Heuristic Context-Sensitive Control of Mental Model Aggregation for Multilevel Organisational Learning

Abstract

Within organisational learning, aggregation of developed individual mental models to obtain shared mental models for the organisation is a crucial process. 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 organisational learning by adaptive networks, where the formation of a shared mental model is a form of network adaptation, the underlying aggregation process better can be controlled by a second-order adaptive dynamical process to obtain a context-sensitive way of aggregation. In this chapter it is explored how context factors can be used to model this form of second-order network adaptation. Indeed, using self-modeling networks, mental model adaptation by learning, formation or aggregation can be modeled in an appropriate manner at a first-order adaptive self-model level, whereas the control over such processes can be modeled at a second-order adaptive self-model level. In this chapter it is shown how by a second-order self-model in such a network, context-rich knowledge can be specified that is used to control the aggregation with the first-order self-model in a context-sensitive manner. This is illustrated for heuristic knowledge. Based on this control knowledge such an adaptive network model can exert context-sensitive control over the mental model aggregation process. Thus, a computational network modeling approach of organisational learning is presented in which the process of aggregation of individual mental models to form shared mental models is controlled in an adaptive context-dependent manner based on heuristic knowledge.