Participative Cognitive Mapping – a Method to Support the Interdisciplinary Innovative Projects of Megascience

The aim of this article is to augment the cognitive mapping procedure and that of the analysis of cognitive diversity to transform them from the research method into the instrument of decision-making support to promote the implementation of the Magacience capacity (the large-scale co-specialized mega-class research facilities for collective use) in regard of large-scale interdisciplinary projects. An important effect of the Megascience is an opportunity for multidisciplinary research and implementation of the research outcome in business sector. In the business intelligence perspective, the Megascience can be described as a distributed network of intangible assets with concentration of the tangible capital. The large cognitive diversity, information overload and the tacit knowledge peculiar to science-business interaction poses the challenges for data retrieval, information filtering and knowledge presentation in Megascience projects. This study objective is to develop decision making framework for the application of the participatory cognitive mapping to drive the decision process in the science-business interaction within the Megascience. The article meets the challenge of the developing an analytical approach for in order to stimulate the decision-making process in application to the complex interdisciplinary and innovative projects, such as, those of Megascience. The article presents an analytical approach for applying the participating cognitive mapping for the support of decision-making involving the interaction of representatives of different disciplines, as well as representatives of the business community, including the algorithm of the new management procedure. According to this approach, the data retrieval, information filtering and knowledge presentation are performed through the cycles of humanmachine interaction which are repeated until achieving the distance ratio target. This framework makes it possible to present shared knowledge/mindset of decision making team. The main advantage of the proposed framework is to offset the subjectivity in building the pool of original constructs by filtering the semi-structured big data. The results allow managers to solve the most complicated task of dealing with the complexity, not only for the complex systems of project type, but also for object type systems, such as a country, region, industry, enterprise.