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Robust decision-making under severe uncertainty for conservation management

Regan, H M; Ben-Haim, Y; Langford, B; Wilson, W G; Lundberg, Per LU ; Andelman, S J and Burgman, M A (2005) In Ecological Applications 15(4). p.1471-1477
Abstract
In-conservation biology it is necessary to make management decisions for endangered and threatened species under severe uncertainty. Failure to acknowledge and treat uncertainty can lead to poor decisions. To illustrate the importance of considering uncertainty, we reanalyze a decision problem for the Sumatran rhino, Dicerorhinus sumatrensis, using information-gap theory to propagate uncertainties and to rank management options. Rather than requiring information about the extent of parameter uncertainty at the outset, information-gap theory addresses the question of how much uncertainty can be tolerated before our decision would change. It assesses the robustness of decisions in the face of severe uncertainty. We show that different... (More)
In-conservation biology it is necessary to make management decisions for endangered and threatened species under severe uncertainty. Failure to acknowledge and treat uncertainty can lead to poor decisions. To illustrate the importance of considering uncertainty, we reanalyze a decision problem for the Sumatran rhino, Dicerorhinus sumatrensis, using information-gap theory to propagate uncertainties and to rank management options. Rather than requiring information about the extent of parameter uncertainty at the outset, information-gap theory addresses the question of how much uncertainty can be tolerated before our decision would change. It assesses the robustness of decisions in the face of severe uncertainty. We show that different management decisions may result when uncertainty in utilities and probabilities are considered in decision-making problems. We highlight the importance of a full assessment of uncertainty in conservation management decisions to avoid, as much as possible, undesirable outcomes. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Ecological Applications
volume
15
issue
4
pages
1471 - 1477
publisher
Ecological Society of America
external identifiers
  • wos:000230876900033
  • scopus:23044493930
ISSN
1051-0761
DOI
10.1890/03-5419
language
English
LU publication?
yes
id
6ff1f57f-76fd-4858-b502-4555167e5d48 (old id 147395)
date added to LUP
2007-07-03 15:36:51
date last changed
2017-11-12 03:56:05
@article{6ff1f57f-76fd-4858-b502-4555167e5d48,
  abstract     = {In-conservation biology it is necessary to make management decisions for endangered and threatened species under severe uncertainty. Failure to acknowledge and treat uncertainty can lead to poor decisions. To illustrate the importance of considering uncertainty, we reanalyze a decision problem for the Sumatran rhino, Dicerorhinus sumatrensis, using information-gap theory to propagate uncertainties and to rank management options. Rather than requiring information about the extent of parameter uncertainty at the outset, information-gap theory addresses the question of how much uncertainty can be tolerated before our decision would change. It assesses the robustness of decisions in the face of severe uncertainty. We show that different management decisions may result when uncertainty in utilities and probabilities are considered in decision-making problems. We highlight the importance of a full assessment of uncertainty in conservation management decisions to avoid, as much as possible, undesirable outcomes.},
  author       = {Regan, H M and Ben-Haim, Y and Langford, B and Wilson, W G and Lundberg, Per and Andelman, S J and Burgman, M A},
  issn         = {1051-0761},
  language     = {eng},
  number       = {4},
  pages        = {1471--1477},
  publisher    = {Ecological Society of America},
  series       = {Ecological Applications},
  title        = {Robust decision-making under severe uncertainty for conservation management},
  url          = {http://dx.doi.org/10.1890/03-5419},
  volume       = {15},
  year         = {2005},
}