Sensitivity Analysis is an invaluable tool for revealing how uncertainties in input parameters affect decision outcomes.
It helps in assessing the robustness of decisions in the face of uncertainty. Here are some key features of Sensitivity Analysis.
Parameter Variation: Evaluates how changes in input parameters influence the results, often focusing on the most uncertain or variable factors.
Model Robustness: Assesses the stability of a decision's outcome under different scenarios, providing insights into which parameters are critical to the decision.
Risk Assessment: Identifies potential risks associated with uncertain inputs, aiding in better-informed decision-making. Here are the steps in Sensitivity Analysis
-Define the Model: Clearly outline the decision-making model and its parameters.
-Select Parameters: Identify which input parameters are uncertain or expected to vary.
-Determine Ranges: Establish the range of variation for each selected parameter (minimum and maximum values).
-Run Scenarios: Conduct tests by changing one parameter at a time (or multiple parameters) within its defined range.
-Analyze Outcomes: Compare the results to assess how changes in inputs affect the output. This could involve a graphical representation of statistical summaries.
-Interpret Results: Evaluate which parameters have the most significant impact on the outcome, helping prioritize areas for further investigation or risk management.
-Document Findings: Record the insights gained from the analysis, including which inputs are most influential and any implications for decision-making.
Types of Sensitivity Analysis:
-One-Way Sensitivity Analysis: Varies one parameter at a time while keeping others constant to observe the impact on the outcome.
-Multi-Way Sensitivity Analysis: Examines the effects of changing multiple parameters simultaneously.
-Probabilistic Sensitivity Analysis: Use probability distributions for uncertain parameters to assess the likelihood of different outcomes.
-Scenario Analysis: Evaluate different "what-if" scenarios based on specific combinations of parameter values.
Sensitivity Analysis is an invaluable tool for revealing how uncertainties in input parameters affect decision outcomes. By understanding these relationships, decision-makers can better prepare for risks and make more robust and informed choices.
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