“This Is What We Don't Know” : Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment
Sahlin, Ullrika LU
; Helle, Inari
and Perepolkin, Dmytro
LU
(2021)
In Integrated Environmental Assessment and Management
17(1).
p.221-232
- Abstract
Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify... (More)
Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2020;00:1–12.
(Less)
- author
- Sahlin, Ullrika
LU
; Helle, Inari
and Perepolkin, Dmytro
LU
- organization
- publishing date
- 2021-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bayesian network, Epistemic uncertainty, Model uncertainty, Subjective probability, Uncertainty analysis
- in
- Integrated Environmental Assessment and Management
- volume
- 17
- issue
- 1
- pages
- 12 pages
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85097021367
- pmid:33151017
- ISSN
- 1551-3793
- DOI
- 10.1002/ieam.4367
- language
- English
- LU publication?
- yes
- id
- f331fd99-88bb-4173-8821-ce5083630fda
- date added to LUP
- 2020-12-14 11:02:11
- date last changed
- 2024-06-14 04:49:22
@article{f331fd99-88bb-4173-8821-ce5083630fda,
abstract = {{<p>Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2020;00:1–12.</p>}},
author = {{Sahlin, Ullrika and Helle, Inari and Perepolkin, Dmytro}},
issn = {{1551-3793}},
keywords = {{Bayesian network; Epistemic uncertainty; Model uncertainty; Subjective probability; Uncertainty analysis}},
language = {{eng}},
number = {{1}},
pages = {{221--232}},
publisher = {{Wiley-Blackwell}},
series = {{Integrated Environmental Assessment and Management}},
title = {{“This Is What We Don't Know” : Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment}},
url = {{http://dx.doi.org/10.1002/ieam.4367}},
doi = {{10.1002/ieam.4367}},
volume = {{17}},
year = {{2021}},
}