Bergman Daniel R, Jackson Trachette, Jain Harsh Vardhan, Norton Kerri-Ann
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, United States of America.
PLoS Comput Biol. 2025 Sep 8;21(9):e1013427. doi: 10.1371/journal.pcbi.1013427. eCollection 2025 Sep.
Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. 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 (Surrogate Modeling for Recapitulating Global Sensitivity), 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's flexibility by applying it to two biological ABMs: a simple 2D in vitro 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.
基于主体的模型（ABM）已成为模拟复杂生物、生态和社会系统的重要工具，在这些系统中，个体主体之间的相互作用产生了涌现行为。通过全局敏感性分析量化不确定性对于评估ABM预测的稳健性和可靠性至关重要。然而，大多数全局敏感性方法需要大量计算资源，这使得它们对于高度复杂的模型不切实际。在这里，我们引入了SMoRe GloS（用于概括全局敏感性的代理建模），这是一种用于对ABM进行全局敏感性分析的新颖且计算高效的方法。通过利用明确制定的代理模型，SMoRe GloS允许在不牺牲准确性的情况下进行全面的参数空间探索和不确定性量化。我们通过将其应用于两个生物ABM来证明我们方法的灵活性：一个简单的二维体外细胞增殖模型和一个复杂的三维血管肿瘤生长模型。我们的结果表明，SMoRe GloS与更简单的方法（如Morris一次改变一个参数的方法）兼容，也与计算强度更大的基于方差的方法（如eFAST）兼容。SMoRe GloS在每种情况下都准确地恢复了全局敏感性指数，同时实现了大幅加速，在几分钟内完成了分析。相比之下，直接实施eFAST对于复杂的ABM来说需要几天的CPU时间。值得注意的是，我们的方法还估计了代表代理模型中未明确包含的过程的ABM参数的敏感性，进一步提高了其效用。通过使计算成本高昂的模型进行全局敏感性分析变得可行，SMoRe GloS为复杂系统中的不确定性量化开辟了新机会，允许对模型行为进行更深入的探索，从而增加对模型预测的信心。
