Global sensitivity analysis (GSA) is a recommended step in the use of computer simulation models. GSA quantifies the relative importance of model inputs on outputs (Factor Ranking), identifies inputs that could be fixed, thus simplifying model calibration (Factor Fixing), and pinpointing areas for future data collection (Factor Prioritization). Given the wide variety of GSA methods, choosing between methods can be challenging for non-GSA experts. Issues include workflow steps and complexity, interpretation of GSA outputs, and the degree of similarity between methods in Factor Ranking. We conducted a study of both widely and less commonly used GSA methods applied to three simulators of differing complexity. All methods share common issues around implementation with specification of parameter ranges particularly critical. Similarities in Factor Rankings were generally high based on Kendall's W. Sobol' first order and total sensitivity indices were easy to interpret and informative with regression trees providing additional insight into interactions.

翻译：全局敏感性分析（GSA）是计算机仿真模型使用中推荐的关键步骤。GSA量化模型输入对输出的相对重要性（因子排序），识别可固定的输入以简化模型校准（因子固定），并确定未来数据收集的重点领域（因子优先级）。鉴于GSA方法的多样性，非GSA专家在选择方法时面临挑战，主要问题包括工作流程步骤与复杂性、GSA结果的解读以及不同方法在因子排序上的相似程度。本研究对广泛使用及较少使用的GSA方法进行了系统性考察，将其应用于三个复杂度各异的仿真模型。所有方法在实施过程中均面临共性问题，其中参数范围的设定尤为关键。基于肯德尔和谐系数（Kendall's W）的评估显示，各方法的因子排序具有较高相似性。Sobol'一阶与总敏感性指数易于解读且信息丰富，而回归树方法进一步揭示了交互作用的内在机制。