Comparing two sensitivity analysis approaches for two scenarios with a spatially explicit rural agent-based model
Use of complementary sensitivity analysis methods provides additional insight in sensitivities. The one-at-a-time approach provides rapid insight into the importance of parameters. The Monte Carlo analysis reveals parameter interactions and nonlinearities. Parameter sensitivity of agent-based models can be different across different policy scenarios. Sensitivity analysis can be used to identify opportunities for policy leverage.
sensitivity analysis agent-based models social simulation complex systems