What can mitigate the danger of obtaining poor solutions in simulation?

• A. Relying on default settings alone.
• B. Increasing the number of random seeds without checks.
• C. Ignoring input data quality.
• D. Exercising good judgment in model development and following proper verification and validation steps.

Answer: (D)

Building trust in Monte Carlo results comes from careful model development paired with a rigorous verification and validation process. Exercising good judgment in how the model is constructed means choosing processes, distributions, dependencies, and input sources that genuinely reflect how the real system behaves, and making explicit the assumptions involved. Verification then asks: did we implement the model right? This involves debugging, code reviews, testing components, ensuring reproducibility, and checking that the numerical procedures converge as intended. Validation asks: does the model behave like the real system? This includes comparing outputs to observed data, calibrating against known results, performing sensitivity analyses, back-testing, and testing across plausible scenarios.

These steps address both structural correctness and real-world relevance. Relying on defaults, adding more random seeds without checks, or ignoring input data quality can all lead to misleading results because they don’t fix underlying model misspecifications or data problems. In short, combining thoughtful model design with thorough verification and validation is what mitigates the danger of obtaining poor simulation solutions.