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MindManager and Risk Analysis
Risk and uncertainties are everywhere. In fact, almost all complex natural, business, and technological processes are probabilistic. Deterministic analysis in most cases is just an approximation of complex problems. When we are talking about weather forecasts, project management, financial analysis, geophysical surveys, military planning – all these processes have uncertainties, which require probabilistic methods of the analysis.
Where are these uncertainties are coming from? Uncertainties can be related to the possible variations and random errors in the values of the parameters and their estimates. An example of such uncertainties is geological properties. Another group of uncertainties is related to lack of our knowledge about certain subjects. It most cases, it is lack of knowledge about something, which has not yet occurred. A clear example of this is predicting a stock price.
Managing of uncertainties includes the steps of identification, analysis, and, if required, risk mitigation. Different risk analysis methods and tools are widely available and used in many industries. In most cases, different statistical methodologies are applied to particular subject areas. The question remains, if risk analysis tools are well established and developed, why is the actual use of risk and uncertainty management still limited in many industries?
One of the main issues is identification of uncertainties, which can be a very complex process. If uncertainties are not properly identified, it will lead to incorrect results regardless of what methods of analysis are used. This phenomenon is popularly referred to as “garbage in/ garbage out.” If uncertainty is related to random variations of parameters, it is possible to determine them objectively, for example, through accurate measurement. However, if uncertainties are related to a lack of knowledge, industry experts who base their estimates on historical data or actual measurement during a process can only determine them subjectively.
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