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Complexity reduction: A pragmatic approach

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  • Joseph Simpson
  • Mary Simpson

Abstract

An increasing need for shared communication in disparate domains as well as the production of increasingly dynamic, large‐scale systems has always been at the heart of the practice of systems engineering. As a branch of general systems theory, systems engineering was developed to address practical considerations posed by diverse organizations, environments, and cultures within which systems are designed, developed, and operated. Types and categories of complexity are used in this paper to focus the discussion on complexity and the reduction of complexity. Formal and theoretical foundations of systems science and systems engineering provided the basis upon which many effective systems engineering tools were built. This paper identifies some of the classical tools of systems science and systems engineering that manage complexity. Based on these classical tool components and principles, abstract relation types (ART) were developed to enhance the understanding and application of these tools. A pragmatic approach that is designed to reduce complexity as well as compare relative complexity reduction between and among methods is also presented. The direct value of systems engineering techniques as they are applied in any context is rooted in the ability of systems engineering techniques and systems engineering practitioners to reduce the cognitive complexity associated with the systems problem of interest. © 2010 Wiley Periodicals, Inc. Syst Eng 14: 180–192, 2011

Suggested Citation

  • Joseph Simpson & Mary Simpson, 2011. "Complexity reduction: A pragmatic approach," Systems Engineering, John Wiley & Sons, vol. 14(2), pages 180-192, June.
    • Handle: RePEc:wly:syseng:v:14:y:2011:i:2:p:180-192
    DOI: 10.1002/sys.20170
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    References listed on IDEAS

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    1. David M. Sharman & Ali A. Yassine, 2004. "Characterizing complex product architectures," Systems Engineering, John Wiley & Sons, vol. 7(1), pages 35-60.
    2. Kenneth E. Boulding, 1956. "General Systems Theory--The Skeleton of Science," Management Science, INFORMS, vol. 2(3), pages 197-208, April.
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    Cited by:

    1. Joseph J. Simpson & Mary J. Simpson, 2014. "Entropy Metrics for System Identification and Analysis," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 140-156, June.

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