@article{ref1,
title="A probabilistic model of belief in safety cases",
journal="Safety science",
year="2021",
author="Nešić, Damir and Nyberg, Mattias and Gallina, Barbara",
volume="138",
number="",
pages="e105187-e105187",
abstract="A safety case is a hierarchical argument supported by evidence, whose scope is defined by contextual information. The goal is to show that the conclusion of such argument, typically &quot;the system is acceptably safe&quot;, is true. However, because the knowledge about systems is always imperfect, the value true cannot be assigned with absolute certainty. Instead, researchers have proposed to assess the belief that a conclusion is true, which should be high for a safe system. Existing methods for belief calculations were shown to suffer from various limitations that lead to unrealistic belief values. This paper presents a novel method, underlined by formal definitions of concepts such as conclusion being true, or context defining the scope. Given these definitions, a general, probabilistic model for the calculation of belief in a conclusion of an arbitrary argument is derived. Because the derived probabilistic model is independent of any safety-case notation, the elements of a commonly used notation are mapped to the formal definitions, and the corresponding probabilistic model is represented as a Bayesian Network to enable large-scale calculations. Finally, the method is applied to scenarios where previous methods produce unrealistic values, and it is shown that the presented method produces belief values as expected.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2021.105187",
url="http://dx.doi.org/10.1016/j.ssci.2021.105187"
}