We trust that, just like the auto mechanic, we will be clever enough to clear up the
problems created by the introduced change. We let our tendency toward optimism
mask the new problems.
However, the level of intervention in social and ecological systems has become
so great that the adverse effects cannot be ignored. As our optimism about repair
begins to crumble, we take on the attitude of patience toward the inevitable—
unassignable cancer risk, global warming, fossil fuel depletion—the list is long. We
are pessimistic about our ability to identify and influence cause and effect relationships. We need to understand the interactions of the components of dynamic
systems in order to guide our actions. We need to add synthetic thinking to the
reductionist approach. Otherwise we will continue to be overwhelmed by details,
failing to see the forest for the trees.
There is something useful that we can do to turn from this path. We can
experiment using computer models. Models give us predictions of the short- and
long-term outcomes of proposed actions. To do this we can effectively combine
mathematical models with experimentation. By building on the strengths of each
we will gain insight that exceeds the knowledge derived from choosing one method
over the other. Experimenting with computer models will open a new world in our
understanding of dynamic system. The consequences of discovering adverse effects
in a computer model are no more than ruffled pride.
Computer modeling has been with us for over 50 years. Why then are we so
enthusiastic about its use now? The answer comes from innovations in software and
powerful, affordable hardware available to most individuals. Almost anyone can
now begin to simulate real-world phenomena on their own, in terms that are easily
explainable to others. Computer models are no longer confined to the computer
laboratory. They have moved into every classroom, and we believe they can and
should move into the personal repertoire of every educated citizen. Even more
important, we believe that the modern biologist and ecologist should, before
beginning any lab or field experiments, formulate their hypothesis and construct a
model to address it. This struggle for understanding will not only clarify the
biological dynamics but also point to the parameters that need the appropriate
levels of determination through the ensuing lab and field experiments. Model
first, before the lab or field experiment. It is less time and resource consuming
and produces more meaningful experiments.
The ecologist Garrett Hardin and the physicist Heinz Pagels have noted that an
understanding of system function, as a specific skill, needs to be and can become an
integral part of general education. It requires the recognition (easily demonstrable
with exceedingly simple computer models) that the human mind is not capable of
handling very complex dynamic models by itself. Just as we need help in seeing
bacteria and distant stars, we need help modeling dynamic systems. We do solve the
crucial dynamic modeling problem of ducking stones thrown at us or of safely
crossing busy streets. We learned to solve these problems by being shown the
logical outcome of mistakes or through survivable accidents of judgment. We
experiment with the real world as children and get hit by hurled stones, or we let
adults play out their mental model of the consequences for us and we believe them.
viii
Preface
problems created by the introduced change. We let our tendency toward optimism
mask the new problems.
However, the level of intervention in social and ecological systems has become
so great that the adverse effects cannot be ignored. As our optimism about repair
begins to crumble, we take on the attitude of patience toward the inevitable—
unassignable cancer risk, global warming, fossil fuel depletion—the list is long. We
are pessimistic about our ability to identify and influence cause and effect relationships. We need to understand the interactions of the components of dynamic
systems in order to guide our actions. We need to add synthetic thinking to the
reductionist approach. Otherwise we will continue to be overwhelmed by details,
failing to see the forest for the trees.
There is something useful that we can do to turn from this path. We can
experiment using computer models. Models give us predictions of the short- and
long-term outcomes of proposed actions. To do this we can effectively combine
mathematical models with experimentation. By building on the strengths of each
we will gain insight that exceeds the knowledge derived from choosing one method
over the other. Experimenting with computer models will open a new world in our
understanding of dynamic system. The consequences of discovering adverse effects
in a computer model are no more than ruffled pride.
Computer modeling has been with us for over 50 years. Why then are we so
enthusiastic about its use now? The answer comes from innovations in software and
powerful, affordable hardware available to most individuals. Almost anyone can
now begin to simulate real-world phenomena on their own, in terms that are easily
explainable to others. Computer models are no longer confined to the computer
laboratory. They have moved into every classroom, and we believe they can and
should move into the personal repertoire of every educated citizen. Even more
important, we believe that the modern biologist and ecologist should, before
beginning any lab or field experiments, formulate their hypothesis and construct a
model to address it. This struggle for understanding will not only clarify the
biological dynamics but also point to the parameters that need the appropriate
levels of determination through the ensuing lab and field experiments. Model
first, before the lab or field experiment. It is less time and resource consuming
and produces more meaningful experiments.
The ecologist Garrett Hardin and the physicist Heinz Pagels have noted that an
understanding of system function, as a specific skill, needs to be and can become an
integral part of general education. It requires the recognition (easily demonstrable
with exceedingly simple computer models) that the human mind is not capable of
handling very complex dynamic models by itself. Just as we need help in seeing
bacteria and distant stars, we need help modeling dynamic systems. We do solve the
crucial dynamic modeling problem of ducking stones thrown at us or of safely
crossing busy streets. We learned to solve these problems by being shown the
logical outcome of mistakes or through survivable accidents of judgment. We
experiment with the real world as children and get hit by hurled stones, or we let
adults play out their mental model of the consequences for us and we believe them.
viii
Preface