Robust decision analysis under severe uncertainty and ambiguous tradeoffs: an invasive species case study

TL;DR

This paper presents a robust Bayesian decision analysis framework that handles severe uncertainty and value ambiguity, demonstrated through an invasive species management case in Sweden, providing transparent risk management decisions.

Contribution

It introduces a robust Bayesian approach with bounds on probabilities and utilities, and a modified utility elicitation method for severe value ambiguity, applied to environmental risk management.

Findings

Drainage and removal strategies are consistently ineffective across all bounds.

The methodology effectively integrates limited data and ambiguous tradeoffs.

Provides transparent decision support in high-uncertainty environmental management scenarios.

Abstract

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.