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Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (<jats:underline>S<\/jats:underline>urrogate <jats:underline>Mo<\/jats:underline>deling for <jats:underline>Re<\/jats:underline>capitulating <jats:underline>Glo<\/jats:underline>bal <jats:underline>S<\/jats:underline>ensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method\u2019s flexibility by applying it to two biological ABMs: a simple 2D <jats:italic>in vitro<\/jats:italic> cell proliferation model and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013427","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T18:00:23Z","timestamp":1757354423000},"page":"e1013427","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["An efficient and flexible framework for inferring global sensitivity of agent-based model parameters"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2166-6616","authenticated-orcid":true,"given":"Daniel R.","family":"Bergman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trachette","family":"Jackson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9662-0444","authenticated-orcid":true,"given":"Harsh Vardhan","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kerri-Ann","family":"Norton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"pcbi.1013427.ref001","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.healthplace.2018.08.022","article-title":"Developing agent-based models of complex health behaviour","volume":"54","author":"J Badham","year":"2018","journal-title":"Health Place"},{"issue":"4","key":"pcbi.1013427.ref002","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1002\/wics.1356","article-title":"Agent-based models in sociology","volume":"7","author":"F Bianchi","year":"2015","journal-title":"WIREs Computat Stats"},{"key":"pcbi.1013427.ref003","doi-asserted-by":"crossref","first-page":"7280","DOI":"10.1073\/pnas.082080899","article-title":"Agent-based modeling: Methods and techniques for simulating human systems","author":"E Bonabeau","year":"2002","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"4","key":"pcbi.1013427.ref004","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.tcb.2022.10.006","article-title":"Agent-based methods facilitate integrative science in cancer","volume":"33","author":"J West","year":"2023","journal-title":"Trends Cell Biol"},{"key":"pcbi.1013427.ref005","doi-asserted-by":"crossref","first-page":"1056461","DOI":"10.3389\/fmolb.2022.1056461","article-title":"SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth","volume":"9","author":"HV Jain","year":"2022","journal-title":"Front Mol Biosci"},{"issue":"1","key":"pcbi.1013427.ref006","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s11538-023-01240-6","article-title":"Connecting agent-based models with high-dimensional parameter spaces to multidimensional data using SMoRe ParS: A surrogate modeling approach","volume":"86","author":"DR Bergman","year":"2023","journal-title":"Bull Math Biol"},{"issue":"1","key":"pcbi.1013427.ref007","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s10588-021-09358-5","article-title":"Sensitivity analysis of agent-based models: A new protocol","volume":"28","author":"E Borgonovo","year":"2022","journal-title":"Comput Math Organ Theory"},{"issue":"3","key":"pcbi.1013427.ref008","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1111\/0272-4332.00040","article-title":"Sensitivity analysis for importance assessment","volume":"22","author":"A Saltelli","year":"2002","journal-title":"Risk Anal"},{"key":"pcbi.1013427.ref009","doi-asserted-by":"crossref","unstructured":"Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D. 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