# Estimating the Human Error Probability during Lifeboat Drills
## 2 Methodology  
The Success Likelihood Index Method (SLIM) is considered as one of the most commonly used methods for human reliability analysis in the maritime industry due to insufficient data or limited resources. Furthermore, the SLIM is commonly used in processes that contain task steps in a hierarchical structure [18; 19]. The SLIM was designed by [PERSON] et al. [20; 21], and it is a decision-analytic approach that uses experts to estimate the human error probability that could happen while carrying out certain tasks. It belongs to the family of human reliability analysis (HRA) methods. It is appropriate for use in problems with a scarcity of data related to the performance of a certain task under study due to the fact that it is based on expert opinion [20; 22; 23; 24]. Since very little data, i.e., reports on analyses of lifeboat accidents, are publicly available (perhaps due to the failure to report all maritime accidents), it can be concluded that the SLIM is suitable for the use of finding factors that influence the success of exercises and quantifying the probability of human errors during their execution. In addition, as stated in [23], it has advantages when it is used for \"operational, maintenance and incident analysis for specific tasks or scenarios\". Having this in mind, the SLIM is a methodology that perfectly fits the scope of this study. Therefore, experts need to identify the task(s) to be evaluated first. The method considers that each task to be performed is affected by Performance-Shaping Factors (PSFs), whose weights and ratings define how each of them contributes to the Success Likelihood Index (SLI). PSFs are the factors that, according to experts' elicitation, have the most significant effect on human performance during a specific task's execution and its success (in this case, a lifeboat drill and all operations related to the drill). The weights (Wi) present the relative importance of the PSFs to the overall task (values from 0 to 100), while ratings (Ri) present the effect of each PSF on the performance of each task (values from 0 to 100, where 0 presents the most negative effect on task performance, 100 is the most positive effect, and 50 is a neutral effect) [20]. The SLI is calculated using Equation (1) [20; 21; 22; 23; 24]:  
\[SLI=\sum_{i=1}^{n}W_{i}\times R_{i} \tag{1}\]  
Then, human error probability (HEP) is quantified by using the obtained SLI and calculating the Probability of Success (PoS) (Equation (2)):  
\[\log(PoS)=a\times SLI+b \tag{2}\]  
Then, the PoS must be subtracted from 1 and antilogarithmized. In Equation (2), \(a\) and \(b\) are constants that could be quantified by the given statistical data for the tasks performed (if available, which is not the case for the lifeboat drill accidents) or absolute probability judgments made by experts participating in the study [22]. In the latter case, the experts estimate the PoS of a specific task in the possible best and worst situations (depending on the PSFs). However, the SLIM methodology suffers from certain drawbacks, such as, for example, the total HEP of the specific operation considered being estimated as the aggregation of the human error probabilities of all tasks identified, and ignoring the dependencies among HEPs that occur due to common PSFs [25]. To mitigate the shortcomings of the SLIM, it was decided to use the Bayesian Network-Success Likelihood Index Method (BN-SLIM) [25; 26; 27] for the part of the lifeboat drill that, by utilising the SLIM, is shown to be with the highest probability of human error. A Bayesian network (BN) is a probabilistic directed acyclic graphical model for reasoning under uncertainty. It is composed of nodes representing variables and edges representing causal relationships between the variables. The causal relationships between the variables are quantified by the conditional probability tables (CPTs), making them a powerful reasoning tool. CPTs quantify the conditional relationship of a child node given all possible combinations of the states of its parent nodes.