Brief method description Sample Clauses

Brief method description. The method from Deliverable 2.1 is here shortly recapitulated, using the key concepts and the conceptual model described in the previous sections. The purpose of the method is to enable systematic descriptions of key characteristics of cascading effects in past events. These descriptive accounts can then be used to analyse cascading effects, both by analysing individual events involving cascading effects and by analysing general patterns across different events. The results from such analyses (however outside the scope of Task 2.1-2.3) can then be used and integrated for decision support in the Incident Evolution Tool (Task 4.2). The method was developed with the aim to be useful for describing cascading effects among a broad variety of societal sectors and critical infrastructures. Hence, a more holistic approach has been taken rather than trying to capture small- scale cascading effects within a system or network, such as those occurring within electric power systems (see e.g. Zio and Sansavini, 2011) or domino effects within an industrial premise (Reniers and Cozzani, 2013). Furthermore, the aim of the method is to capture both actual cascading effects, where an impact on a dependent system has taken place, and potential cascading effects, where a dependency exists between a dependent system and an originating system but where an impact on the dependent system did not occur due to some conditions. The reason for including the latter is that valuable learning opportunities with respect to “near misses” should be included. Finally, the method should be able to capture the effects of conditions which represent circumstances that affect how the cascading effect develops. This can be in terms of enabling or aggravating a cascading effect, for example if a transportation incident happens to occur at rush hours instead of at night time. It can also be in terms of preventing or mitigating the cascading effect, such as if a response organisation would have more resources available than usual. These types of conditions play an important role as they enable the extrapolation and generalization of the findings from a one context to another, important in order for knowledge of past events to be useful as decision support in future events.