  Bayesian decision analysis is a useful method for risk management decisions,
but is limited in its ability to consider severe uncertainty in knowledge, and
value ambiguity in management objectives. We study the use of robust Bayesian
decision analysis to handle problems where one or both of these issues arise.
The robust Bayesian approach models severe uncertainty through bounds on
probability distributions, and value ambiguity through bounds on utility
functions. To incorporate data, standard Bayesian updating is applied on the
entire set of distributions. To elicit our expert's utility representing the
value of different management objectives, we use a modified version of the
swing weighting procedure that can cope with severe value ambiguity. We
demonstrate these methods on an environmental management problem to eradicate
an alien invasive marmorkrebs recently discovered in Sweden, which needed a
rapid response despite substantial knowledge gaps if the species was still
present (i.e. severe uncertainty) and the need for difficult tradeoffs and
competing interests (i.e. value ambiguity). We identify that the decision
alternatives to drain the system and remove individuals in combination with
dredging and sieving with or without a degradable biocide, or increasing pH,
are consistently bad under the entire range of probability and utility bounds.
This case study shows how robust Bayesian decision analysis provides a
transparent methodology for integrating information in risk management problems
where little data are available and/or where the tradeoffs ambiguous.
