Managing Complexity

  • Dirk Helbing
Part of the Understanding Complex Systems book series (UCS)


This contribution summarizes some typical features of complex systems such as non-linear interactions, chaotic dynamics, the “butterfly effect”, phase transitions, self-organized criticality, cascading effects, and power laws. These imply sometimes quite unexpected, counter-intuitive, or even paradoxical behaviors of socioeconomic systems. A typical example is the faster-is-slower effect. Due to their tendency of self-organization, complex systems are often hard to control. Instead of trying to control their behavior, it would often be better to pursue the approach of guided self-organization, i.e. to use the driving forces of the system rather than to fight against them. This is illustrated by the example of hierarchical systems, which need to fulfill certain principles in order to be efficient and robust in an ever-changing environment. We also discuss the important role of fluctuations and heterogeneity for the adaptability, flexibility and robustness of complex systems. The presentation is enriched by a number of examples ranging from decision behavior up to production systems and disaster spreading.


Catastrophe Theory Capacity Drop Lower Hierarchical Level Strict Hierarchy Butterfly Effect 
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.


  1. 1.
    H. Haken, Synergetics (Springer, Berlin, 1977)Google Scholar
  2. 2.
    G. Ausiello, P. Crescenzi, G. Gambosi, et al., Complexity and Approximation – Combinatorial optimization problems and their approximability properties (Springer, Berlin, 1999)Google Scholar
  3. 3.
    S.H. Strogatz, Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering (Perseus, New York, 2001)Google Scholar
  4. 4.
    Y. Kuramoto, Chemical Oscillations, Waves, and Turbulence (Springer, Berlin, 1984)Google Scholar
  5. 5.
    A. Pikovsky, M. Rosenblum, J. Kurths, Synchronization: A Universal Concept in Nonlinear Sciences (Cambridge University Press, Cambridge, 2003)Google Scholar
  6. 6.
    S.C. Manrubia, A.S. Mikhailov, D.H. Zanette, Emergence of Dynamical Order. Synchronization Phenomena in Complex systems (World Scientific, Singapore, 2004)Google Scholar
  7. 7.
    D. Helbing, Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067 (2001)Google Scholar
  8. 8.
    E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute Studies in the Sciences of Complexity Proceedings (1999)Google Scholar
  9. 9.
    A. Kesting, M. Schönhof, S. Lämmer, M. Treiber, D. Helbing, Decentralized approaches to adaptive traffic control. In Managing Complexity: Insights, Concepts, Applications ed. by D. Helbing (Springer, Berlin, 2008)Google Scholar
  10. 10.
    D. Helbing, S. Lämmer, Verfahren zur Koordination konkurrierender Prozesse oder zur Steuerung des Transports von mobilen Einheiten innerhalb eines Netzwerkes [Method to Coordinate Competing Processes or to Control the Transport of Mobile Units within a Network]. Pending patent DE 10 2005 023 742.8 (2005)Google Scholar
  11. 11.
    R. Axelrod, The Evolution of Cooperation (Basic Books, New York, 1985)Google Scholar
  12. 12.
    J. von Neumann, O. Morgenstern, A. Rubinstein, H.W. Kuhn, Theory of Games and Economic Behavior (Princeton University Press, Princeton, 2004)Google Scholar
  13. 13.
    T.C. Schelling, The Strategy of Conflict (Harvard University Press, Cambridge, 2006)Google Scholar
  14. 14.
    D. Helbing, M. Schönhof, H.-U. Stark, J.A. Holyst, How individuals learn to take turns: Emergence of alternating cooperation in a congestion game and the prisoner’s dilemma. Adv. Complex Syst. 8, 87 (2005)Google Scholar
  15. 15.
    N.S. Glance, B.A. Huberman, The dynamics of social dilemmas. Sci. Am. 270, 76 (1994)Google Scholar
  16. 16.
    G. Hardin, The Tragedy of the Commons. Science 162, 1243 (1968)Google Scholar
  17. 17.
    E.C. Zeeman, Catastrophe Theory (Addison-Wesley, London, 1977)Google Scholar
  18. 18.
    H.E. Stanley, Introduction to Phase Transitions and Critical Phenomena (Oxford University Press, Oxford, 1971)Google Scholar
  19. 19.
    M. Schroeder, Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise (Freeman, New York, 1992)Google Scholar
  20. 20.
    D. Helbing, H. Ammoser, C. Kühnert, Disasters as extreme events and the importance of network interactions for disaster response management, in The Unimaginable and Unpredictable: Extreme Events in Nature and Society, ed. by S. Albeverio, V. Jentsch, H. Kantz (Springer, Berlin, 2005), pp. 319–348Google Scholar
  21. 21.
    P. Bak, C. Tang, K. Wiesenfeld, Self-organized criticality: An explanation of 1 ∕ f noise. Phys. Rev. Lett. 59, 381 (1987)Google Scholar
  22. 22.
    P. Bak, How Nature Works: The Science of Self-Organized Criticality (Copernicus, New York, 1996)Google Scholar
  23. 23.
    A. Aleksiejuk, J.A. Hołyst, A simple model of bank bankruptcies. Physica A 299(1-2), 198 (2001)Google Scholar
  24. 24.
    A. Aleksiejuk, J.A. Hołyst, G. Kossinets, Self-organized criticality in a model of collective bank bankruptcies. Int. J. Mod. Phys. C 13, 333 (2002)Google Scholar
  25. 25.
    S.L. Tubbs, A Systems Approach to Small Group Interaction (McGraw-Hill, Boston, 2003)Google Scholar
  26. 26.
    H. Arrow, J.E. McGrath, J.L. Berdahl, Small Groups as Complex Systems: Formation, Coordination, Development, and Adaptation (Sage, CA, 2000)Google Scholar
  27. 27.
    K.-Y. Chen, L.R. Fine, B.A. Huberman, Predicting the Future. Inform. Syst. Front. 5, 47 (2003)Google Scholar
  28. 28.
    A.S. Mikhailov, Artificial life: an engineering perspective, in Evolution of Dynamical Structures in Complex Systems, ed. by R. Friedrich, A. Wunderlin (Springer, Berlin, 1992), pp. 301–312Google Scholar
  29. 29.
    F-L. Ulschak, Small Group Problem Solving: An Aid to Organizational Effectiveness (Addison-Wesley Reading Mass., MA, 1981)Google Scholar
  30. 30.
    J. Gautrais, G. Theraulaz, J.-L. Deneubourg, C. Anderson, Emergent polyethism as a consequence of increased colony size in insect societies. J. Theor. Biol. 215, 363 (2002)Google Scholar
  31. 31.
    D. Helbing, H. Ammoser, C. Kühnert, Information flows in hierarchical networks and the capability of organizations to successfully respond to failures, crises, and disasters. Physica A 363, 141 (2006)Google Scholar
  32. 32.
    L.A. Adamic, E. Adar, Friends and neighbors on the web. Social Networks 25(3), 211–230 (2003)Google Scholar
  33. 33.
    D. Stauffer, P.M.C. de Oliveira, Optimization of hierarchical structures of information flow. Int. J. Mod. Phys. C 17, 1367 (2006)Google Scholar
  34. 34.
    D.J. Watts, S.H. Strogatz, Collective dynamics of smallworld networks. Nature 393, 440 (1998)Google Scholar
  35. 35.
    D. Helbing, Quantitative Sociodynamics, in Stochastic Methods and Models of Social Interaction Processes (Kluwer Academic, Dordrecht, 1995)Google Scholar
  36. 36.
    M. Christen, G. Bongard, A. Pausits, N. Stoop, R. Stoop, Managing autonomy and control in economic systems. In Managing Complexity: Insights, Concepts, Applications ed. by D. Helbing (Springer, Berlin, 2008)Google Scholar
  37. 37.
    D. Fasold, Optimierung logistischer Prozessketten am Beispiel einer Nassätzanlage in der Halbleiterproduktion. MA thesis, TU Dresden (2001)Google Scholar
  38. 38.
    D. Helbing, T. Seidel, S. Lämmer, K. Peters, Self-organization principles in supply networks and production systems, in Econophysics and Sociophysics - Trends and Perspectives, ed. by B.K. Chakrabarti, A. Chakraborti, A. Chatterjee (Wiley, Weinheim, 2006), pp. 535–558Google Scholar
  39. 39.
    D. Helbing, T. Platkowski, Self-organization in space and induced by fluctuations. Int. J. Chaos Theor. Appl. 5, 47–62 (2000)Google Scholar
  40. 40.
    D. Helbing, Dynamic decision behavior and optimal guidance through information services: Models and experiments, in Human Behaviour and Traffic Networks, ed. by M. Schreckenberg, R. Selten (Springer, Berlin, 2004), pp. 47–95Google Scholar
  41. 41.
    D. Helbing, M. Treiber, N.J. Saam, Analytical investigation of innovation dynamics considering stochasticity in the evaluation of fitness. Physical Review E 71, 067101 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dirk Helbing
    • 1
  1. 1.CLU E1ETH ZurichZurichSwitzerland

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