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Parameterization is also a crucial step in model design. This involves setting initial values for agent attributes and environmental conditions based on real-world data or theoretical assumptions. Accurate parameterization is essential for ensuring that the simulation produces meaningful results. However, parameterization can be challenging when data is limited or uncertain. In such cases, researchers may use sensitivity analysis to identify which parameters have the most significant impact on the model's outcomes.

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