One principle of this method (See: Universality) is that our methods should be portable to any form of risk. To get the hang of it, it helps to understand how widely flexible it is, for many industries.
Below, you’ll find links that lead into the use of estimation techniques already in use across various industries.
These examples are not quite as comprehensive as what you would see in an industry, and are meant to serve as simplified examples.
With this method, we structure risks as a scenario, which will resemble the examples below. Then, we forecast the values associated with them. If these decisions are important, increase the rigor involved with the process.
An example in Cyber-Security¶
A 16 person panel forecasted the likelihood of a critical vulnerability being exploited in widely deployed software.
- Will a “Critical” Chromium exploit be discovered “in the wild” in September 2018?
% Likelihood of Yes / No:Yes with a likelihood of 1.64%
An example in Nuclear Safety¶
- Will a pump failure result in core damage within the next year?
% Likelihood of Yes / No:Yes with a likelihood of 0.001%
The nuclear industry relies on extensive data gathering to inform estimation methods, and expert opinion is relied on when uncertainty is present.
An example in Environmental Safety¶
- The change in the mortality rate due to Fine Particles (PM2.5) decrease if we pass X regulation.
Credible Interval:Reduction of .001 -.05% with 95% confidence.
The CSB organizes investigations that provide transparency into root causes informing probabilistic risk approaches supported in EPA policies.
An example in Meteorology¶
The United States spends billions on weather forecasting and its associated infrastructure.
- Will the east coast hurricane make landfall near our city before we can evacuate?
% Likelihood of Yes / No:Yes with a likelihood of 50%.
NOAA and other global organizations build weather infrastructure that makes operational forecasting possible.