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Benefits of Bayesian Network Models by Christophe Simon, Philippe Weber

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Introduction

I.1. Problem statement

Since the beginning of the 20th Century, our perception of technological artifacts has continued to evolve and ranges from complicated systems to complex systems combining technical, human, organizational and environmental components. The first major accidents, such as the Flixborough blast [DEP 75], the Three Mile Island accident [KEM 79] or the toxic cloud at Seveso [SEV 82] that occurred at the end of the 1980s, have highlighted the role played by humans and their organizations in the failure of technical systems. The analysis of these accidents helps us to understand that the components are not independent. Thus, they have to be considered jointly to assess the true risk presented by our industrial systems.

Nowadays, the technological objects used are taken in their environment and are defined as complex socio-technical systems. The increase in complexity is due to the complexity emerging from the interdependence between technical, human, organizational and environmental components. A system is a set of elements interacting with each other with shared dependencies.

Faced with regulations, industrial systems are now required to have a high level of risk management. This level should be continuously demonstrated and proved [DE 12]. It is also necessary to handle socio-technical systems from a global point of view. To cope with this, dependability analysis and decision-making methods should be improved. Dependability analysis is primarily ...

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