Advanced Concepts and Generative Simulation Formalisms for Creative Discovery Systems Engineering

  • Levent Yilmaz
  • C. Anthony Hunt
Part of the Intelligent Systems Reference Library book series (ISRL, volume 10)


While M&S has been widely used in engineering and computational sciences to facilitate empirical insight, optimization, prediction, and experimentation, the role of simulation in supporting early foresight phases of creative problem solving received less attention. We advocate models of creative cognition to rethink simulation modeling so that creativity is enhanced rather than stifled. Generative Parallax Simulation (GPS) is introduced as a strategy and a generic and abstract specification for its realization is presented. GPS is based on an evolving ecology of ensembles of models that aim to cope with ambiguity, which pervades in early phases of model-based science and engineering. Besides its contributions as a modeling and simulation methodology in support of creativity, GPS provides a fertile and useful domain as an application testbed for parallel simulation.


Migration Rate Creative Cognition Model Ensemble Selection Ratio Phenotypic Attribute 
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.
    Amabile, M.T.: Creativity in Context: Update to the Social Psychology of Creativity, Westview, Boulder, CO (1996)Google Scholar
  2. 2.
    Campbell, D.T.: Blind variation and selective retention in creative thought as in other knowledge processes. Psychological Review 67, 380–400 (1960)CrossRefGoogle Scholar
  3. 3.
    Csikszenthmihalyi, M.: Implications of a systems perspective for the study of creativity. In: Handbook of Creativity, pp. 313–338 (1999)Google Scholar
  4. 4.
    Davis, P.K., Bigelow, J.H.: Exploratory analysis enabled by multiresolution, multiperspective modeling. In: Proceedings of the 2000 Winter Simulation Conference, pp. 127–134 (2000)Google Scholar
  5. 5.
    Engelberg, J.A., Ropella, G.E., Hunt, C.A.: Essential operating principles for tumor spheroid growth. BMC Syst. Biol. (2008),
  6. 6.
    Fishwick, P., Zeigler, B.P.: A multimodel methodology for qualitative model engineering. ACM Transactions on Modeling and Simulation 2(1), 52–81 (1992)CrossRefMATHGoogle Scholar
  7. 7.
    Gero, J.S.: Computers and creative design. In: The Global Design Studio, National University of Singapore, pp. 11–19 (1996)Google Scholar
  8. 8.
    Gero, J.S., Kazakov, V.: An exploration-based evolutionary model of generative design process. Microcomputers in Civil Engineering 11, 209–216 (1996)Google Scholar
  9. 9.
    Goldberg, D.: The race, the hurdle, and the sweet spot: Lessons from genetic algorithms for the automation of design innovation and creativity. In: Evolutionary Design by Computers. Morgan Kaufmann, San Francisco (1999)Google Scholar
  10. 10.
    Grant, M.R., Mostov, K.E., Tisty, T.D., Hunt, C.A.: Simulating properties of in vitro epithelial cell morphogenesis. PLoS Comput. Biol. 2(e129) (2007)Google Scholar
  11. 11.
    Holland, J.H.: Emergence: Chaos to Order. Oxford University Press, Oxford (1998)MATHGoogle Scholar
  12. 12.
    Kim, H.S.J., Park, S., Yu, W., Mostov, K.E., Matthay, M.A., Hunt, C.A.: Systems modeling of alveolar morphogenesis in vitro. In: Proc. ISCA 20th International Conference on Comp. Appl. Ind. Engr., pp. 141–144 (2008)Google Scholar
  13. 13.
    Kim, S.H.J., Park, S., Yu, W., Mostov, K.E., Matthay, M.A., Hunt, C.A.: A computational approach to unravel cellular principles of alveolar morphogenesis. Technical Report: UCSF/UC Berkeley Joint Graduate Group in Bioengineering, University of California, San Francisco (09-PONE-RA-08124) (2009)Google Scholar
  14. 14.
    Lam, T.N., Hunt, C.A.: Discovering plausible mechanistic details of hepatic drug interactions. Drug Metab. Dispos. (published October 20, 2008), doi:10.1124/dmd.108.023820Google Scholar
  15. 15.
    Mitchell, B., Yilmaz, L.: Symbiotic adaptive multisimulation: An autonomic simulation framework for real-time decision support under uncertainty. ACM Transactions on Modeling and Computer Simulation 19(1), 1–31 (2008)CrossRefGoogle Scholar
  16. 16.
    Poincare, H.: Mathematical creation. In: The Creative Process: A Symposium, Mentor (1908)Google Scholar
  17. 17.
    Rosenman, M.: The generation of form using and evolutionary approach. In: Evolutionary Algorithms in Engineering Applications. Springer, Heidelberg (1997)Google Scholar
  18. 18.
    Sawyer, K.: Group Genius: The Creative Power of Collaboration. Basic Books, New York (2008)Google Scholar
  19. 19.
    Schneiderman, B.: Creativity support tools: accelerating discovery and innovation. Communications of the ACM 50(12), 20–32 (2007)CrossRefGoogle Scholar
  20. 20.
    Simonton, D.K.: Origins of genius: Darwinian perspectives on creativity. Oxford University Press, Oxford (1999)Google Scholar
  21. 21.
    Smith, S.M., Blakenship, S.E.: Incubation and the persistence of fixation in problem solving. American Journal of Psychology 104, 61–87 (1991)CrossRefGoogle Scholar
  22. 22.
    Ward, T.M., Smith, S.M., Vaid, J.: Conceptual structures and processes in creative thought. In: Creative Thought: An Investigation of Conceptual Structures and Processes, pp. 1–27. American Psychological Association (1997)Google Scholar
  23. 23.
    Zeigler, B.P., Oren, T.: Multifaceted. multiparadigm modeling perspectives: Tools for the 90s. In: Proceedings of the 1986 Winter Simulation Conference, pp. 708–712 (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Levent Yilmaz
    • 1
  • C. Anthony Hunt
    • 2
  1. 1.Auburn UniversityUSA
  2. 2.University of CaliforniaSan Francisco

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