Sahlin, Ullrika and Troffaes, Matthias C. M. and Edsman, Lennart (2021) 'Robust decision analysis under severe uncertainty and ambiguous tradeoffs: an invasive species case study.', Risk analysis., 41 (11). pp. 2140-2153.
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 are ambiguous.
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|Publisher Web site:||https://doi.org/10.1111/risa.13722|
|Publisher statement:||Risk Analysis © 2021 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.|
|Date accepted:||12 February 2021|
|Date deposited:||19 February 2021|
|Date of first online publication:||05 May 2021|
|Date first made open access:||10 May 2021|
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