Towards modeling of Communities of Practice (CoPs) - A Hebbian learning approach to organizational learning

This article addresses the issue of group learning, which is an emerging ph ilosophy in the field of organizational learning. Although not all groups l earn, those that do and form spontaneously have been referred to as Communi ties of Practice (CoPs). These groups appear to be very important among pro fessional and dynamically interactive organizations. Members of such groups come together mainly due to exposure to a set of shared problems, professi onal and/or social. These members interact directly and use each other as s ounding boards for new ideas and help each other learn. Both the business a nd academic fields have come to recognize CoPs as one of the most important structures in learning institutions or organizations. Identification, cult ivation and maintenance of such groups has become a key issue in the field of knowledge management. If CoPs are one of the mechanisms by which organiz ations learn then it would be useful to acquire greater insight into these groups. In this article, we propose an analytical model of CoPs based on th e neural network concept of Hebbian learning. Computer simulations are used to test the analytical model. (C) 2000 Elsevier Science Inc.