Bayesian belief networks as a tool for analyzing maritime safety taking into account the human factor

The study addresses cognitive hazards, also referred to as “demons” in the work of M. Endsley, which contribute to reductions in a navigator’s situational awareness during watchkeeping duties on the ship’s bridge. Despite the widespread adoption of advanced information and communication technologies in maritime operations, accidents on transport vessels remain frequent, with human error identified as the primary cause. Such errors often result from lapses in situational awareness triggered by cognitive hazards encountered while monitoring the navigational environment. When these hazards are unrecognized, they compromise the quality of observation, reducing the navigator’s ability to perceive, comprehend, and anticipate the development of the surrounding situation over time. Cognitive hazards are difficult to identify, creating challenges for collecting sufficient data to model human mental and physical states and behavior in various, especially critical, scenarios. To address these challenges, mathematical tools that integrate statistical and expert assessments can be applied. The study proposes Bayesian belief networks to evaluate the impact of cognitive hazards on navigational incidents. This method allows direct assessment of the probability of an incident arising from emerging hazards, facilitating risk analysis, and inverse evaluation of the probabilities of causes for incidents that have already occurred. A Bayesian belief network is presented to model conditional probabilistic relationships between events linking potential cognitive hazards to the navigator’s situational awareness and the immediate causes of incidents, using a grounding scenario as an illustrative example. The network enables comprehensive analysis of how cognitive hazards propagate through decision-making on the bridge and quantifies their contribution to maritime risk, providing practical guidance for improving navigation safety and supporting further research integrating human factors into probabilistic risk assessments in ship operations.

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