Typical risk assessment processes produce risk estimates by multiplying together single-valued, expected failure frequencies and associated consequences. However, a range of consequences can result from an incident, and a more representative estimate of failure frequency is captured by a distributed variable rather than by a single point value. Risk estimates calculated by typical assessment processes are sometimes referred to as “mean” estimates or “cautious best estimates”. This terminology acknowledges implicitly that there is truly a range of possible values. Meta-risk is a potential approach for analyzing risk that captures this uncertainty by utilizing distributions of failure frequency and consequence in place of point estimates. These distributions are combined to form a risk distribution that can then be used more directly in quantified decision making. Meta-risk improves on the principle of “As low as reasonably practicable” (ALARP) by acknowledging that the levels of uncertainty associated with models used in the risk assessment process are not equal. By providing “probability of exceedance” targets relative to defined risk acceptance criteria, the meta-risk approach allows for quantified decision making that addresses both the level of risk and the associated level of uncertainty. This process allows an analyst to compare risks more accurately from multiple hazards between which levels of uncertainty may vary greatly, and to quantify the benefits of integrity management strategies such as condition monitoring whose primary effect is to reduce uncertainty rather than to reduce risk directly.

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