SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

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 (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 quantificati

SUBMITTER: Bergman DR 

PROVIDER: S-EPMC11429786 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature