Adapting science to adaptive managers - spidergrams, belief models and multi-agent systems modelling.
Abstract
Two case studies are presented in which models were used as focal tools in problems associated with common-pool resource management in developing countries. In the first case study, based in Zimbabwe, Bayesian or Belief Networks were used in a project designed to enhance the adaptive management capacity of a community in a semiarid rangeland system. In the second case study, based in Senegal, multi-agent systems models were used in the context of role plays to communicate research findings to a community, as well as to explore policies for improved management of rangelands and arable lands over which herders and farmers were in conflict.
The paper provides examples of the use of computer-based modeling with stakeholders who had limited experience with computer systems and numerical analyses. The paper closes with a brief discussion of the major lessons learned from the two independent case studies. Perhaps the most important lesson was the development of a common understanding of a problem through the development of the models with key stakeholders. A second key lesson was the need for research to be adaptive if it were to benefit adaptive managers. Both case study situations required significant changes in project orientation as stakeholder needs were defined. Both case studies recognized the key role that research, and particularly the development of models, played in bring different actors together to formulate improved management strategies or policies. Participatory engagement with stakeholders is a time-consuming and relatively costly process in which, in the case studies, most of the costs were born by the research projects themselves. We raise the concern that these activities may not be widely replicable if such costs are not reduced or born by the stakeholders themselves.
Citation
Lynam T.J.P., Bousquet F., Le Page C., d’Aquino P., Barreteau O., Chinembiri F. and Mombeshora B. Adapting science to adaptive managers - spidergrams, belief models and multi-agent systems modelling. Conservation Ecology (2002) 5 (2) 24.