Complexity, Modeling, and Natural Resource Management
Paul Cilliers, University of Stellenbosch
Harry C. Biggs, South African National Parks
Sonja Blignaut, The Narrative Lab
Aiden G. Choles, The Narrative Lab
Jan-Hendrik S. Hofmeyr, University of Stellenbosch
Graham P. W. Jewitt, University of Kwazulu Natal
Dirk J. Roux, South African National Parks; Nelson Mandela Metropolitan University; Monash South Africa
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This paper contends that natural resource management (NRM) issues are, by their very nature, complex and that both scientists and managers in this broad field will benefit from a theoretical understanding of complex systems. It starts off by presenting the core features of a view of complexity that not only deals with the limits to our understanding, but also points toward a responsible and motivating position. Everything we do involves explicit or implicit modeling, and as we can never have comprehensive access to any complex system, we need to be aware both of what we leave out as we model and of the implications of the choice of our modeling framework. One vantage point is never sufficient, as complexity necessarily implies that multiple (independent) conceptualizations are needed to engage the system adequately.
We use two South African cases as examples of complex systems—restricting the case narratives mainly to the biophysical domain associated with NRM issues—that make the point that even the behavior of the biophysical subsystems themselves are already complex. From the insights into complex systems discussed in the first part of the paper and the lessons emerging from the way these cases have been dealt with in reality, we extract five interrelated generic principles for practicing science and management in complex NRM environments. These principles are then further elucidated using four further South African case studies—organized as two contrasting pairs—and now focusing on the more difficult organizational and social side, comparing the human organizational endeavors in managing such systems.
complex systems; diversity; management; mental models; resilience; social complexity; social–ecological systems
Ecology and Society. ISSN: 1708-3087