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ABSTRACT 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 (SurrogateModeling forRecapitulatingGlobalSensitivity), 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 cell proliferation assay 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.
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Barrier island modeling insights from applied global sensitivity analyses
Barrier island models that include marsh and lagoon processes are highly parameterized. To constrain model uncertainty, those desiring to use these models should seek a robust understanding of the parameter sensitivities. In this study, global sensitivity analysis was performed on a long-term barrier island model to yield insights into the modeled barrier-backbarrier system. Given that a variety of global sensitivity analysis methods exist, each one appearing to differ in its implementation, computational burden, and output, three methods (i.e., the Two-Level Full Factorial Method, Morris Method, and Sobol Method) were applied to the model for the purposes of comparison. Key influential parameters (e.g., sea level rise rate, equilibrium/critical barrier width, and reference wind speed) were consistently identified by all three sensitivity analysis methods. Despite the relatively low number of simulations required by the Morris Method, the Two-Level Method computationally outperformed the others, warranting further exploration of the Morris Method’s parallelization structure. These results may be used to help identify parameter constraints and characterize model uncertainty toward more confident predictions and management decisions for coastal barrier systems.
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- PAR ID:
- 10415698
- Date Published:
- Journal Name:
- Coastal Sediments 2023
- Page Range / eLocation ID:
- 2875-2882
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1142/9789811275135_0261
