Titlebook: Risk Analysis of Complex and Uncertain Systems; Louis Anthony Cox Book 2009 Springer-Verlag US 2009 Risk Assessment.Risk Management.causal

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annexation

Limitations of Risk Assessment Using Risk MatricesSuch matrices have become very popular in applications as diverse as terrorism risk analysis, highway construction project management, office building risk analysis, climate change risk management, and enterprise risk management (ERM). Their use is now so widespread in important applications that it

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Limitations of Quantitative Risk Assessment Using Aggregate Exposure and Risk Modelsthe classification system. Do other methods necessarily do better? This chapter shows that careless use of quantitative risk assessment concepts can also lead to worse-than-useless risk comparisons and recommendations. This happens if causal drivers of risk (such as age-specific failure rates, detai

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Identifying Nonlinear Causal Relations in Large Data Setsering reliability data sets. The causal relations to be discovered may be completely unknown initially; thus, successfully identifying them from data is sometimes called .. This is usually more challenging than merely estimating the parameters of a statistical model that is known or specified a prio

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Overcoming Preconceptions and Confirmation Biases Using Data Mininglations in data sets, even if the relations are unknown a priori and involve nonlinearities and high-order interactions. Chapter 6 showed that information theory provided one possible common framework and set of principles for applying these methods to support causal inferences. This chapter examine

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Determining What Can Be Predicted: Identifiability Modern simulation modeling software environments (such as MATLAB/SIMULINK., or STELLA/ITHINK. for continuous simulation, and SIMUL8. for discrete-event simulation) make the mechanics of simulation model building and use relatively straightforward. Stochastic simulation risk models have been develop