Beyond Global Importance: A Heterogeneity Index for Sensitivity Analysis
Abstract
Global sensitivity analysis quantifies how strongly inputs influence a model output, however, input importance often varies across the model space. We introduce a heterogeneity index H to quantify this variation. The index compares normalized regional sensitivity profiles using mean pairwise total-variation distance and its values range from zero to one. We test H on nine benchmark functions and examine its robustness to the number of regions, sample size, and sampling strategy. We further demonstrate it using models of wooden-pallet carbon footprint, flood risk, and steel-structure reliability. Across these cases, H reveals changes in sensitivity that global indices obscure, including inputs whose importance changes substantially across the model space and differences between rare behavioral regimes. The index complements global sensitivity analysis by indicating when a single global sensitivity profile is insufficient. An open-source implementation is provided in the simdec package.
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BibTeX
@misc{kozlova2026beyond,
title = {{Beyond Global Importance: A Heterogeneity Index for Sensitivity Analysis}},
author = {Mariia Kozlova and Justus Helo and Pamphile T. Roy and Julian Scott Yeomans and Andrea Saltelli},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.31008},
eprint = {2610.31008},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.31008},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Global Importance: A Heterogeneity Index for Sensitivity Analysis
AU - Mariia Kozlova
AU - Justus Helo
AU - Pamphile T. Roy
AU - Julian Scott Yeomans
AU - Andrea Saltelli
PY - 2026
DA - 2026/10/24
PB - arXiv
DO - 10.48550/arxiv.2610.31008
UR - https://arxiv.org/abs/2610.31008
AB - Global sensitivity analysis quantifies how strongly inputs influence a model output, however, input importance often varies across the model space. We introduce a heterogeneity index H to quantify this variation. The index compares normalized regional sensitivity profiles using mean pairwise total-variation distance and its values range from zero to one. We test H on nine benchmark functions and examine its robustness to the number of regions, sample size, and sampling strategy. We further demonstrate it using models of wooden-pallet carbon footprint, flood risk, and steel-structure reliability. Across these cases, H reveals changes in sensitivity that global indices obscure, including inputs whose importance changes substantially across the model space and differences between rare behavioral regimes. The index complements global sensitivity analysis by indicating when a single global sensitivity profile is insufficient. An open-source implementation is provided in the simdec package.
ER -