Combining Computational Models and Interactive Visualization to Support Rational Decision Making

  • Tobias Ruppert
  • Jürgen Bernard
  • Thorsten May
  • Jörn Kohlhammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8887)


Decision making is a complex process consisting of several consecutive steps. Before converting a decision into effective action the problem to be tackled needs to be analyzed, alternative solutions need to be developed, and the best solution needs to be picked. In many cases computational models support decision makers in this process. Therefore, providing an intuitive access to these model-driven techniques is crucial. In this approach, we introduce a decision support system that provides visual-interactive access to three computational models - a simulation model, an optimization model, and an opinion mining model - covering different aspects of decision making. For each model our decision support system realizes the visual access to the model, an in-depth analysis of the generated solutions, and the comparison of alternative solutions. Finally, we evaluate the usefulness and the usability of our system in a use case in the field of public policy making.


Decision Maker Decision Support System Opinion Mining Information Visualization Energy Plan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Simon, H.A.: The New Science of Management Decision. Harper & Brothers (1960)Google Scholar
  2. 2.
    Pomerol, J.C., Adam, F.: Practical decision making: From the legacy of Herbert Simon to decision support systems. In: IFIP TC8/WG8.3 Int. Conf. (2004)Google Scholar
  3. 3.
    Brim, O.: Personality and Decision Processes: Studies in the Social Psychology of Thinking. In: Stanford Studies in Sociology. Stanford University Press (1962)Google Scholar
  4. 4.
    Power, D.J., Sharda, R.: Model-driven decision support systems: Concepts and research directions. Decision Support Systems 43 (2007)Google Scholar
  5. 5.
    Hill, L.L., Crosier, S.J., Smith, T.R., Goodchild, M.: A content standard for computational models. D-Lib Magazine 7 (2001)Google Scholar
  6. 6.
    Thomas, J.J., Cook, K.A.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. National Visualization and Analytics Center (2005)Google Scholar
  7. 7.
    Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the Information Age - Solving Problems with Visual Analytics. In: EG (2010)Google Scholar
  8. 8.
    Andrienko, G., Andrienko, N., Jankowski, P., Keim, D., Kraak, M.J., MacEachren, A., Wrobel, S.: Geovisual analytics for spatial decision support: Setting the research agenda. Int. Journal of Geographical Information Science 21 (2007)Google Scholar
  9. 9.
    Jones, C.: Visualization and Optimization. Operations Research/Computer Science Interfaces Series, Kluwer Acad. Publ. (1996)Google Scholar
  10. 10.
    Booshehrian, M., Möller, T., Peterman, R.M., Munzner, T.: Vismon: Facilitating analysis of trade-offs, uncertainty, and sensitivity in fisheries management decision making. Computer Graphics Forum 31 (2012)Google Scholar
  11. 11.
    Afzal, S., Maciejewski, R., Ebert, D.S.: Visual analytics decision support environment for epidemic modeling and response evaluation. In: Symposium on Visual Analytics Science and Technology. IEEE (2011)Google Scholar
  12. 12.
    Kornhauser, D., Wilensky, U., Rand, W.: Design guidelines for agent based model visualization. J. of Artificial Societies and Social Simulation 12 (2009)Google Scholar
  13. 13.
    Crouser, R.J., Kee, D.: Two visualization tools for analyzing agent-based simulations in political science. IEEE Comp. Graph. and App. (2012)Google Scholar
  14. 14.
    Unger, A., Schumann, H.: Visual support for the understanding of simulation processes. In: PacificVis. IEEE (2009)Google Scholar
  15. 15.
    Oelke, D., Hao, M., Rohrdantz, C., Keim, D.A., Dayal, U., Haug, L., Janetzko, H.: Visual opinion analysis of customer feedback data. In: Symposium on Visual Analytics Science and Technology. IEEE (2009)Google Scholar
  16. 16.
    Chen, C., Ibekwe-SanJuan, F., SanJuan, E., Weaver, C.: Visual analysis of conflicting opinions. In: Symposium on Visual Analytics Science and Technology. IEEE (2006)Google Scholar
  17. 17.
    Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D., Roberts, J.C.: Visual comparison for information visualization. Information Visualization 10 (2011)Google Scholar
  18. 18.
    Tominski, C., Forsell, C., Johansson, J.: Interaction support for visual comparison inspired by natural behavior. IEEE Transactions on Visualization and Computer Graphics 18 (2012)Google Scholar
  19. 19.
    Few, S.: Now You See it: Simple Visualization Techniques for Quantitative Analysis. Analytics Press (2009)Google Scholar
  20. 20.
    Ruppert, T., Bernard, J., Ulmer, A., Kuijper, A., Kohlhammer, J.: Visual access to optimization problems in strategic environmental assessment. In: Bebis, G., et al. (eds.) ISVC 2013, Part II. LNCS, vol. 8034, pp. 361–372. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Ruppert, T., Bernard, J., Ulmer, A., Lücke-Tieke, H., Kohlhammer, J.: Visual access to an agent-based simulation model to support political decision making. In: i-KNOW. ACM (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tobias Ruppert
    • 1
  • Jürgen Bernard
    • 1
  • Thorsten May
    • 1
  • Jörn Kohlhammer
    • 1
  1. 1.Department of Information Visualization and Visual AnalyticsFraunhofer Institute for Computer Graphics ResearchDarmstadtGermany

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