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.
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.
Adapting science to adaptive managers - spidergrams, belief models and multi-agent systems modelling.