petri net models of human decision making

To support the human factors engineer in designing a good interactive system, a method has been developed to analyze the empirical data of the interactive decision behavior described in a finite discrete state space. The sequences of decisions and actions produced by users contain much information about their mental models, the individual problem solution strategy for a given task and the underlying decision structure. We distinguish between: 1) the logical structure; 2) the sequential goal structure; and 3) the temporal structure. The analyzing tool AMME can handle the recorded decision and action sequences and automatically extracts a net description of the task dependent decision model (the logical structure). This basic model is extended by further elements to reconstruct an empirical expert user sequence. This article presents two modeling strategies: parallel versus event-driven goal setting processes. Both strategies add sequential structure to the logical structure. Three different models are presented and their predictive power is discussed.

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