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Qualitative and Quantitative Risk Analysis
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Decision makers can be exposed to many cognitive and motivational factors that can lead to biases in perceptions. This effect is often referred to as selective perception. For example, estimation of a task’s cost can be influenced by the intention to fit the task into the project’s budget. As a result, some of the project parameters can be overestimated. Another type of biases is related to management push for a better project performance. Such management biases may cause underestimation of certain risks.
With so many potential pitfalls in decision-making, is any way to reduce biases and provide more accurate estimation and analysis of project parameters? Many project managers recognize this problem. But in many cases they response is provide very basic project risk management or sometimes don’t bother with risk analysis at all.
Most uncertainties in project management are related to the lack of knowledge about the incoming activities and risk. For such so called epistemic uncertainties, there are two major strategies of performing risk analysis:
1. Properly capture all historical information and use it to make estimation and analysis.
2. Carefully track a project performance including information about risks and uncertainties; update estimation when new information about current project performance become available.
Most real life projects have multiple risks and uncertainties, which affect project different way. In such cases computerized qualitative risk management tool could become the only feasible way not only to manage project uncertainties in current projects, but also to provide input for future projects.
Let’s see how qualitative risk analysis software can help to mitigate negative effect of heuristics and biases.
If risks and uncertainties are registered in comprehensive database, it will help to mitigate availability heuristics. Decision maker will judge about probability of the event’s occurrence based of reliable set of data. In qualitative risk management software each risk has accompanied by the set of standard parameters: severity, impacts, mitigation plans, etc. It helps to mitigate representativeness heuristics, because decision will less likely be influenced by more detailed scenario. If risks are properly registered and updated during the course of the project, it helps to mitigate negative impact of selective perception and management biases. Assessment of risks of future project will be done based on objective analysis of risks in current project. If assessment of risk is done based on objective recorded historical data, the “anchor” for decision making may not be present. It can reduce negative impact of anchoring.
Sets of risks recorded and analyzed in qualitative risk management software, can be a foundation of quantitative risk analysis.
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