From a modeling and simulation perspective, studying dynamic systems consists of focusing on changes in states. According to the precision of state changes, generic algorithms can be developed to track the activity of sub-systems. This paper aims at describing and applying this more natural and intuitive way to describe and implement dynamic systems. Activity is defined mathematically. A generic application case of diffusion is experimented with to compare the efficiency of quantized state methods using this new approach with traditional methods which do not focus computations on active areas. Our goal is to demonstrate that the concept of activity can estimate the computational effort required by a quantized state method. Specifically, when properly designed, a discrete-event simulator for such a method achieves a reduction in the number of state transitions that more than compensates for the overhead it imposes.
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