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Adaptive Analysis of Locally Complex Systems in a Globally Complex World

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Type: Journal Article
Author: Lynam, Timothy
Journal: Ecology and Society
Volume: 3
Page(s):
Date: 1999
URI: http://hdl.handle.net/10535/3348
Sector: Social Organization
Agriculture
Region: Africa
Subject(s): adaptive systems
households
resource management
modeling
natural resources
participatory development
Abstract: "Zambezi Valley agro-ecosystems are environmentally, economically, and institutionally variable. This variability means that it is not possible to measure everything necessary to develop a predictive understanding of them. In particular, because people and their environments are constantly changing, what was measured yesterday may change by tomorrow. "Here, I describe elements of the approach that I have developed to address this problem. Called DAAWN, for Detail as and When Needed, the approach advocates an iterative and multiscaled methodology in which we first capture as broad an understanding of the system as possible and then use awareness developed at this scale to identify where to focus subsequent, more detailed, investigations. Because we cannot hope to measure or monitor everything in these complex and adaptive agro-ecosystems, the approach requires us to make judicious use of all available knowledge about the agro-ecosystem. The DAAWN approach is rooted in systems theory, but is tempered by systems and problems where boundaries are not clearly defined, where nonlinearities are the norm, and where structural and functional change is the order of the day. "I describe a few of the most important data collection tools and methods that were developed to record the knowledge of local people and to observe, monitor, and measure changes in their resources. Of particular importance is the tool that I call a 'spidergram.' This tool, which I used extensively with village informants, symbolizes the DAAWN approach and was a major stimulus for its development. Simulation models provide another very important tool; here, I offer some examples of spatially explicit, multi-agent models. Some key findings of the research on Zambezi Valley agro-ecosystems are also briefly presented."

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