Intentional forgetting through cognitive-computational methods of priorization, knowledge compression and contraction

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In the context of organizations knowledge structures can be considered as hyperthymestic, i.e., stored information is indistinctively available for the user, the amount of information grows monotonically, and no knowledge reduction or compactification takes place. As a result, it requires more and more time to sort out information that is outdated, irrelevant, or rarely used. Especially for large amounts of data this process requires an enormous amount of time.This project's goal is the reduction of this effortful preselection and aggregation of information leading to user's working load by using methods from cognitive science and computer science. The starting point is based on the analysis of knowledge structures in organizations, the analysis of mathematical and psychological modeling approaches of human memory structures in cognitive architectures, and to develop functions for priorization and forgetting that may help to compress and reduce the increasing amount of data. Accompanied by computational methods from knowledge representation a cognitive computational system for forgetting is developed. Such a model offers the opportunity to determine and adapt system model parameters systematically and makes them transparent for every single knowledge structure. This model for forgetting is then evaluated for its fit to a lean workflow and readjusted in an organizational test case.