of knowledge (Schmoldt and Rauscher 1996). One goal of AI research is
to program computers to produce seemingly “intelligent” behavior, and this
ability has several applications in ecological modeling. AI can provide an
“intelligent” interface with a model, providing context-sensitive help and
direction in using the model, and it can provide guidance in interpreting the
results. Communication of model characteristics can be aided by interfaces
that allow users to click on icons of model modules to delve deeper into
the structure and assumptions behind each piece of the model. Each icon
can be expanded to show the underlying knowledge used to describe the
associated process and the interactions between processes. Examples of this
kind of representation are the STELLA-based models (Hannon and Roth
1997) and the logic-based models mentioned in Section 8.2.2.
8.4.1.2 Gaming
Communication of model results can also be enhanced when simulation
models are used in a gaming environment to determine strategies that are
optimal for achieving goals. Game theory involves the mathematical analysis of abstract models of strategic competition. Such models are often used
in military and economic planning and more recently in land-use decision
making. In these games, the rules are clearly set forward, but the ramifications of these rules are not always apparent even though (or perhaps
because) they are determined by feedback loops within the system. Sometimes unexpected or random events (such as storms) are simulated in the
models. It is critical that the permissible actions, information available to
each participant, and criteria for termination of the game be made clear.
Typically, there is no single way to win such a game. Optimal strategies
depend upon the goals of the player, and developing a variety of potential
actions may help determine appropriate strategies to attain the desired
outcome. The advantage of using a gaming approach in environmental
decision making is that the options of decision makers can be set forward
without the expense or time involved in actually implementing such
options. The engaging nature of these games causes the user to become
more involved in thinking about the process and interactions than they
would without the gaming tool.
8.4.1.3 Dealing with Uncertainty
A key element of model communication involves appropriate attention to
the uncertainties in the data, model structure, and model projections.
Models always contain some errors and inaccuracies because they are
simplifications of reality. One of the critical tasks in the use of models is to
identify sources of uncertainty and describe the effects of these uncertainties on model predictions so that the output of the model can reliably
support decision making.
Two strategies are available for dealing with model uncertainty. Many
population models embrace and acknowledge uncertainty by selecting
8. Evolving Approaches and Technologies
151
to program computers to produce seemingly “intelligent” behavior, and this
ability has several applications in ecological modeling. AI can provide an
“intelligent” interface with a model, providing context-sensitive help and
direction in using the model, and it can provide guidance in interpreting the
results. Communication of model characteristics can be aided by interfaces
that allow users to click on icons of model modules to delve deeper into
the structure and assumptions behind each piece of the model. Each icon
can be expanded to show the underlying knowledge used to describe the
associated process and the interactions between processes. Examples of this
kind of representation are the STELLA-based models (Hannon and Roth
1997) and the logic-based models mentioned in Section 8.2.2.
8.4.1.2 Gaming
Communication of model results can also be enhanced when simulation
models are used in a gaming environment to determine strategies that are
optimal for achieving goals. Game theory involves the mathematical analysis of abstract models of strategic competition. Such models are often used
in military and economic planning and more recently in land-use decision
making. In these games, the rules are clearly set forward, but the ramifications of these rules are not always apparent even though (or perhaps
because) they are determined by feedback loops within the system. Sometimes unexpected or random events (such as storms) are simulated in the
models. It is critical that the permissible actions, information available to
each participant, and criteria for termination of the game be made clear.
Typically, there is no single way to win such a game. Optimal strategies
depend upon the goals of the player, and developing a variety of potential
actions may help determine appropriate strategies to attain the desired
outcome. The advantage of using a gaming approach in environmental
decision making is that the options of decision makers can be set forward
without the expense or time involved in actually implementing such
options. The engaging nature of these games causes the user to become
more involved in thinking about the process and interactions than they
would without the gaming tool.
8.4.1.3 Dealing with Uncertainty
A key element of model communication involves appropriate attention to
the uncertainties in the data, model structure, and model projections.
Models always contain some errors and inaccuracies because they are
simplifications of reality. One of the critical tasks in the use of models is to
identify sources of uncertainty and describe the effects of these uncertainties on model predictions so that the output of the model can reliably
support decision making.
Two strategies are available for dealing with model uncertainty. Many
population models embrace and acknowledge uncertainty by selecting
8. Evolving Approaches and Technologies
151