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About the Predictability and Complexity of Complex Systems

  • Renate Sitte
Chapter
Part of the Understanding Complex Systems book series (UCS)

Summary

With ever-growing complexity of systems to be modeled, there is a strong need for a proper theory of complexity, other than the algorithmic complexity known in computing. The problem is that there is no unanimous consensus as to what complexity is. Several attempts have been made, some are very promising, but a widely applicable theory and practice have not been derived. Quantification is an essential step in modeling to achieve prediction and control of a system. Quantification is also a crucial step in complexity and some complexity quantification models are emerging. In this chapter, a unifying and systematic approach to complexity is proposed. Its aim is to bring some clarity into the unknown, and a step further towards predictability. It serves as an overview and introduction, in particular to the novices on how to deal with complex system as a practical approach. Some practices summarized here are elementary and others are quite ambitious. It happens over and over that the uninitiated researchers make errors, reinvent the wheel or fall into traps. The purpose of this chapter is to offer good advice and a sense on how to avoid pitfalls.

Keywords

Complex System Chaotic System Model Predictive Control Complexity Reduction Discrete Event System 
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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References

  1. 1.
    Aknine, S., Shehori, O.: Coalition Formation: Concessions, Task Relationships and Complexity Reduction. CoRR, volume abs/cs/0502094 (2005), http://arxiv.org/abs/cs/0502094
  2. 2.
    Bar-Yam, Y.: When Systems Engineering Fails - Toward Complex Systems Engineering. In: IEEE International Conference on Systems, Man and Cybernetics, October 5-8, vol. 2, pp. 2021–2028 (2003)Google Scholar
  3. 3.
    Bartelt, A.F., Feurer, T., Wöeste, L.: Understanding optimal control results by reducing the complexity. Chemical Physics 318, 207–216 (2005)CrossRefGoogle Scholar
  4. 4.
    Basu, S.: On the Combinatorial and Topological Complexity of a Single Cell. In: Proc. 39th Annual Symposium on Foundations of Computer Science, November 8-11, pp. 606–616 (1998)Google Scholar
  5. 5.
    Braha, D., Maimon, O.: The Measurement of a Design Structural and Functional Complexity. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 28(4), 527–535 (1998)CrossRefGoogle Scholar
  6. 6.
    Braitenberg, V.: Vehicles Experiments in Synthetic Psychology. MIT Press, Cambridge (1986)Google Scholar
  7. 7.
    Domingo, C., Tonella, G.: Towards a theory of structural change. Structural Change and Economic Dynamics 11, 209–225 (2000)CrossRefGoogle Scholar
  8. 8.
    Finnigan, J.: The Science of complex systems. Australasian Science, 32–34 (June 2005)Google Scholar
  9. 9.
    Gibbon, J.D., Titi, E.S.: Cluster formation in complex multi-scale systems. Proc. Royal Soc. 461, 3089–3097 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Giles, J.: When doubt is a sure thing (Interview with S. Schneider and R. Moss, Stanford). Nature 418(6897), 476 (2002)CrossRefGoogle Scholar
  11. 11.
    Halstead, M.H.: Elements of Software Science, Operating, and Programming Systems Series, vol. 7. Elsevier, New York (1977)Google Scholar
  12. 12.
    Hazen, R.M., Griffin, P.L., Carothers, J.M., Szostak, J.W.: Functional information and the emergenceof biocomplexity. Proceedings of The National Academy of Sciences of The United States of America 104, 8574–8581 (2007)CrossRefGoogle Scholar
  13. 13.
    Klein, M., Faratin, P., Sayama, H., BarYam, Y.: What complex systems research can teach us about collaborative design. In: The Sixth International Conference on Computer Supported Cooperative Work in Design, July 12-14, pp. 5–12 (2001)Google Scholar
  14. 14.
    Longstaff, P.H.: Can Unpredictable Systems be Managed. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 2(5), pp. 2013–2020 (October 2003)Google Scholar
  15. 15.
    Mittone, L.: The reduction of decision complexity: normative policies and the role of information. Neurocomputing 69(16-18), 2456–2460 (2006)CrossRefGoogle Scholar
  16. 16.
    Rossiter, J.A., Kouvaritakis, B., Cannon, M.: An algorithm for reducing complexity in parametric predictive control. International Journal of Control 78(18), 1511–1520 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Sharony, J.: The Universality of Multi-dimensional Switching Networks. IEEE/ACM Trans-actions on Networking 2(6), 602–612 (1994)CrossRefGoogle Scholar
  18. 18.
    Spiliopoulos, K., Sofianopoulou, S.: Manufacturing cell design with alternative routings in generalized group technology: reducing the complexity of the solution space. International Journal of Production Research 45(6), 1355–1367 (2007)zbMATHCrossRefGoogle Scholar
  19. 19.
    Sitte, R.: Zooming-In Modeling Method For Managing Complexity. International Journal of Modeling and Simulation 21(4), 104–109 (2002)Google Scholar
  20. 20.
    Stoop, R., Stoop, N.: Natural computation measured as a reduction of complexity. Chaos 14(3), 675–679 (2004)CrossRefGoogle Scholar
  21. 21.
    Subbey, S., Howell, D., Bogstad, B., Åsnes, M.: Reducing fisheries model complexity using a finite Fourier series reparameterization. Fisheries Research 84, 390–394 (2007)CrossRefGoogle Scholar
  22. 22.
    Tan, S.C., Rao, M.V.C., Lim, C.P.: On the reduction of complexity in the architecture of fuzzy ARTMAP with dynamic decay adjustment. Neurocomputing 69, 2456–2460 (2006)CrossRefGoogle Scholar
  23. 23.
    van den Boom, T.J.J., Heidergott, B., De Schutter, B.: Complexity reduction in MPC for stochastic max-plus-linear discrete event systems by variability expansion. Automatica 43, 1058–1063 (2007)zbMATHCrossRefGoogle Scholar
  24. 24.
    Wallace, J.S.: HELP bridging scales in water science, management and policy. In: Proceedings of the International Congress on Modeling and Simulation, Townsville, July 14-17, pp. 428–433 (2003)Google Scholar
  25. 25.
    Jianmei, Y.: An Application of Simon’s Theory on the Architecture of Complex Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(1), 264–267 (1993)CrossRefGoogle Scholar
  26. 26.
    Zhang, S.B., Huang, J.C., Zhang, W.M., Liu, Z.: Research on Parallel Decision Analyzing for Complex System of Systems. In: IEEE Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, August 13–16, pp. 1812–1817 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Renate Sitte
    • 1
  1. 1.SEET/ICTGriffith UniversityAustralia

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