Options, cost benefit analysis and decision making (Step 3, 4 and 5)

The remaining three steps of the methodology are covered in Chapter Six as follows:

1. A comparison with other safety critical industries’ efforts into NTS research and training needs were conducted. The two main industries investigated were aviation and anaesthetics. Much has already been covered in chapter two. The possibility of the adoption of successful methods within aviation and anaesthesia were explored. 2. A cost benefit analysis was conducted of all the options explored in step (1). This was carried to weigh the costs against benefits of all options. The analysis was carried out by Bayesian Network (BN) and the Decision Tree Model.

3. Based on cost benefit analysis a decision was made as to which option to select.

Based on the results obtained in step two of the methodology and literature review, a need for options for further improvements to deck officers’ NTS training were explored. This was done by the comparision with other safety critical industries’ NTS development in training and assessment and the possibility that their good practices can be adopted for the maritime domain.

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decision tree analysis, cost benefit analysis was conducted to analyse if the benefits were cost effective.

BN and Decision Tree Model

The BN model is a graphical method and graphs have proven to provide an excellent language for communication and discussing dependence and independence relations among problem- domain variables (Kjaerulff and Madsen, 2014: 17). A large and important class of assumptions about dependence and independence relations expressed in factorised representations of joint probability distribution can be represented very compactly in a class of graphs known as Directed Acyclic Graphs (DAGs). Chain graphs are a generalisation of DAGs, capable of representing a broader class of dependence and independence assumption (Fydenberg, 1989; Wermuth and Lauritzen, 1990).

BNs represent a set of random variables and their conditional dependencies through a DAG. BNs are also known as

“Bayesian Belief Networks (BBNs)”, “Belief Networks”, “Causal Probabilistic Networks”, “Causal Nets”, “Graphical Probability Networks”, and “Probabilistic Cause-Effect” models are an emerging modelling approach of artificial intelligence research that aim to provide a decision-support framework for problems involving uncertainty, complexity and probabilistic reasoning (Fenton and Niel, 1999).

BNs were first developed at Stanford University in the 1970s (McCabe et al., 1998). The first book on BNs was published by Pearl in 1988 and since then several other text books have been published (Haddawy, 1999). The first world application of a BN was Munin (Andreassen et al., 1989). Since then, BNs have spread quickly and been used extensively to model many real world problems (Burnell and Horvits, 1995).

Absolutely anything can be modelled by a BN. The approach is based on conceptualising a model domain or system of interest as a graph of connected nodes and linkages. In the graph, nodes represent important domain variables and a link from one node to another represents a dependency relationship between the corresponding variables. Given their network structuring, BNs successfully capture the notation of modularity (i.e. a complex system can be built by

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combining simpler parts). Due to their Bayesian probability formalism, BNs provide a rational technique to combine both subjective (e.g. expert opinion) and qualitative (e.g. monitoring data) information (Das, 2000).

The reasons for choosing BNs can be summarised as follows:

 They are graphical models, capable of displaying relationships clearly and intuitively.  They are directional, thus being capable of representing cause-effect relationships.  They handle uncertainty through the established theory of probability.

 They can be used to represent indirect causation in addition to a direct one.

BN decision trees are valuable techniques, which are used to make a decision from a set of instances (Janssens et al., 2005). In a decision tree there are two types of nodes: decision nodes and leaves. Leaves are the terminal nodes of the tree and they specify the ultimate decision of the tree. The case is routed down the tree according to the values of attributes tested in successive decision nodes and when a leaf is reached, the instance is classified according to the probability distribution over all classification possibilities (ibid).

BNs are used in a number of studies in all domains such as the Hierarchical Bayesian approach for oil spill estimation as in the Deepwater Horizon accident (Yang et al., 2013), a Bayesian petrophysical decision support system for estimation of reservoir composition (Burgers et al., 2010) and a quantitative risk assessment of the offshore drilling operation such as the blowout preventer failure during the Deepwater Horizon rig accident (Skogdalen and Vinnem, 2011).

Limitations

The BN’s decision tree analysis was conducted based on the assumption that if one shipping company’s ship incurs an accident and that ship is a total loss, then what would be the size of that loss to that shipping company and then also if all the officers had the additional NTS training would that accident have been avoided? The example shipping company chosen was

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Costa Cruise Lines and the accident ship was the Costa Concordia which partially sank in 2012 with 32 fatalities. Human error was one of the causes of the accident. This company and the accident were chosen for the research because data was available and the accident was quite recent.