The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

ullrika at the uncertainty show

Ullrika Sahlin

Senior lecturer

ullrika at the uncertainty show

Robust Decision Analysis under Severe Uncertainty and Ambiguous Tradeoffs : An Invasive Species Case Study

Author

  • Ullrika Sahlin
  • Matthias C.M. Troffaes
  • Lennart Edsman

Summary, in English

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.

Department/s

  • Computational Science for Health and Environment
  • Centre for Environmental and Climate Science (CEC)
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2021

Language

English

Pages

2140-2153

Publication/Series

Risk Analysis

Volume

41

Issue

11

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Computer Science
  • Environmental Sciences
  • Probability Theory and Statistics

Keywords

  • Bayesian
  • decision theory
  • invasive species
  • subjective probability
  • utility

Status

Published

Research group

  • Computational Science for Health and Environment

ISBN/ISSN/Other

  • ISSN: 0272-4332