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.

在完成风险评估和审查可行的风险降低措施后，必须决定选择最合适的保护措施和/或决定有条件地接受哪些剩余风险。直接的方法是建立一个二元决策树并比较每个事件场景的风险降低收益与两个或多个可接受替代方案的成本。然而，通常必须考虑许多促成因素，例如：风险的性质、重要性和背景、措施的可用性、采购和维护成本、对人员的影响，特别是对危险区域附近的公众的影响，风险的脆弱性。环境、重要安全因素的确定和权重，以及可用信息固有的不确定性。

在这种更复杂的情况下，决策问题的形式是与专家团队为首选解决方案进行论证，或使用专家/利益相关者的独立意见从多个选项和选择标准中做出选择。前者被称为论证的 Toulmin 模型，后者是多标准决策（MCDM）方法或多标准决策分析。在后一种情况下，一个标准被专家认为比另一个标准更重要，而专家的意见又可以根据例如教育和经验进行加权，共同导致备选方案的排名。在 MCDM/MCDA 方法中，由于权重和数学处理，Toulmin 模型将挤出被反驳尖锐的明确理性论证，尽管专家的意见是直观的，但将产生选项的最佳折衷排名。众所周知的是层次分析过程 (AHP) 的简单线性模型，但本文档将简要介绍一些更复杂的方法。在多属性效用理论（MAUT）中，效用是一个指导原则，因此经济学占主导地位。将选择多种方法来计算示例并比较结果。