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Complexity: learning to muddle through

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Abstract

The articles in this special issue are placed in the context of the literature of general systems theory. The focus is on the complexity (or requisite variety) of complex work domains and the implications for control. Following the insights of Ashby’s law of requisite variety, it is concluded that classical hierarchical or servomechanism-type control systems are inadequate as a basis for dealing with the unanticipated variability endemic to complex work domains. Alternative types of control (e.g., self-organizing systems) and alternative images of cognition are suggested as a theoretical context for modeling performance in complex work domains.

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Authors and Affiliations

  1. Department of Psychology, Wright State University, Dayton, OH, USA

    John M. Flach

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  1. John M. Flach
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Correspondence to John M. Flach.

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Flach, J.M. Complexity: learning to muddle through. Cogn Tech Work 14, 187–197 (2012). https://doi.org/10.1007/s10111-011-0201-8

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  • DOI: https://doi.org/10.1007/s10111-011-0201-8

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