Validation One way to handle simplifying assumptions is through validation. This involves comparing model predictions with real-world data to assess accuracy. If discrepancies are found, the assumptions may need to be revisited and revised.
Sensitivity Analysis Sensitivity analysis is another valuable tool. It examines how changes in the assumptions affect the model's outcomes. This helps identify which assumptions are most critical and understand the robustness of the model.
Incremental Complexity Starting with simple models and gradually adding complexity can help researchers understand the impact of each assumption. This process allows for a step-by-step validation of the model's components.
