sensitivity analysis

This study addresses the lack of a systematic framework for identifying critical input variables and conducting sensitivity analysis under uncertainty in complex simulations, particularly in military decision-making contexts. The authors propose a unified sensitivity analysis framework that integrates local and global methods—including variance-based, derivative-based, screening, and uncertainty quantification techniques—and strategically maps these approaches to specific decision objectives such as factor prioritization, fixing, variance reduction, and mapping. Innovatively, the framework introduces a “sensitivity audit” mechanism to enhance traceability of model assumptions and promote responsible model usage. By providing a structured guide for high-dimensional, complex simulation systems, this work significantly improves model interpretability, transparency, and the credibility of decisions derived from such models.

This paper addresses the lack of robust design foundations for sensitivity analysis in finite-population causal inference. Methodologically, it introduces a novel sensitivity analysis framework grounded in the experimental design distribution—first integrating design-based distributions with partial identification theory to construct model-free, non-asymptotic confidence intervals for the average treatment effect (ATE). It further reinterprets the role of randomization in sensitivity analysis and provides a new design-driven rationale for covariate balance checks. Key contributions include: (1) model-free, finite-population inference under heterogeneous treatment effects; (2) robust ATE confidence intervals with clear identification-theoretic interpretation; and (3) empirical validation across three real-world applications, demonstrating reliability and practicality in small-sample and highly heterogeneous settings.

This study addresses non-expert users by systematically evaluating the workflow feasibility and factor ranking consistency of multiple global sensitivity analysis (GSA) methods across simulation models of varying complexity. Method: It integrates Sobol’ first-order and total-effect indices with regression tree analysis, and—novelty—employs Kendall’s W to quantify inter-method ranking similarity; special attention is given to how parameter range specification affects result robustness. Contribution/Results: (1) Major GSA methods exhibit high consistency in factor importance ranking; (2) Sobol’ indices offer both interpretability and information richness, while regression trees effectively detect interaction effects; (3) Parameter range specification is identified as a critical practical determinant of GSA reliability. Collectively, these findings significantly enhance operationality and methodological rationality for non-experts in tasks such as factor screening, freezing, and prioritization.

Model-form uncertainty (MFU)—arising from simplifying modeling assumptions and particularly challenging to quantify during extrapolation—remains a critical, yet poorly addressed, source of epistemic uncertainty in physics-based modeling. Existing approaches heavily rely on calibration data and cannot isolate the independent influence of individual assumptions on predictions. Method: We propose a calibration-free MFU quantification framework that parameterizes modeling assumptions and integrates grouped variance-based sensitivity analysis to explicitly characterize how assumption changes propagate into predictive variance. The method accommodates parameter dependencies and enables assumption importance ranking under extrapolative conditions. Contribution/Results: Experiments demonstrate that our approach effectively identifies the assumptions dominating prediction uncertainty. It provides quantitative guidance for model simplification, verification, and refinement, thereby significantly enhancing the credibility and robustness of complex physics-based models.

This work proposes a gradient-based active learning method to enhance the accuracy of global sensitivity analysis under limited computational budgets. By leveraging the posterior gradient distribution of a Gaussian process surrogate model, the approach introduces a novel acquisition function that explicitly accounts for correlations among partial derivatives, enabling intelligent selection of the most informative input samples within a constrained simulation budget. Compared to existing derivative-based global sensitivity measures (DGSM)-oriented strategies, the proposed method offers a more comprehensive and robust framework. Experimental results on multiple benchmark test functions and a real-world pesticide transport environmental model demonstrate that the method significantly outperforms state-of-the-art approaches, yielding notably improved estimates of both Sobol’ indices and DGSM sensitivity measures.

High-dimensional stochastic agent-based models (ABMs) are notoriously difficult to analyze systematically due to the curse of dimensionality and inherent stochasticity. This work proposes a multi-stage automated exploration framework that first employs model-driven experimental design to identify key variables and partition the parameter space, then leverages machine learning surrogate models to efficiently capture residual nonlinear interaction effects. The approach operates without human intervention, automatically detecting unstable regions within the simulator and enabling robust sensitivity analysis and policy testing. Applied to a predator–prey case study, the framework successfully isolates dominant variables and highly sensitive nonlinear regimes, substantially enhancing the efficiency and reliability of ABM exploration.

This study addresses the lack of systematic understanding of error mechanisms and data selection strategies in estimating global feature effects, such as partial dependence (PD) and accumulated local effects (ALE). It proposes a novel mean squared error decomposition framework that disentangles model bias, estimation bias, model variance, and estimation variance at the estimator level. Through an integrated approach combining bias–variance analysis, probabilistic modeling, and multi-model simulation experiments, the work reveals how these error components interact with model characteristics, sample size, and the choice between training and hold-out data. The findings show that while using training data introduces slight bias, its larger sample size substantially reduces variance; ALE is more sensitive to sample size than PD; and cross-validation–based estimation effectively mitigates variance from overfitted models, offering both theoretical grounding and practical guidance.

This study addresses the lack of intuitive and interpretable methods for quantifying variable influence in existing regression models. To this end, the authors propose Impact Range Assessment (IRA), a novel approach that robustly measures and ranks predictor importance by evaluating the maximum potential change a predictor can induce in the response variable across its entire range of values, relative to the total variation observed in the response. Experiments on both synthetic linear and nonlinear datasets, as well as a real-world particulate matter prediction case, demonstrate that IRA effectively distinguishes relevant from irrelevant variables with consistent and reliable results. By providing a clear, quantitative interpretation of each variable’s contribution, IRA significantly enhances model transparency and trustworthiness.

This study addresses the challenge of reliably estimating treatment effects and interactions in precision medicine clinical trials, where target subgroups often suffer from sparse sample sizes. To overcome this limitation, the authors propose a Bayesian framework that partially borrows information from external data sources—such as retrospective studies or early-phase trials—during both trial design and analysis. The approach assigns fitness-based weights to individual external observations through covariate distribution matching, enabling precise information borrowing. Innovatively integrating covariate matching, individual-level weighting, and Bayesian modeling, the method also incorporates design priors to determine sample size and decision boundaries. Simulation studies demonstrate its superior performance over existing dynamic borrowing strategies across diverse scenarios, yielding substantially improved accuracy in subgroup effect estimation. The framework is successfully illustrated through an application to a gastric cancer clinical trial design.