Decision Analysis for Management of Natural Hazards

Simpson, Michael; James, Rachel; Borgomeo, Edoardo; Almeida, Susana; Kingsborough, Ashley; Economou, Theo; Stephenson, David B.; Wagener, Thorsten

doi:10.1146/annurev-environ-110615-090011

Cited by 45 publications

(26 citation statements)

References 150 publications

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“…The primary reason for making uncertainty assessments for evaluating risk in natural hazards is because taking account of uncertainty might make a difference to the decision that is made (e.g. Hall and Solomatine, 2008;Rougier and Beven, 2013;Hall, 2003;and Simpson et al, 2016). For many decisions a complete, thoughtful, uncertainty assessment of risk might not be justified by the cost in time and effort.…”

Section: Assessing Whether a Decision Is Robust To The Chosen Assumptmentioning

confidence: 99%

“…Hall, 2006;Baroni and Tarantola, 2014;and Savage et al, 2016) or that allow us to explore the impact of distributions that are practically unconstrained due to a lack of observations (e.g. Prudhomme et al, 2010;Singh et al, 2014;Almeida et al, 2017). Alternatively, we can directly select an approach that attempts to find robust decisions in the presence of poorly bounded uncertainties (e.g.…”

Section: Assessing Whether a Decision Is Robust To The Chosen Assumptmentioning

confidence: 99%

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Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice?

Beven

Aspinall

Bates

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Part 1 of this paper has discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. Such deficits may include uncertainties about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalized on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions made, say, for risk management, so it is important to examine the sensitivity of such decisions to different feasible sets of assumptions, to communicate the meaning of associated uncertainty estimates, and to provide an audit trail for the analysis. A conceptual framework for good practice in dealing with epistemic uncertainties is outlined and the implications of applying the principles to natural hazard assessments are discussed. Six stages are recognized, with recommendations at each stage as follows: (1) framing the analysis, preferably with input from potential users; (2) evaluating the available data for epistemic uncertainties, especially when they might lead to inconsistencies; (3) eliciting information on sources of uncertainty from experts; (4) defining a workflow that will give reliable and accurate results; (5) assessing robustness to uncertainty, including the impact on any decisions that are dependent on the analysis; and (6) communicating the findings and meaning of the analysis to potential users, stakeholders, and decision makers. Visualizations are helpful in conveying the nature of the uncertainty outputs, while recognizing that the deeper epistemic uncertainties might not be readily amenable to visualizations.

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Section: Assessing Whether a Decision Is Robust To The Chosen Assumptmentioning

confidence: 99%

Section: Assessing Whether a Decision Is Robust To The Chosen Assumptmentioning

confidence: 99%

Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice?

Beven

Aspinall

Bates

et al. 2018

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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“…However, in a situation of a high degree of doubt, two approaches may make a difference: namely flexibility and robustness. Decisions are flexible to the extent that "[they] can be reversed or modified as more information becomes available in the future" [33]. They are robust to the extent that they are "resilient to surprise, immune to ignorance" [25].…”

Section: Managementmentioning

confidence: 99%

A Conceptual Framework to Classify and Manage Risk, Uncertainty and Ambiguity: An Application to Energy Policy

Gaudard

Romerio

2020

Energies

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Global issues are such that we should assess and manage a variety of risks and uncertainties. Due to increasing world complexity, the development of an adequate and innovative conceptual framework, anchored in the literature, is required. This article contributes to this effort with an approach particularly relevant to decision-makers dealing with threats of different natures, limited heterogeneous information, and experts' assessments tainted by doubts. Our approach is based on two pillars: 1) An "acuity scale", based on the probability of the occurrence of an event, its impact and the experts' degree of doubt; 2) A taxonomy focused on the concepts of risk, uncertainty, gamble and butterfly ambiguity. Accordingly, we present in a second step the major management implications of such approach. Global policy trends (e.g., sustainability transition) put energy sector decision-makers at the forefront of risk and uncertainty management. Consequently, we carry out a case study focused on Swiss energy policy since the 1980s, including its inception, the turnaround provoked by the Fukushima accident, and the government's 2050 energy strategy. Our investigation shows that the proposed conceptual framework allows for the development of an original analysis of the main drivers that influence governmental policies and stakeholder strategies.

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“…These frameworks have appealing properties in conditions of deep uncertainty, and do not depend on characterizing uncertainty via probability distributions (Cox, ; Heal & Millner, ; Lempert & Collins, ). A final alternative is sequential strategies, which build flexibility into decision making through staged implementation of mitigation efforts, leaving space for adaptation to changing conditions (Simpson et al., ). The general point is that risk assessments are typically not a direct input to the decisionmaker, but rather fed into a broader analysis framework wherein decisionmakers’ (or societal) preferences are explicitly incorporated, for example, through assigning utilities, measures of risk aversion and equity, and so forth, or informally incorporated via structured decision processes.…”

Section: Null Hypothesis Testing Is a Poor Descriptive And Normative mentioning

confidence: 99%

Null Hypothesis Testing ≠ Scientific Inference: A Critique of the Shaky Premise at the Heart of the Science and Values Debate, and a Defense of Value‐Neutral Risk Assessment

MacGillivray¹

2019

Risk Analysis

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Many philosophers and statisticians argue that risk assessors are morally obligated to evaluate the probabilities and consequences of methodological error, and to base their decisions of whether to adopt a given parameter value, model, or hypothesis on those considerations. This argument is couched within the rubric of null hypothesis testing, which I suggest is a poor descriptive and normative model for risk assessment. Risk regulation is not primarily concerned with evaluating the probability of data conditional upon the null hypothesis, but rather with measuring risks, estimating the consequences of available courses of action and inaction, formally characterizing uncertainty, and deciding what to do based upon explicit values and decision criteria. In turn, I defend an ideal of value‐neutrality, whereby the core inferential tasks of risk assessment—such as weighing evidence, estimating parameters, and model selection—should be guided by the aim of correspondence to reality. This is not to say that value judgments be damned, but rather that they should be accounted for within a structured approach to decision analysis, rather than embedded within risk assessment in an informal manner.

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Decision Analysis for Management of Natural Hazards

Cited by 45 publications

References 150 publications

Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice?

Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice?

A Conceptual Framework to Classify and Manage Risk, Uncertainty and Ambiguity: An Application to Energy Policy

Null Hypothesis Testing ≠ Scientific Inference: A Critique of the Shaky Premise at the Heart of the Science and Values Debate, and a Defense of Value‐Neutral Risk Assessment

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