Multi-way Sensitivity Analysis

Multi-way sensitivity analysis is a technique used to explore the impact of uncertainty in multiple parameters within a model, acknowledging that the model output is sensitive to the values chosen for these key parameters. In this analysis, the values of multiple parameters are varied simultaneously to assess their combined effect on the model’s outcomes.

Key features of multi-way sensitivity analysis include:

– Simultaneous Parameter Variation: Changing the values of several parameters at the same time to understand their joint impact on the model output.

– Impact Investigation: Evaluating how these simultaneous changes influence the results, providing insights into the robustness and reliability of the model.

– Parameter Limitation: Typically, this technique is limited to varying relatively few parameters (usually between 2 to 5) simultaneously. This is because the number of possible combinations of parameter values can become exceedingly large and complex to manage.

Multi-way sensitivity analysis is particularly useful in scenarios where multiple factors interact and influence the outcome, allowing for a more comprehensive understanding of the model’s behavior under different conditions. This approach helps identify critical parameters that significantly affect the results, aiding in more informed decision-making and better risk management.


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