Using Boolean Functions of Context Factors for Adaptive Mental Model Aggregation in Organisational Learning

Aggregation of developed individual mental models to obtain shared mental models for the organization is a crucial process for organizational learning. This aggregation process usually does not only depend on the mental models used as input for it, but also on several context factors that may vary over circumstances and time. This means that for computational modeling of organizational learning the aggregation process better can be modeled as an adaptive dynamical process where adaptation is used to obtain a context-sensitive outcome of the aggregation. In this paper it is explored how Boolean functions of these context factors can be used to model this form of adaptation. Using self-modeling networks, mental model adaptation by learning, formation or aggregation can be modeled in an appropriate manner at a first-order self-model level, whereas the control over such processes can be modeled at a second-order self-model level. Therefore, for adaptation of aggregation of mental models in particular, a second-order adaptive self-modeling network model for organizational learning can be used. In this paper it is shown how in such a network model at the second-order self-model level, 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. Thus, a computational network model of organizational learning is presented in which the process of aggregation of individual mental models to form shared mental models takes place in an adaptive context-dependent manner based on any Boolean combinations of context factors.