SSRCA: a novel machine learning pipeline to perform sensitivity analysis for agent-based models

TL;DR

The paper introduces SSRCA, a machine learning pipeline that simplifies sensitivity analysis for complex agent-based models, enabling identification of key parameters and output patterns efficiently.

Contribution

SSRCA is a novel machine learning pipeline that streamlines sensitivity analysis for agent-based models, identifying sensitive parameters and output patterns more robustly than traditional methods.

Findings

SSRCA successfully identifies key sensitive parameters in ABMs.

SSRCA reveals common output patterns and their generating parameters.

SSRCA's results are more robust to model descriptor choices than Sobol' Method.

Abstract

Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the Simulate, Summarize, Reduce, Cluster, and Analyze (SSRCA) methodology, a machine-learning based pipeline designed to facilitate SA for ABMs. In particular, SSRCA can achieve the following tasks for ABMS: 1) identify sensitive model parameters, 2) reveal common output model patterns, and 3) determine which input parameter values generate these patterns. We use an example ABM of tumor spheroid growth to showcase how SSRCA identifies four common patterns from the ABM and the parameter regions that generate these outputs. Additionally, we compare the SA results between SSRCA and the popular Sobol' Method and find that SSRCA's identified sensitive parameters are robust to the choice of model descriptors while Sobol's are not. This analysis could streamline data-driven tasks, such as parameter estimation, for ABMs by reducing parameter space. While we highlight these results with an ABM on tumor spheroid formation, the SSRCA Methodology is broadly applicable to biological ABMs.