What can mitigate the danger of obtaining poor solutions?

• A. Relying on random seeds
• B. Shortening the validation process
• C. Exercising good judgment in model development and following proper verification and validation steps
• D. Reducing model documentation

Answer: (C)

The important idea here is that avoiding poor solutions comes from disciplined model development and thorough verification and validation. Relying on random seeds only affects reproducibility and the amount of variability you see in results; it does not fix flaws in the model’s structure, assumptions, or data. Shortening the validation process leaves hidden errors and misfits unchecked, so you’re more likely to trust outputs that aren’t actually credible. The best approach is to exercise good judgment in how the model is built and to follow proper verification and validation steps, which ensure the model is implemented correctly (verification) and that it realistically represents the real system and remains credible under uncertainty (validation). Reducing documentation undercuts transparency and auditability, making it harder to trust or reproduce results.