Since the focus of this site is QRA–quantitative risk assessment–numbers will play a critical role here. An underlying premise in optimal risk assessment is that all inputs into a risk assessment must be quantified–expressed in numbers. Risk must be understood in terms of frequencies and intensities and that understanding is incomplete without numerical measurements or estimates.
Working with numbers means working with statistics to better understand those numbers. Here we discuss issues related to numbers and statistics commonly used in risk management.
Complexity and intelligent simplification.
Graphic tools–Seduced by Graphics.
PHMSA nat gas, haz liq trans html stats, (coming soon)
Confidence Intervals
Confidence intervals are related to the discussions on uncertainty, conservatism, and bias. As a generic statistical concept, our colleagues at pipeline-risk.com offer this document.
Black Swans
The term was coined in relation to Scottish Enlightenment philosopher David Hume’s work on the problem of induction. During much of the 17th century, an Englishman could seemingly state with confidence, “all swans we have seen are white; therefore all swans are white.” Black swans were discovered in Australia in 1697, exposing inductive logic’s flaw. In his seminal treatise, Black Swans: the Impact of the Highly Improbable, Nassim Taleb characterizes a black swan as any event, positive or negative, that is highly improbable, and results in nonlinear consequences. The black swan is an outlier beyond the realm of expectation; nothing in our past experience convincingly points to its possibility. Human nature being what it is, we concoct an explanation after the fact, and convince ourselves the black swan was predictable.
Tragic incidents like that at San Bruno often result from several improbable factors combining in the worst possible location. We’ve already identified two at San Bruno: 1) the use of several lengths of lower quality pipe containing seam defects, which were still strong enough to last decades, and 2) errors in the GIS that rendered the operator blind to the seamed pipe. A third wildcard was a 2008 sewer replacement project that used a standard pneumatic fracturing technique which likely damaged the nearby suspect pipe. The confluence of these three factors is highly unlikely; the San Bruno incident was unforeseeable. Like stock market crashes, many pipeline accidents are explainable only in hindsight. As with the market, despite predictive risk models, pipeline accidents remain inherently unpredictable. They are black swans.
Taleb emphasizes that black swans are creatures of chance; the best we can hope for is to make ourselves less vulnerable to them. One technique Taleb uses to assess potential outliers is brute force statistical modeling via Monte Carlo simulation. Given a range of potential inputs for an analytical model, Monte Carlo simulation calculates all possible outcomes. Given some notion of the level of error (uncertainty) in the input data, when applied to a pipeline risk model Monte Carlo simulation will output a probability distribution of relative risk values, rather than the single number output by risk models currently in use. More certain input data narrows the probability distribution; less certain input data widens it. Correctly applied to situations like San Bruno, Monte Carlo simulation might tell us we don’t know what we think we know.
A last word of caution: We must consider the impact of the unknown. Like our GIS databases, our risk models are reductionist. They are incapable of addressing factors outside the domain of the model itself. Even if the risk model inputs are well constrained, and the Monte Carlo risk probability distribution narrow, we are still at risk. Black swan events may still emerge from circumstances beyond the scope of our models.
See also
https://sensorsandsystems.com/pipelines-black-swans-and-data-governance/