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Issue 368: 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 our paper, we propose the Simulate, Summarize, Reduce, Cluster, and Analyze (SSRCA) methodology, a machine-learning based pipeline designed to facilitate SA for ABMs. 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. In the image, we portray the growth of several tumor spheroids following 4 common patterns identified by SSRCA from an ABM of tumor spheroid growth.