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Uncertainty will be an important factor in many decisions. In
most cases, the goal of further analysis of uncertainty is not
necessarily to reduce it, but to better understand it and its
implications for the decision. There are many analytical methods
for treating uncertainty (e.g., sensitivity analysis, scenario
analysis, Monte carlo simulation, etc.); the choice depends on
the technical details of the problem. However, some general
guidelines for dealing with uncertainty in the decision process
include:
- Document uncertainties and identify those most likely to
have an impact on the decision. There will be many
uncertainties – only a few will matter.
- Characterize these uncertainties as explicitly and
unambiguously as possible. This could be in the form of
assigning probabilities to competing hypotheses, or developing
probability distributions for uncertain parameters.
- Estimate the consequences of management alternatives
probabilistically or under alternative scenarios. If the
results are insensitive, either the uncertainty is unimportant
in the context of the decision, or the evaluation criteria
need to be re-defined to reflect the impact of the uncertainty
on the decision.
- When different management alternatives have different
distributions of outcomes, some alternatives being more risky
than others, ensure that presentation of consequences captures
the risk trade-offs. Use graphic representations such as risk
profiles to support he consequence table where necessary.
- Report the level of agreement and disagreement among
experts; this is important information for decision makers.
- Provide context for understanding the relevance and
significance of uncertainty. For example, are results close to
a recognized or legal standard? Are results near a threshold
of severe impact or near a steep part of a cost curve? In
general, what are the consequences of being wrong, and are
there alternatives that are more robust than others?
- Where relevant, identify options for reducing uncertainty
to aid future decisions (e.g., potential research, monitoring,
management initiatives). Ensure that there is in fact some
future decision that this reduction in uncertainty will
support.
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Evaluate and Select>>
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Uncertainty pervades most real-world decisions