to achieve understanding and to test policy options. This perspective means
that models and policies based on them are not taken as the ultimate
answers but rather as guiding an iterative experimental process with the
regional system. Emphasis is placed on monitoring and feedback to check
and improve models rather than on using models to obfuscate and defend
a policy that does not correspond to reality. Continuing stakeholder
involvement is essential to adaptive management (Holling 1978).
14.5.2 Computational Methods and Toolkits
The collaborative scoping and consensus-building process can be facilitated
by the use of a wide range of modeling tools. In graphic-icon-based tools
(see Chapter 8, Section 8.4.1.1, this volume), the structure of the module is
represented diagrammatically so that new users can recognize the major
interactions at a glance (Costanza 1987). One of the main strengths of such
tools is their ability to enable scientists and decision makers to quickly and
easily build “scoping models” that focus and clarify their mental models.
Running these models enables visualization of the dynamic consequences
hidden in the modelers’ assumptions and understanding of a system. With
relative ease of use, these graphical programming tools offer a powerful
method for investigating the workings of complex systems (Hannon and
Ruth 1997).
Building on the initial scoping models to develop effective research and
management tools often requires the inclusion of spatial interactions in the
model. This phase of the modeling process usually requires more sophisticated tools such as a geographic information system (GIS). In cases that
involve effects on or management of landscapes or habitats, GISs can serve
two distinct but related roles. First, a GIS can improve communication and
understanding among the stakeholders by allowing a way to visualize the
options or impacts in a spatial context. Also, it relates the actions and results
to a known, concrete, familiar place to which the stakeholders can relate.
Second, a GIS can function as an analytical tool that is an integral part of
the modeling process for addressing spatially explicit questions.
A good example of a modelers’ toolkit that integrates disparate applications into a unified, seamless environment is the Spatial Modeling
Environment (SME) (Maxwell and Costanza 1997). The SME links the
STELLA modeling tool with advanced computing resources, allowing users
to easily develop their scoping models into a high-performance spatial
modeling and visualization environment. It is being applied jointly with
state, local, and federal management agencies as an integrated, adaptive
framework for managing ecological–economic systems. Current application
areas include the Patuxent River watershed (Voinov et al. 1999) and the
Baltimore metropolitan area.
Developing an awareness of the potential of and limitations of available
computational hardware and software and training potential users are
280
Thomas P. Maxwell et al.
that models and policies based on them are not taken as the ultimate
answers but rather as guiding an iterative experimental process with the
regional system. Emphasis is placed on monitoring and feedback to check
and improve models rather than on using models to obfuscate and defend
a policy that does not correspond to reality. Continuing stakeholder
involvement is essential to adaptive management (Holling 1978).
14.5.2 Computational Methods and Toolkits
The collaborative scoping and consensus-building process can be facilitated
by the use of a wide range of modeling tools. In graphic-icon-based tools
(see Chapter 8, Section 8.4.1.1, this volume), the structure of the module is
represented diagrammatically so that new users can recognize the major
interactions at a glance (Costanza 1987). One of the main strengths of such
tools is their ability to enable scientists and decision makers to quickly and
easily build “scoping models” that focus and clarify their mental models.
Running these models enables visualization of the dynamic consequences
hidden in the modelers’ assumptions and understanding of a system. With
relative ease of use, these graphical programming tools offer a powerful
method for investigating the workings of complex systems (Hannon and
Ruth 1997).
Building on the initial scoping models to develop effective research and
management tools often requires the inclusion of spatial interactions in the
model. This phase of the modeling process usually requires more sophisticated tools such as a geographic information system (GIS). In cases that
involve effects on or management of landscapes or habitats, GISs can serve
two distinct but related roles. First, a GIS can improve communication and
understanding among the stakeholders by allowing a way to visualize the
options or impacts in a spatial context. Also, it relates the actions and results
to a known, concrete, familiar place to which the stakeholders can relate.
Second, a GIS can function as an analytical tool that is an integral part of
the modeling process for addressing spatially explicit questions.
A good example of a modelers’ toolkit that integrates disparate applications into a unified, seamless environment is the Spatial Modeling
Environment (SME) (Maxwell and Costanza 1997). The SME links the
STELLA modeling tool with advanced computing resources, allowing users
to easily develop their scoping models into a high-performance spatial
modeling and visualization environment. It is being applied jointly with
state, local, and federal management agencies as an integrated, adaptive
framework for managing ecological–economic systems. Current application
areas include the Patuxent River watershed (Voinov et al. 1999) and the
Baltimore metropolitan area.
Developing an awareness of the potential of and limitations of available
computational hardware and software and training potential users are
280
Thomas P. Maxwell et al.