Department of Computer Engineering2024-11-092022978-3-030-96993-6978-3-030-96992-91860-949X10.1007/978-3-030-96993-6_52-s2.0-85127699923http://dx.doi.org/10.1007/978-3-030-96993-6_5https://hdl.handle.net/20.500.14288/7560Aggregation of individual mental models to obtain shared mental models for an organization is a crucial process for organizational learning. This aggregation process usually depends on several context factors that may vary over circumstances. It is explored how Boolean functions of these context factors can be used to model this form of adaptation. For adaptation of aggregation of mental model connections (represented by first-order self-model states), a second-order adaptive self-modeling network model for organizational learning was designed. It is shown how in such a network model, Boolean functions can be used to express logical combinations of context factors and based on this can exert context-sensitive control over the mental model aggregation process.Computer scienceArtificial intelligenceTheory methodsConference proceeding1860-9503833484200005N/A1346