System Complexity and Its Measures: How Complex Is Complex

  • Witold Kinsner
Part of the Studies in Computational Intelligence book series (SCI, volume 323)


The last few decades of physics, chemistry, biology, computer science, engineering, and social sciences have been marked by major developments of views on cognitive systems, dynamical systems, complex systems, complexity, self-organization, and emergent phenomena that originate from the interactions among the constituent components (agents) and with the environment, without any central authority. How can measures of complexity capture the intuitive sense of pattern, order, structure, regularity, evolution of features, memory, and correlation? This chapter describes several key ideas, including dynamical systems, complex systems, complexity, and quantification of complexity. As there is no single definition of a complex system, its complexity and complexity measures too have many definitions. As a major contribution, this chapter provides a new comprehensive taxonomy of such measures. This chapter also addresses some practical aspects of acquiring the observables properly.


Dynamical systems complex systems cognitive systems complexity complexity measures monoscale and multiscale measures 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Witold Kinsner
    • 1
    • 2
  1. 1.Cognitive Systems Laboratory, Department of Electrical and Computer EngineeringUniversity of ManitobaWinnipegCanada
  2. 2.The Institute of Industrial Mathematical Sciences, and Telecommunications Research LaboratoriesTRLabsWinnipegCanada

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