A Literature Review on the Use of Expert Opinion in Probabilistic Risk Analysis
Risk assessment is part of the decision making process in many fields of discipline, such as engineering, public health, environment, program management, regulatory policy, and finance. There has been considerable debate over the philosophical and...
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| Language: | English en_US |
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World Bank, Washington, DC
2013
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| Online Access: | http://documents.worldbank.org/curated/en/2004/01/3169814/literature-review-use-expert-opinion-probabilistic-risk-analysis http://hdl.handle.net/10986/15623 |
| Summary: | Risk assessment is part of the decision
making process in many fields of discipline, such as
engineering, public health, environment, program management,
regulatory policy, and finance. There has been considerable
debate over the philosophical and methodological treatment
of risk in the past few decades, ranging from its definition
and classification to methods of its assessment.
Probabilistic risk analysis (PRA) specifically deals with
events represented by low probabilities of occurring with
high levels of unfavorable consequences. Expert judgment is
often a critical source of information in PRA, since
empirical data on the variables of interest are rarely
available. The author reviews the literature on the use of
expert opinion in PRA, in particular on the approaches to
eliciting and aggregating experts' assessments. The
literature suggests that the methods by which expert
opinions are collected and combined have a significant
effect on the resulting estimates. The author discusses two
types of approaches to eliciting and aggregating expert
judgments-behavioral and mathematical approaches, with the
emphasis on the latter. It is generally agreed that
mathematical approaches tend to yield more accurate
estimates than behavioral approaches. After a short
description of behavioral approaches, the author discusses
mathematical approaches in detail, presenting three
aggregation models: non-Bayesian axiomatic models, Bayesian
models, and psychological scaling models. She also discusses
issues of stochastic dependence. |
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