Description
Research methods for risk analysisKnowledge associated with risk could not refer, in many instances, to objective knowledge, as conventionally understood. Future and unobserved events or quantities cannot be known with certainty before they occur or assume a value. Unlike objective knowledge, risk knowledge can only be justified by using direct evidence that becomes available, indirect evidence from other observed quantities, supported by modelling predictions, and expert judgement.
The scientific scrutiny of risk knowledge (usually about non-observed events and quantities in the future), by, e.g., the scientific method, is often challenging. For example, accurate prediction is usually difficult because research methods such as case-control experimentation cannot be applied. This shows that there are difficulties in verifying and validating risk knowledge and it is unlikely that the conventional tools of the scientific method can meaningfully evaluate it.
Alternative methods are required to provide credibility in the assessment of risk and these are described and used in the course
General objectives:
To provide students with fundamental knowledge and tools about research methods for risk analysis.
To encourage the use of modern tools for research in risk in future students’ research challenges.
Duration: Customised to the particular arrangement.
Scope and form: Design and implementation of individualised tutoring, guiding documents, lectures and assignments. As an engagement strategy, a case analysis will be used throughout the course. The case will deal with researching a specific risk challenge.
Target groups: Master students of different faculties.
Learning objectives
On completion of the course, a student will be able to:
Understand the standardised notions of risk and related concepts.
Understand the notion of risk knowledge and how it differs from objective knowledge
Acquire hands-on skills to use research tools.
Content:
Society for Risk Analysis’ definitions
Epistemology of risk
Tools to assess the quality of data
Tools to assess the quality of models
Tools to assess the quality of assumptions
Tools to assess the credibility of predictions
Structured expert elicitation methods
Course Literature
Aven T (2019) The science of risk analysis: Foundation and practice. Routledge, London. https://doi-org/10.4324/9780429029189
Bedford T, Quigley J, Walls L. (2006) Expert elicitation for reliable system design. Stat Sci, 21(4): 428–450.
Fenton N, Neil M (2018) Risk assessment and decision analysis with Bayesian networks. CRC Press.
Garthwaite PH, Kadane JB, O’Hagan A. (2005) Statistical methods for eliciting probability distributions. J Am Stat Assoc, 100(470): 680–700.
Goossens LHJ, Cooke RM, Hale AR, Rodic-Wiersma LJ. (2008) Fifteen years of expert judgement at TUDelft. Safety Sci, 46(2): 234–244.
Hallowell MR, Gambatese JA. (2010) Qualitative research: Application of the Delphi method to CEM research. J Constr Eng Manage. 136(1): 99–107.
Society for Risk Analysis (2018) Society for Risk Analysis glossary. Society for Risk Analysis web. https://www.sra.org/wp-content/uploads/2020/04/SRA-Glossary-FINAL.pdf.