What can we
learn from everyday decisions that can be helpful for critical thinking about
complex decision making? To gain some insight concerning an answer to this
question, let’s examine the
decision that a family makes when considering a hike in the mountains for the
weekend. The hike is an enjoyable family activity; it is an experience that has
value, or in the terminology of decision analysis, it has utility for the family. In general terms, we can consider the utility, or “value,” of any item or
experience as one of two essential components of a decision. For outdoor
activities like hiking, the weather during the hike is also a factor; a family
is apt to cancel or postpone a hike if the weather forecast calls for heavy
rain. The weather in this situation is the state
of nature, and the family’s knowledge of the weekend weather is uncertain,
which is a common state of affairs for most decisions we confront that require
us to estimate conditions in the future. In decision analysis, we characterize
this uncertainty in the state of nature (e.g., the weekend weather forecast) as
a probability. Probability is the
second essential component of a decision.
Most interesting decisions involve multiple
objectives or attributes. That is, real decisions usually require consideration
of multiple endpoints or multiple outcomes of interest, such as overall costs,
distribution of costs, environmental impacts, human health impacts,…To address
these decisions, we can use the same approach involving probability and utility
as described above. However, we first need to identify all objectives relevant
to the decision, and the measures of effectiveness (or “attributes”) that
indicate the degree to which each objective is achieved by a proposed action.
Stated another way, all problem-specific objectives, and the attributes or
features of an outcome that are valued by a decision maker and are affected by
the decision, should first be determined.
While identification of each important
objective may seem so obvious that it does not need to be stated, observation
of current practice in environmental management indicates otherwise. In too
many instances, relatively little time appears to be allocated to identifying
and agreeing upon program objectives. Instead it seems that a few obvious
objectives are quickly identified, and most of the effort is then devoted to
data gathering, scientific research, modeling, and analysis.
For example, in lake eutrophication
management studies, scientific research, monitoring, and assessment are often
focused on quantifying the relationship between nutrient loading and in-lake
nutrient (phosphorus and nitrogen) concentration. In some cases, this emphasis
may be appropriate. However, in other cases this assessment focus may simply be
following familiar, well-studied paths, with little forethought. In these cases, a thoughtful consideration of
objectives and attributes might have identified major fishkills as the most
uncertain factor in need of scientific clarification. In this situation, the
result of inadequate attention to the objectives may be an incomplete analysis
or an analysis of the wrong problem.
The objectives of a problem under study may
be clarified through the process of constructing an objectives hierarchy or
value tree. For the management of eutrophication in Lake Sunapee, a
recreational lake in New Hampshire, an objectives hierarchy has been constructed
and is presented in Figure 1. This hierarchy begins with an all-encompassing
objective at the top; a comprehensive set of issue-specific objectives is then
derived with objectives that are consistent with the overall objective.
Finally, attributes (identified by the arrowheads in the figure) that are
meaningful, measurable, and predictable are derived for each specific
objective.
Attributes provide the essential link between
the program objectives or policy and the information needs. If decisions are to
be made based on attribute levels, then the attributes must be meaningful to
the decision maker. For example, even though Lake Sunapee is currently managed
based on total phosphorus concentration, Figure 1 indicates that total
phosphorus is not a meaningful attribute to decision makers. Meaningful
attributes for eutrophication concern the areal extent of aquatic weed growth,
fish quantity and quality, and other measures of direct concern to the public
presented in Figure 1. While attributes like these are more difficult to
scientifically understand and predict, they do reflect public values or
utility, and thus they will be a measure by which the public assesses the
success of a management program. The decision maker should translate all objectives
into meaningful attributes like those above and then present these attributes
to scientists/engineers as indicative of the specific information needs for the
problem under study.
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| Figure 1 |
It is possible, of course, that the
scientist/engineer may be unable to quantify or model an important attribute.
Another necessary condition for attributes is that they should be measurable or
that they can be predicted reasonably well with a mathematical model or with
expert scientific judgment. In order for the scientist to provide information
on an attribute, it must be possible to measure or observe the attribute.
Alternatively, if prediction of a future, unrealized level of the attribute is
needed, then consideration must be given to specifying, calibrating, and
testing a model (mathematical, judgmental, or both) that can be used to provide
the prediction.
Attribute determination may be an iterative
process involving the scientist and the decision maker. Some attributes may not
be both meaningful and measurable; as a result, compromises may be required to
identify measurable attributes that have meaning to the decision maker. The
final choice of the attributes should be the responsibility of the decision
maker, not of the scientist/engineer, since the decision maker must interpret
and use the information for management purposes.
Once there is general agreement on the
management objectives and attributes, the analysis can begin; the purpose of
the analysis at this stage is to estimate or predict the levels of the
attributes associated with implementation of each of the management options.
For the management of eutrophication in Lake Sunapee, the first column of Figure
2 provides a list of some of the options that have been proposed. Across the
top of the table are the attributes identified through the development of the
objectives hierarchy.
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| Figure 2 |
The next step sounds straightforward but is
extremely difficult to do thoroughly and well - fill in Figure 2. The entries
in the body of the table should represent what each management option achieves
for each attribute. Thus, for example, the table cell for the intersection of
"restrict shoreland fertilizer application" (management action) with
"water quality standards" (attribute) should contain a prediction
(with uncertainty estimated) of the level of the attribute expected if that
particular management strategy is implemented. In Figure 3, a miniature “boxes
and arrows” diagram is presented in this table cell to represent a
probabilistic Bayes network model that would be used to predict the effect of
that management action (restrict shoreland fertilizer application) on that
attribute (water quality standards), with uncertainty analysis. This Bayes network
is shown in Figure 4. The objectives-attributes table is presented again in
Figure 5, with the prediction from the Bayes network model shown as a
probability density function for the water quality standards attribute. In
principle, models like the Bayes network would be applied to completely fill-in
the objectives-attributes table; in reality, the most cost-effective management
actions will be analyzed for the most important attributes.
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| Figure 3 |
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| Figure 4 |
As a final thought, several points should be made
concerning this assessment for Lake Sunapee:
(1) Some attributes still need to be more
specific (e.g., What are appropriate units of measurement for "fish
quantity and quality"?).
(2) The management options need more
explanation (e.g., What are the viable limits on impervious area, shoreland
lawn, and marina activity?) so that predictions can be made.
(3) An overall strategy may involve a
combination of management options.
(4) Prediction of attribute level is likely
to involve a combination of statistical relationships, mechanistic simulation
models, uncertainty analysis, and expert judgment.
