A Foresight Support System to Manage Knowledge on Information Society Evolution

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7710)


In this paper we present an intelligent knowledge fusion and decision support system tailored to manage information on future social and technological trends. It focuses on gathering and managing the rules that govern the evolution of selected information society technologies (IST) and their applications. The main idea of information gathering and processing here presented refers to so-called real-time 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 labelled 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 it 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 expert judgments and assign trust vectors to experts and other sources of information.


Foresight Support Systems Complex Socioeconomic Models Group Model Building Knowledge Fusion Intelligent Decision Support 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Antoniou, M.R., Stenning, V.: The Information Society as a Complex System. Journal of Universal Computer Science 6(3), 272–288 (2000)Google Scholar
  2. 2.
    Bañuls, V.A., Salmeron, J.L.: Scope and Design Issues in Foresight Support Systems. International Journal of Foresight and Innovation Policy 7(4), 338–351 (2011)CrossRefGoogle Scholar
  3. 3.
    Górecki, H., Skulimowski, A.M.J.: A Joint Consideration of Multiple Reference Points in Multicriteria Decision Making. Found. Control Engrg. 11(2), 81–94 (1986)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Lane, D., Pumain, D., van der Leeuw, S.E., West, G. (eds.): Complexity Perspectives in Innovation and Social Change. Springer Science+Business Media B.V (2009)Google Scholar
  5. 5.
    Nakamori, Y. (ed.): Knowledge Science. Modelling the Knowledge Creation Process. CRC Press, Boca Raton (2012)Google Scholar
  6. 6.
    Olivera, N.L., Proto, A.N., Ausloos, M.: Information Society: Modeling A Complex System With Scarce Data. Proc. of the V Meeting on Dynamics of Social and Economic Systems 6, 443–460 (2011) (arXiv:1201.1547)Google Scholar
  7. 7.
    Ramadge, P.J., Wohnam, W.M.: Supervisory control of a class of discrete event processes. SIAM J. Control 25(1), 206–230 (1987)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Salo, A., Mild, P., Pentikäinen, T.: Exploring causal relationships in an innovation program with Robust Portfolio Modeling. Tech. Forecasting Soc. Change 73, 1028–1044 (2006)CrossRefGoogle Scholar
  9. 9.
    Scenarios and Development Trends of Selected Information Society Technologies until 2025, Progress & Business Foundation, Kraków (2012),
  10. 10.
    Skulimowski, A.M.J.: Optimal Control of a Class of Asynchronous Discrete-Event Systems. In: Proceedings of the 11th IFAC World Congress, Automatic Control in the Service of Mankind, Tallinn, Estonia, vol. 3, pp. 489–495. Pergamon Press, London (1991)Google Scholar
  11. 11.
    Skulimowski, A.M.J.: Framing New Member States and Candidate Countries Information Society Insights. In: Compaño, R., Pascu, C. (eds.) Prospects For a Knowledge-Based Society in the New Members States and Candidate Countries, Publishing House of the Romanian Academy, pp. 9–51 (2006)Google Scholar
  12. 12.
    Skulimowski, A.M.J.: Application of dynamic rankings to portfolio selection. In: Soares, J.O., Pina, J.P., Catalão-Lopes, M. (eds.) New Developments in Financial Modelling, pp. 196–212. CSP Cambridge Scholars Publishing, Newcastle (2008)Google Scholar
  13. 13.
    Skulimowski, A.M.J.: Future Trends of Intelligent Decision Support Systems and Models. In: Park, J.J., Yang, L.T., Lee, C. (eds.) FutureTech 2011, Part I. CCIS, vol. 184, pp. 11–20. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Skulimowski, A.M.J.: Fusion of Expert Information on Future Technological Trends and Scenarios. In: Kunifuji, S., Tang, X.J., Theeramunkong, T. (eds.): Proc. of the 6th International Conference on Knowledge, Information and Creativity Support Systems, Beijing, China, October 22-24 (KICSS 2011), pp. 10–20. JAIST Press, Beijing (2011)Google Scholar
  15. 15.
    Skulimowski, A.M.J.: Discovering Complex System Dynamics with Intelligent Data Retrieval Tools. In: Zhang, Y., Zhou, Z.-H., Zhang, C., Li, Y. (eds.) IScIDE 2011. LNCS, vol. 7202, pp. 614–626. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Skulimowski, A.M.J.: Anticipatory Network Models of Multicriteria Decision-Making Processes. Int. J. Systems Sci. 44 (2012), doi: 10.1080/00207721.2012.670308Google Scholar
  17. 17.
    Skulimowski, A.M.J., Schmid, B.F.: Redundancy-free description of partitioned complex systems. Mathematical and Computer Modelling 16(10), 71–92 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Sudár, E., Peto, D., Gábor, A.: Modeling the Penetration of the Information Society Paradigm. In: Wimmer, M.A. (ed.) KMGov 2004. LNCS (LNAI), vol. 3035, pp. 201–209. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Tadeusiewicz, R.: A need of scientific reflection on the information society development. In: Bliźniuk, G., Nowak, J.S. (eds.) Information Society 2005, pp. 11–38. PTI (2005)Google Scholar
  20. 20.
    Walden, P., Carlsson, C., Liu, S.: Industry foresight with intelligent agents. Human Systems Management 19(3), 169–180 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Automatic Control and Biomedical Engineering, Decision Science LaboratoryAGH University of Science and TechnologyKrakówPoland
  2. 2.International Centre for Decision Sciences and Forecasting, Progress & Business FoundationKrakówPoland

Personalised recommendations