An efficient and flexible framework for inferring global sensitivity of agent-based model parameters
Fig 3
Surrogate model (SM) selection for the 3D vascular tumor growth ABM.
A) ABM storyboard showing vascular tumor growth. B) ABM parameters included in the sensitivity analysis. The yellow box highlights local spatial parameters that are not explicitly captured by the surrogate models (SMs). C) Fits of the SMs to ABM output at a representative ABM parameter vector. D) Histograms of log(RSS) values for each SM across all sampled ABM parameter vectors. E) Comparison of Akaike Information Criterion (AIC)-based relative log-likelihoods between the three SMs. Individual ABM parameter vectors are represented as darker colored dots. The x-axis shows the relative log-likelihood of the exponential model, and the y-axis shows the relative log-likelihood of the logistic model, both compared to the von Bertalanffy model. Positive (resp. negative) values indicate that von Bertalanffy is more (resp. less) likely than the alternative SM. The background is color-coded by the SM selected by AIC: yellow indicates preference for von Bertalanffy, red for logistic, and blue for exponential. The ABM parameter vector corresponding to panel C) is highlighted with a black circle. Dashed lines indicate where the log scales change sign. F-H) Identifiability donuts of SM parameters where color indicates the identifiability index, and area the proportion of ABM parameter vectors for which the given SM parameter had that index.