Influence Diagrams Approach (IDA) is a technique used in the field of Human reliability Assessment (HRA), for the purposes of evaluating the probability of a human error occurring throughout the completion of a specific task. From such analyses measures can then be taken to reduce the likelihood of errors occurring within a system and therefore lead to an improvement in the overall levels of safety. There exist three primary reasons for conducting an HRA; error identification, error quantification and error reduction. As there exist a number of techniques used for such purposes, they can be split into one of two classifications; first generation techniques and second generation techniques. First generation techniques work on the basis of the simple dichotomy of ‘fits/doesn’t fit’ in the matching of the error situation in context with related error identification and quantification and second generation techniques are more theory based in their assessment and quantification of errors. ‘HRA techniques have been utilised in a range of industries including healthcare, engineering, nuclear, transportation and business sector; each technique has varying uses within different disciplines. An Influence diagram(ID) is essentially a graphical representation of the probabilistic interdependence between Performance Shaping Factors (PSFs), factors which pose a likelihood of influencing the success or failure of the performance of a task. The approach originates from the field of decision analysis and uses expert judgement in its formulations. It is dependent upon the principal of human reliability and results from the combination of factors such as organisational and individual factors, which in turn combine to provide an overall influence. There exists a chain of influences in which each successive level affects the next. The role of the ID is to depict these influences and the nature of the interrelationships in a more comprehensible format. In this way, the diagram may be used to represent the shared beliefs of a group of experts on the outcome of a particular action and the factors that may or may not influence that outcome. For each of the identified influences quantitative values are calculated, which are then used to derive final Human Error Probability (HEP) estimates.
Background IDA is a decision analysis based framework which is developed through eliciting expert judgement through group workshops. Unlike other first generation HRA, IDA explicitly considers the inter-dependency of operator and organisational PSFs. The IDA approach was first outlined by Howard and Matheson, and then developed specifically for the nuclear industry by Embrey et al. [2].
IDA Methodology The IDA methodology is conducted in a series of 10 steps as follows: 1. Describe all relevant conditioning events Experts who have sufficient knowledge of the situation under evaluation form a group; in depth knowledge is essential for the technique to be used to its optimal potential. The chosen individuals include a range of experts - typically those with first hand experience in the operational context under consideration – such as plant supervisors, reliability assessors, human factor specialists and designers. The group collectively assesses and gradually develops a representation of the most significant influences which will affect the success of the situation. The resultant diagram is useful in that it identifies both immediate and underlying influences of the considered factors with regards their effect on the situation under assessment and upon one another. 2. Refine the target event definition The event which is the basis of the assessment requires to be defined as tightly as possible. 3. Balance of Evidence The next stage is to select a middle-level event in the situation and using each of the bottom level influences, assess the weight of evidence, also known as the ‘balance of evidence’; this represents expert analysis of the likelihood that a specific state of influence or combination of the various influences is existent within the considered situation. 4. Assess the weight of evidence for this middle-level influence, which is conditional on bottom-level influences 5. Repeat 3 and 4 for the remaining middle-level and bottom-level influences These three steps are conducted in the aim of determining the extent to which the influences exist in the process, alone and in different combinations, and their conditional effects. 6. Assess probabilities of target event conditional on middle-level influences 7. Calculate the unconditional probability of target event and unconditional weight of evidence of middle-level influences For the various combinations of influences that have been considered, the experts identify direct estimates of the likelihood of either success or failure. 8. Compare these results to the holistic judgements of HEPs by the assessors. Revise if necessary to reduce discrepancies. At this stage the probabilities derived from the use of the technique are compared to holistic estimates from the experts, which have been derived through an Absolute probability judgement (APJ) process. Discrepancies are discussed and resolved within the group as required. 9. Repeat above steps until assessors are finished refining their judgements The above steps are iterated, in which all experts share opinions, highlight new aspects to the problem and revise the initially made assessments of the situation. The process is deemed complete when all participants reach a consensus that any misgivings about the discrepancies are resolved. 10. Perform sensitivity analyses If individual experts remain to be unsure of the discrepancies about the assessments which have been made, then sensitivity analysis can be used to determine the extent to which individual influence assessments affect the target event HEP. Conducting a cost-benefit analysis is also possible at this stage of the process.
Example The diagram below depicts an influence diagram which can be applied to any human reliability assessment [3].
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