Invariant Probabilistic Sensitivity Analysis
提出一种对单调变换不变的度量方法，通过累积分布间的距离衡量模型输出对概率输入的灵敏度，适用于投资分析中的不确定性评估。
In evaluating opportunities, investors wish to identify key sources of uncertainty. We propose a new way to measure how sensitive model outputs are to each probabilistic input (e.g., revenues, growth, idiosyncratic risk parameters). We base our approach on measuring the distance between cumulative distributions (risk profiles) using a metric that is invariant to monotonic transformations. Thus, the sensitivity measure will not vary by alternative specifications of the utility function over the output. To measure separation, we propose using either Kuiper's metric or Kolmogorov–Smirnov's metric. We illustrate the advantages of our proposed sensitivity measure by comparing it with others, most notably, the contribution-to-variance measures. Our measure can be obtained as a by-product of a Monte Carlo simulation. We illustrate our approach in several examples, focusing on investment analysis situations. This paper was accepted by Peter Wakker, decision analysis.