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Deciding with Thresholds: Importance Measures and Value of Information
Author(s) -
Borgonovo Emanuele,
Cillo Alessandra
Publication year - 2017
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12732
Subject(s) - risk analysis (engineering) , metric (unit) , probabilistic logic , probabilistic risk assessment , event (particle physics) , risk management , factor analysis of information risk , risk assessment , computer science , value of information , uncertainty reduction theory , actuarial science , information system , engineering , risk management information systems , operations management , psychology , artificial intelligence , management information systems , business , computer security , physics , electrical engineering , finance , quantum mechanics , communication
Risk‐informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value‐of‐information‐based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.