Enhancing the Computational Collective Intelligence within Communities of Practice Using Trust and Reputation Models

Abstract

Knowledge assets are a critical resource that can generate a competitive advantage for organizations. Generally, knowledge can be divided in explicit knowledge and tacit knowledge. Organizations focus on managing the explicit knowledge, but also on capturing the tacit knowledge embedded in the individuals’ experiences. Through the interactions in social networks, community-based knowledge development has become a very effective tool. In this context, more and more organizations are developing communities of practice as a strategic tool for knowledge development and sharing within the organization and across organizational boundaries. In the last years numerous contributions and approaches pointed out the importance of communities of practice in the knowledge economy. The most relevant argument is that communities of practice are the core of collective learning and collective intelligence, relaying on a permanent exchange of knowledge and information related to the practice. Communities of practice enhance particular knowledge that exist in the organizations and contribute to its coherence. Communities of practice can provide a social reservoir for practitioners, knowledge producers and policy makers to analyze, address and explore new solutions to their problems. These communities are emerging in knowledge-based organizations. They can enhance the efficiency of production and can improve the innovative processes. The paper addresses the new trends and challenges of knowledge dynamics within communities of practice and examines the emergence of this type of communities. We show how computational techniques enhance collective intelligence within communities of practice and suggest a way to model communities’ dynamics. The main objective of the paper is to simulate computational collective intelligence using agent-based models.