Most models of how accidents are caused can be traced back to models such as Heinrich's Domino Theory and the Iceberg. What these models imply is that preventing one or more precursors of an incident is sufficient to prevent such incidents. The notion that there is a root cause of an incident is embedded in the idea that incident causation is linear and deterministic, that there are clear sequences of causes going back to a root cause. This thinking has been very successful and its application may be regarded as reducing the number of (potential) accidents by 80%. Most of these accidents are personal and the development and use of the Swiss Cheese model, aimed also at process incidents, has led to a reduction of possibly 80% of the remaining incidents, covering some 96% in total. Such models are still deterministic, but non-linear in their attribution of causation. The remaining 4% of possible incidents, especially complex and major process accidents, unfortunately appears to be much more intractable. The proposal is that these incidents have a causal structure that is both non-linear and non-deterministic, being inherently probabilistic. This has consequences for the management and prevention of such incidents, including the fact that simpler approaches leave them very hard to manage, despite considerable commitment. This paper explains the different models of how accidents are caused, why people may find the newer models difficult to understand, and the consequences for management that may enable us to actually achieve target zero.

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