Conservation Ecology: Adapting science to adaptive managers: spidergrams, belief models, and multi-agent systems modeling
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ES Home > Vol. 5, No. 2 > Art. 24
Copyright © 2002 by the author(s). Published here under license by The Resilience Alliance.
The following is the established format for referencing this article:
Lynam, T., F. Bousquet, C. Le Page, P. d'Aquino, O. Barreteau, F. Chinembiri, and B. Mombeshora. 2002. Adapting
science to adaptive managers: spidergrams, belief models, and multi-agent systems modeling. Conservation
Ecology 5(2): 24. [online] URL: http://www.consecol.org/vol5/iss2/art24/
A version of this article in which text, figures, tables, and appendices are separate files may be found by
following this
link.
Report, part of Special Feature on Integrated Natural Resource Management
Adapting Science to Adaptive Managers:
Spidergrams, Belief Models, and Multi-agent
Systems Modeling
Tim Lynam
1
, Francois Bousquet
2
, Christophe Le Page
2
, P. d'Aquino
2
, Olivier Barreteau
3
, Frank Chinembiri
4
, and
Bright Mombeshora
5
1
Tropical Resource Ecology Program, University of Zimbabwe;
2
CIRAD Tera;
3
Cemagref Division
Irrigation;
4
Agritex;
5
Department of Research and Specialist Services
● Abstract
● Introduction
● The Adaptive Management Context
● Case Studies
❍ Zimbabwe: Participatory development of vegetation resource management strategies
❍ Senegal: Role games and multi-agent systems
● Lessons Learned
● Responses to this Article
● Acknowledgments
● Literature Cited
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
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Conservation Ecology: Adapting science to adaptive managers: spidergrams, belief models, and multi-agent systems modeling
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.
KEY WORDS:
adaptive management, Bayesian belief networks, developing country, dynamic modeling,
multi-agent systems, participatory modeling, semiarid rangeland, Senegal, spidergrams, Zimbabwe.
Published: January 2, 2002
INTRODUCTION
At the interface between natural and social dynamics, environmental research is tackling development problems
by examining questions that relate to resources and externalities. These include the management of renewable
resources, externalities of production (pollution, effluent, etc.) and areas with multiple uses. Natural dynamics are
composed of numerous interwoven processes involving different resources at different spatial and temporal
scales. Social processes involve different stakeholders at various levels of organization, ranging from individuals or
communities that use resources and spaces to large development institutions. The issues focus on the regulation
of resource use, which is adapted to natural dynamics, through the application of economic, legal, or institutional
management tools. In each of the cases presented here, the issues were related to problems of collective
management where ecological processes have to be reconciled with social processes for resource use.
Public administrators, NGOs, researchers, agriculturalists, and migrants have different representations of the
system. The management of natural resources is a collective learning problem. Models may be used to focus
discussions on cause-and-effect connections between behavioral and interaction rules and the resource dynamics.
The question is how to use these models.
Recent research in the smallholder sector of Zimbabwe and elsewhere has demonstrated the great complexity of
these production systems based on natural resources (Fresco 1986, Scoones 1996, Cumming and Lynam 1997).
Multiple stakeholders seek to satisfy multiple and often competing objectives using resources that are both
spatially and temporally variable. To further add to the complexity, the resource users in these systems often
function within diverse institutional circumstances, mixtures of quasi-private through common-pool resource
management regimes that are established and maintained by mixtures of traditional, locally elected, and central
government authorities (Cumming and Lynam 1997). At the same time, it has become increasingly clear that for
development-oriented initiatives to achieve their objectives, the key stakeholders in the system must be involved
in all stages of the process, from problem identification through the implementation of solutions (Chambers
1983).
Faced with such daunting complexity, many have advocated an adaptive approach to managing ecological
systems (Holling 1978, Walters 1986, Rogers and Bestbier 1997). Much of the stimulus for advocating an adaptive
approach is the recognition that it may not be possible to collect and analyze sufficient data to adequately
understand the system of interest (Walters 1986, Johannes 1998).
Dynamic modeling is a key component of the adaptive management process and serves three core functions, as
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Conservation Ecology: Adapting science to adaptive managers: spidergrams, belief models, and multi-agent systems modeling
identified by Walters (1997). First, it seeks to clarify problems and improve communication among stakeholders;
second, it facilitates the screening of management or policy options to eliminate unworkable solutions; and third,
it identifies critical knowledge gaps. However, in the context of smallholder managers in developing countries,
where most managers have no history of interaction with computer systems and have limited or no mathematical
abilities, modeling is a complex challenge on its own.
Two modeling approaches are presented:
1. Bayesian or Belief Networks (BNs; Jensen 1996) provide a probabilistic and relatively, although not entirely,
static representation of the relationships between input variable states and the states of the variables of interest,
and have proven useful in natural resource management situations (Varis 1997, Cain et al. 1999). This approach
was used in the Zimbabwe case study.
2. Multi-agent systems (MAS, also called agent-based simulation) provide a useful modeling framework in
systems consisting of a large number of agents who interact with each other in various ways (Holland 1995). In
these models, the agents change their actions as a result of events in the process of interaction. The behavior of
the whole system depends on these interactions between agents, which can be represented in a model. MAS are
used to set up spatial models, which integrate social and ecological dimensions (Bousquet 1994, Barreteau and
Bousquet 2000, Janssen et al. 2000, Kohler and Gumerman 2000). The aim of the modeling experiment was not
to represent the whole system, but to build and test theories. Complex dynamics may emerge from simple rules.
In this paper, we present the results of independent case studies, carried out in Zimbabwe and Senegal, that have
used different modeling activities to facilitate communication between scientists and participating communities,
and also to explore options for improved resource use. It is important to emphasize that, in the contexts in which
these case studies are presented, the models were used more as part of a process of developing and exploring a
common understanding of problems and possible solutions. They were not designed to be highly validated,
predictive models in the sense in which systems models are usually developed and used. We are not aware of
other examples in which local people, who have no history of computer-based modeling, have been involved, not
only in the use of computer models, but also in their development. The paper begins by presenting a simple
conceptual model of the adaptive management process that will guide the later presentations. Thereafter, results
of field experiences in Zimbabwe and Senegal are presented in relation to this model. In the final section of the
paper, the lessons learned from these experiences are presented in relation to the opportunities and constraints
that might hinder or improve the effectiveness of adaptive change agents in the future.
THE ADAPTIVE MANAGEMENT CONTEXT
In this section, we provide a brief outline of the process of adaptive management as a context for the modeling
processes described in the case studies. Adaptive management is generally accepted as a continuously iterative,
learn-by-doing process, in which objectives, activities, monitoring protocols, and evaluative procedures are
established and then refined as new information is gleaned from the experimental manipulation of structures or
processes. FIn order to simplify the discussion in this paper, we condense this set of processes into five sets of
activities: Problem formulation (including needs analyses and setting system objectives); System understanding
(including modeling the system to locate key leverage points or to identify optimal activities or designs as well as
the selection of actions to be taken); Action (those activities undertaken to achieve the objectives); Monitoring
and evaluation (including all observations and evaluation of system performance in achieving objectives); and
Updating. The last set of activities explicitly recognizes that adaptive management calls for continuous and careful
updating of each set of activities. In the context of the work presented here, the adaptive management process is
seen as distinctly nonlinear; refinements and improvements in any of the stages can, and indeed should, happen
at almost any stage of the process.
It is important to recognize that learning by doing is a long, time-consuming process. In some cases, it may have
negative consequences, which implies a risk to the participating stakeholder. Often there is no possibility of
repeating particular trials or experiments. Therefore, modeling and simulation can play important roles in each set
of activities in the adaptive management process. Models provide an important tool when it comes to clarifying
the nature of a particular problem. Both case studies in this paper reflect the use of models in this mode. Perhaps
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the more common view of models in systems activities and in formal system analysis is in their role of
representing current understanding of the system, and thence being used in a predictive mode to identify key
intervention points or activities as well as key gaps in understanding. Instead of prediction, models can also be
used for communication and mediation in a collective decision-making process (Bousquet et al. 1999). Although
models may not play a direct role in the actions themselves, they do form the stimulus–response framework,
which guides the nature of the actions and their implementation. Models play an important role in devising
monitoring protocols as well as in providing a useful set of evaluative tools to suggest when critical thresholds or
conditions are likely to be reached or exceeded.
The more formal modeling tools presented in this paper are by no means the only useful representations. They
represent a small sample of potential models and model applications. Perhaps their importance, however, lies in
their use in a developing-country context and where they are having a significant impact on the direction taken in
the described development projects.
CASE STUDIES
Zimbabwe: Participatory development of vegetation resource management strategies
In the Zimbabwe case study, a collaborative research project was initiated in early 2000 with the community of
Mahuwe Ward, Guruve District, a semiarid area, of about 400 km
2
, in the eastern Zambezi valley of Zimbabwe.
The project's objectives were the design of management strategies for the common-pool vegetation resources
that would improve productivity in terms of the supply of livestock feeds as well as other goods and services that
households use (e.g., timber, wild fruits, thatching grass). A major objective of the donor funding the project was
the development of a replicable approach to improving management of common-pool vegetation resources.
Recognition of the failure of so many similar development initiatives prompted the Zimbabwean research team to
ask themselves what would most meaningfully contribute to the sustainability and replicability of their initiatives.
The answer was obvious: enhancement of the capacity of local managers to manage adaptively. As a
consequence, the project shelved many of its pre-determined objective and activity sets, and focused instead on
how to enhance local adaptive capacity.
A community-based coordination committee was formed, drawing on local leaders. Each village in the community
was asked to select two local informants, called “Village Representatives,” as well as a communications team
member. Different experts were called in to assist with any one particular stage of the research process.
To begin with, several participatory rural appraisals were conducted to obtain a broad and general understanding
of the structure and use of vegetation resources and to identify key problems from as many perspectives as
possible. Thereafter, a focused workshop was held with the coordinating committee members, the Village
Representatives, and the communications team members to identify the broad community objectives to be used
as a guide for woodland resource management. Eight objectives for Mahuwe Ward were defined and agreed upon:
1. To conserve our natural, grazing and browse resources.
2. To protect and respect the traditionally sacred places, our spirit mediums and traditional leaders.
3. All residents to be aware of their rights pertaining to the use of common pool resources.
4. Residents to appreciate the importance of wise use of natural resources for the benefit of future generations.
5. To generate income from the natural, graze and browse resources.
6. For future generations to learn from these resources (so they know how to use and benefit from the
resources).
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7. To carry out research on how best to manage and use natural, graze and browse resources in partnership
with other interested parties.
8. To carry out reclamation work so as to protect and improve the status of our natural resources.
In formal meetings held in each village, these objectives were first presented to village leaders and then to the
entire village to seek their broad approval. The objectives were accepted unanimously, and thus provided a set of
community-approved foci to guide project implementation.
These initially broad objectives were not appropriate for developing actual interventions; they were rather a basis
for local policy-level goals. Thus, a second workshop was held to identify which of the objectives were most
important and, subsequently, to develop a more refined set of objectives that would provide focused and tangible
targets as well as guidance for the identification of project activities. A group rank-scoring exercise was carried
out to identify the three most important of the original set of eight community natural resource management
objectives. These were then explored in greater detail, and the major sub-objectives, which would result in
achievement of the broad objective, were identified using a graphical representation (called spidergrams) that
enabled people to identify components of an answer to a given question and to weight each component of the
answer (Lynam 1999). The sub-objectives were ranked based on importance scoring (
Fig. 1); then the most
important of these were taken as workable objectives. Sub-objectives with the highest scores were ranked as the
most important.
Fig. 1. Sub-objectives associated with the community objective of resource conservation
and their associated importance scores.
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