Which method is used to validate a simulation model's effectiveness?

Validating a simulation model's effectiveness is crucial in ensuring that the model accurately reflects real-world processes and outcomes. The most reliable method to achieve this is by comparing the outcomes generated by the simulation to actual data from real-world scenarios. This comparison helps to establish the model's predictive validity, which indicates whether the model can provide accurate representations of the system it simulates.

When outcomes from the model align closely with real-world data, it suggests that the underlying assumptions and mechanisms within the model are sound. This method leverages empirical evidence to confirm that the model functions well under conditions that mimic reality, therefore reinforcing the model's credibility and utility for decision-making, forecasting, or planning.

While the other methods like using expert opinions or conducting pilot studies can provide insights or supplementary validation, they do not offer the same level of empirical confirmation that is essential for robust simulation validation. Adjusting parameters arbitrarily lacks a systematic approach and risks undermining the reliability of the model, as it does not rely on evidence or benchmarking against actual outcomes.