Ten Heuristics for Modeling Projects

• File Number: 8461.100
• Description: Adaptive Management Workgroup
• State: CO
• Basin: South Platte
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• Author: Craig R. Nicolson, Anthony M. Starfield, Gary P. Kofinas, John A. Kruse
• Title: Ten Heuristics for Modeling Projects
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Heuristics for Interdisciplinary Modelers 381
ercise. Submodels may operate at different time
scales because of the nature of the underlying pro-
cesses. Similar variables in two submodels may be
represented at different levels of detail from one
another. The probabilistic outcomes produced by
one stochastic submodel may not translate easily
into hard-and-fast input values for other determin-
istic submodels in the system.
Although it is essential to represent adequately
the logic and the results of each submodel in all the
other relevant submodels to which it links, if this is
done properly, it may not be necessary to have one
"supersynthesis" model that runs each submodel
within the same overall programming framework.
In fact, for quality-control purposes, it is probably
good to have some kind of human interface be-
tween submodels. This allows the results of each
submodel to be assessed and the quality of its con-
clusions evaluated, so it can be determined how
best to include the insights gained from each model
in the next submodel. This process helps to deter-
mine which details are not essential and allows the
development of a"boiled down" whole system
model at a level of abstractism that may initially
have been unacceptable to some team members.
A further argument for a suite of models is that
few users of the overall synthesis model will be
interested in all of its components. Most people
have an interest in only three or four of the out-
comes of the model. A suite of models allows users
to examine the components with which they are
familiar and to see how these results fit with the
outcomes they eacpect, based on their knowledge
and experience of that part of the system. However,
for nonscientific users, it is helpful to have a seam-
less interface that allows them to explore whichever
part of the system they are most interested in. If no
such intedace exists, users will not readily recog-
nize that they are seeing an integrated view of the
system, and much of the benefit of the exercise will
be lost.
Heuristic 7. Maintain a healthy balance between the
? well-undetstood and the poorly understood components of
the system. All system models are balancing acts
between what one knows and understands and
what one does not know. The temptation is to put
too much emphasis on those parts of the system
where understanding and data are good and to
ignore or gloss over the areas where little is known.
This is not surprising, given the way in which the
scientific enterprise tends to favor specialists. For
example, even though there may be a clear and
obvious link between caribou migration and house-
hold economic production, a caribou biologist
might know a great deal about caribou activities
and energetics, but relatively little about herd mi-
gration pattems. An economist may have a good
understanding of the factors that influence people's
deasions to take wage employment, but know rel-
atively little about the factors that account for the
successful harvest and production of caribou meat
on the land itself.
Furthermore, people like to concentrate on the
details they know about and understand (Likens
1998). In particular, scientists are socialized into an
epistemological framework that places a high value
on detailed quantitative hard facts and tends to take
a dim view of uncertainty (even when the uncer-
tainty involves an educated guess in an important
area where little else is known). They are often
skeptical of simplification and even more uncom-
fortable with the idea of making educated guesses.
For example, the village economy model we devel-
oped for the SAC Project contained over 90 differ-
ent job categories, each characterized in terms of its
required education level, its seasonal availability,
and whether men or women were more likely to be
found in that position. These definitions were
firmly grounded in survey data. However, the econ-
omists on our team were reluctant to speculate on
how these definitions might evolve over the next
40 years; as a consequence, the model contained
the implicit assumption that social norms such as
gender preferences for job types would not change
during two generations.
Maintaining the balance between the known and
the unknown requires strong project leadership. In
a review of IA projects, Parson (1995) observed:
"Since researchers working within their fields do
not normally attend to borders of other fields,
achieving this attention shift requires some form of
authority in an assessment project, or at least a
coordinating mechanism and a common language
for communicating across boundaries." One way to
achieve such coordination would be to bring in an
outside modeling consultant who could facilitate
key workshops. In addition to providing a fresh
viewpoint, an outsider who has the trust of the
team could also provide the kind of authority that
Parson refers to. Modelers are certainly not the only
people who could fulfill this role. The two most
important qualifications are an ability to see the big
picture and the earned trust and respect of other
team members. These qualities may well be present
in one of the disciplinary specialists if he or she is
also a good big-picture scholar. However, through
rapid prototyping and sensitivity analysis, modeling
can be particularly useful for ranking the relative
importance of the parts and processes in the model,
as well as making a rapid assessment of the value