Which of the following is a technique used in decision analysis?

Sensitivity analysis is a vital technique in decision analysis that evaluates how the variability in the output of a model can be attributed to different variations in the inputs. This helps in understanding which variables have the most influence on outcomes and assists decision-makers in identifying key factors that could impact their choices. By systematically assessing how changes in input parameters may affect decisions, sensitivity analysis becomes an essential tool for risk management and strategic planning.

This technique plays a significant role in decision-making processes, particularly when uncertainty and variable conditions exist, allowing analysts to highlight the robustness or vulnerability of various strategies against fluctuations in data or assumptions. Understanding the implications of different scenarios enables stakeholders to make more informed, data-driven choices, which is fundamental in systems engineering and other complex fields.

The other techniques listed, while valuable in their respective contexts, do not specifically focus on the variability of outcomes based on input changes in the same manner as sensitivity analysis does. This distinction is what makes sensitivity analysis particularly pertinent to decision analysis.