The plausibility concept has gained increasing attention in recent years in risk analysis settings. A number of definitions exist, most of which interpret plausibility as an expression of uncertainty. The concept is frequently referred to in scenario analysis and emerging risk contexts, which are characterized by large uncertainties. The difficulty of assigning probabilities in such cases has led some to claim that, by offering a purely qualitative approach, plausibility is a more suitable tool for measuring uncertainty. However, a proper clarification of what the plausibility concept means in a risk analysis context is missing; current definitions of the concept do not provide a clear understanding of how plausibility is linked to fundamental aspects of risk and uncertainty. The present paper aims to rectify these issues, by i) reviewing and discussing how the plausibility concept is interpreted and used in the literature, ii) providing a suggested interpretation of the concept in a risk analysis context, and iii) giving our recommendations on how the practical application of the plausibility concept can be enhanced by drawing on contemporary risk science, specifically with regard to highlighting the likelihood and knowledge dimensions of risk. Based on the review, it is shown that the concept of plausibility should be seen as a measure of uncertainty capturing a combination of likelihood and judgments on the supporting knowledge. We conclude that a prudent use of the concept requires that each of these dimensions are addressed explicitly, using imprecise probabilities and strength of knowledge judgments.

近年来，合理性概念在风险分析环境中越来越受到关注。存在许多定义，其中大多数将合理性解释为不确定性的表达。该概念在情景分析和新兴风险环境中经常被提及，其特点是具有很大的不确定性。在这种情况下分配概率的困难导致一些人声称，通过提供纯粹的定性方法，合理性是测量不确定性的更合适的工具。然而，缺乏对风险分析上下文中似真性概念的含义的适当澄清；该概念的当前定义并没有提供对合理性如何与风险和不确定性的基本方面相关联的清晰理解。本文旨在纠正这些问题，通过 i) 审查和讨论如何在文献中解释和使用似真概念，ii) 在风险分析上下文中提供对该概念的建议解释，以及 iii) 就似真概念的实际应用提出我们的建议通过借鉴当代风险科学，特别是在强调风险的可能性和知识维度方面得到加强。根据审查，它表明似是而非的概念应被视为不确定性的度量，它捕获了对支持知识的可能性和判断的组合。我们得出的结论是，谨慎使用该概念需要使用不精确的概率和知识判断强度来明确解决这些维度中的每一个。