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Selecting a method/tool for risk-based decision making in complex situations
Journal of Loss Prevention in the Process Industries  (IF3.66),  Pub Date : 2021-10-30, DOI: 10.1016/j.jlp.2021.104669
Hans J. Pasman, William J. Rogers, Stewart W. Behie

After a risk assessment has been completed and feasible risk reduction measures have been reviewed, decisions must be made to select the most appropriate safeguards and/or a decision taken to determine as to what residual risk would be conditionally accepted. The straightforward way is to set up a binary decision tree and compare for each event scenario, the risk reduction gain versus cost of two or more acceptable alternatives. However, often many contributing factors must be considered, such as: the nature, importance, and context of the risk, availability of the measures, procurement and maintenance costs, the impact to personnel and particularly to the public near the hazardous area, vulnerability of the environment, determination and weighting of important contributing safety factors, and uncertainties inherent to the available information.

In such more complex cases, the decision problem takes the form of building argumentation for a preferred solution with a team of experts, or making a choice from a number of options and selection criteria using the independent opinions of experts/stakeholders. The former is known as the Toulmin model of argumentation, the latter are Multi-Criteria Decision Making (MCDM) methods or Multi-Criteria Decision Analysis. In the latter case, one criterion is weighted as more important than another by experts of which in turn opinion can be weighted based on, e.g., education and experience, together resulting in a ranking of the alternatives. Where the Toulmin model will squeeze out explicit rational arguments sharpened by rebuttal ones, in MCDM/MCDA methods due to the weighting and mathematical processing, the best compromise ranking of the options will result, despite experts’ opinions are intuitive. Well-known is the simple linear model of the Analytic Hierarchical Process (AHP), but a number of more sophisticated methods will be briefly described in this document. In Multi- Attribute Utility Theory (MAUT) utility is a guiding principle, hence economics dominate. A number of methods will be selected for working out an example and comparing results.