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Requisite Simplicities to Help Negotiate Complex Problems

AMBIO volume 39, pages 600–607 (2010)Cite this article

Abstract

Decision makers responsible for natural resource management often complain that science delivers fragmented information that is not useful at the scale of implementation. We offer a way of negotiating complex problems by putting forward a requisite simplicity. A requisite simplicity attempts to discard some detail, while retaining conceptual clarity and scientific rigor, and helps us move to a new position where we can benefit from new knowledge. We illustrate the above using three case studies: elephant densities and vegetation change in a national park, the use of rules of thumb to support decision making in agriculture, and the management of salt in irrigation. We identify potential requisite simplicities that can allow us to generate new understanding, lead to action and provide opportunities for structured learning.

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Notes

  1. http://www.angasbremerwater.org.au/. Accessed 31 March 2010.

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Acknowledgments

We thank Oonsie Biggs, Nicky Grigg, John Passioura, Fabio Boschetti, Ted Lefroy, and Stefanie Freitag-Ronaldson for their helpful comments on the manuscript.

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Authors and Affiliations

  1. CSIRO Land and Water, PO Box 1666, ACT, 2601, Canberra, Australia

    Richard Stirzaker

  2. Scientific Services, Kruger National Park, Private Bag X402, Skukuza, 1350, South Africa

    Harry Biggs

  3. IWC, Africa Water Research Node, Monash South Africa, Private Bag X60, Roodepoort, 1725, South Africa

    Dirk Roux

  4. Centre for Studies in Complexity, University of Stellenbosch, Private Bag X1, Matieland, 7602, South Africa

    Paul Cilliers

Authors
  1. Richard Stirzaker
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  2. Harry Biggs
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  3. Dirk Roux
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  4. Paul Cilliers
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Correspondence to Richard Stirzaker.

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Stirzaker, R., Biggs, H., Roux, D. et al. Requisite Simplicities to Help Negotiate Complex Problems. AMBIO 39, 600–607 (2010). https://doi.org/10.1007/s13280-010-0075-7

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  • DOI: https://doi.org/10.1007/s13280-010-0075-7

Keywords

  • Reductionism
  • Complexity
  • Adaptive learning
  • Decision support models
  • Natural resource management
  • Irrigation