Abstract

Complexity is perhaps as important a concept for systems science as the concept of a system. It is a difficult concept, primarily because it has many possible meanings. While various specific meanings of complexity have been proposed and discussed on many occasions, there is virtually no sufficiently comprehensive study that attempts to capture its general characteristics. The reason for this situation is well expressed by John Casti [1986]:

The notion of system complexity is much like St. Augustine’s description of time: “What then is time [complexity]? If no one asks me, I know; if I wish to explain it to one that asks, I know not.” There seems to be fairly well-developed intuitive ideas about what constitutes a complex system, but attempts to axiomatize and formalize this sense of the complex all leave a vague, uneasy feeling of basic incompleteness, and a sense of failure to grasp important aspects of the essential nature of the problem.

Keywords

Polynomial Time Problem Instance Polynomial Time Algorithm Shannon Entropy Conditional Entropy 
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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Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • George J. Klir
    • 1
  1. 1.State University of New York at BinghamtonBinghamtonUSA

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