1.5. Using Dynamic Modeling to Generate Consensus
15
provide decision makers with an ability to play out the consequences of alternative actions in what-if scenarios. Although these decision support tools
are a step forward in empowering decision makers , they still are based on
the understanding that an outside expert brings to the problem, rather than
on the knowledge of the people directly involved. The question: What
should I do? now changes to: What does the model do?The problem is then
not whether to believe the experts' answers, but whether to believe the assumptions they put into their models. And, certainly, we all can find fault
with some assumption, and therefore disregard a model 's validity.
Another strategy is to go all the way and have those people who have a
stake in a particular decision develop computer models themselves. The
usual response to this suggestion is that problem-specific model development by stakeholders would be too costly and time consuming, and that
there is no guarantee that at the end the model would be a better decisionsupport tool than one developed by outside experts. But that does not
need to be so. The concepts, methods and tools of dynamic modeling presented in this book enable virtually anyone to develop dynamic models of
complex systems and to effectively communicate different assumptions
among the various stakeholders-such as the decision makers, the scientists and other experts, and the public. You will learn these concepts, methods and tools as you work through this book. And you should use them to
develop models in a group which includes, if possible, those who have a
problem to resolve. Work with them, help them identify the questions to be
answered by the modeling process, help them arrive at an agreeable solution and finally, help them formulate new questions about their system. In
this way you will learn a lot from others , and you will help people become
modelers, rather than skeptical users of models developed for them-models whose construction is a mystery to them and models they do not fully
understand or believe in. The very nature of this book and the books of the
Dynamic Modeling Series is to help you in learning how to translate your
mental models into rigorous computer-based models , and how to engage
yourself and others in a continuous learning process.
Besides helping people to handle uncertainty, feedback, lags, and group
decision making , the development of formal and computer models provides authentic tasks that are intellectually challenging and rewarding.
Through exchange of models among modelers, the learning process turns
into a cognitive apprenticeship in which all members of the modeling
group can learn from each other.
The process and product of dynamic modeling can help highlight gaps in
our understanding, and it helps identify the most important parameters in a
system. As models are developed, they provide a record of the existing understanding. When the models are run, they reveal "normal" system behavior if no interference into the system takes place, and they may reveal emergent properties of the system. We may see smooth dynamics, or perhaps
erratic transitions from one type of dynamics to another. Such knowledge
15
provide decision makers with an ability to play out the consequences of alternative actions in what-if scenarios. Although these decision support tools
are a step forward in empowering decision makers , they still are based on
the understanding that an outside expert brings to the problem, rather than
on the knowledge of the people directly involved. The question: What
should I do? now changes to: What does the model do?The problem is then
not whether to believe the experts' answers, but whether to believe the assumptions they put into their models. And, certainly, we all can find fault
with some assumption, and therefore disregard a model 's validity.
Another strategy is to go all the way and have those people who have a
stake in a particular decision develop computer models themselves. The
usual response to this suggestion is that problem-specific model development by stakeholders would be too costly and time consuming, and that
there is no guarantee that at the end the model would be a better decisionsupport tool than one developed by outside experts. But that does not
need to be so. The concepts, methods and tools of dynamic modeling presented in this book enable virtually anyone to develop dynamic models of
complex systems and to effectively communicate different assumptions
among the various stakeholders-such as the decision makers, the scientists and other experts, and the public. You will learn these concepts, methods and tools as you work through this book. And you should use them to
develop models in a group which includes, if possible, those who have a
problem to resolve. Work with them, help them identify the questions to be
answered by the modeling process, help them arrive at an agreeable solution and finally, help them formulate new questions about their system. In
this way you will learn a lot from others , and you will help people become
modelers, rather than skeptical users of models developed for them-models whose construction is a mystery to them and models they do not fully
understand or believe in. The very nature of this book and the books of the
Dynamic Modeling Series is to help you in learning how to translate your
mental models into rigorous computer-based models , and how to engage
yourself and others in a continuous learning process.
Besides helping people to handle uncertainty, feedback, lags, and group
decision making , the development of formal and computer models provides authentic tasks that are intellectually challenging and rewarding.
Through exchange of models among modelers, the learning process turns
into a cognitive apprenticeship in which all members of the modeling
group can learn from each other.
The process and product of dynamic modeling can help highlight gaps in
our understanding, and it helps identify the most important parameters in a
system. As models are developed, they provide a record of the existing understanding. When the models are run, they reveal "normal" system behavior if no interference into the system takes place, and they may reveal emergent properties of the system. We may see smooth dynamics, or perhaps
erratic transitions from one type of dynamics to another. Such knowledge