K. Aberer et al. (Eds.): SocInfo 2012, LNCS 7710, pp. 246–259, 2012. © Springer-Verlag Berlin Heidelberg 2012 A Foresight Support System to Manage Knowledge on Information Society Evolution Andrzej M.J. Skulimowski 1,2 1 AGH University of Science and Technology, Chair of Automatic Control and Biomedical Engineering, Decision Science Laboratory, Al. Mickiewicza 30, 30-050 Kraków, Poland 2 International Centre for Decision Sciences and Forecasting, Progress & Business Foundation, 30-041 Kraków, Poland ams@agh.edu.pl Abstract. In this paper we present an intelligent knowledge fusion and decision support system tailored to manage the information on future social and techno- logical trends. It focuses on gathering and managing the rules that govern the evolution of selected information society technologies (IST) and their applica- tions. The main idea of information gathering and processing here presented refers to so-called on- line expert Delphi, where an expert community works on the same research problems by responding to structured questionnaires, elaborating complex dynamical system models, providing recommendations, and verifying the models so arisen. The knowledge base is structured in layers that correspond to the selected kinds of information on the technology and social evolution, uses, markets, and management. An analytical engine uses labeled hypermultigraphs to process the mutual impacts of objects from each layer to elicit the technological evolution rules and calculate future trends and scenarios. The processing rules are represented within discrete-time and discrete-event control models. Multicriteria decision support procedures make possible to aggregate individual expert recommendations. The resulting foresight support system can process uncertain information using a fuzzy-random-variable-based model, while a coupled reputation management system can verify collective experts’ judgments and assign trust vectors to experts and other sources of information. Keywords: Foresight Support Systems, Complex Socioeconomic Models, Group Model Building, Knowledge Fusion, Intelligent Decision Support. 1 Introduction The evolution of modern societies cannot be sufficiently explained without a penetrative study of its technological research, economic, political, and social context. A universe of objects, events and dynamical phenomena, and relations between them, has to be taken into account to carry out such a study. These form a complex system [1], [4], [6], [10], [18], [19], usually referred to as the Information Society (IS). Therefore, research on IS modelling methodology can provide clues to building foresight scenarios, eliciting social and technological trends, and planning of future development of information technologies (IT) and their application areas.