What is validation in the context of simulation?

• A. Verifying the code runs without errors.
• B. Ensuring the simulation model accurately represents a real system.
• C. Checking that outputs stay within bounds.
• D. Debugging the software.

Answer: (B)

Validation in simulation is about making sure the model is a faithful representation of the real system. It asks whether the way the model is built—the structure, relationships, assumptions, and input data—captures how the actual process behaves, so that the results are credible for decision making. Validation uses real-world data, expert judgment, calibration, and out-of-sample checks to show that the model’s outputs align with what would be observed in the real system and that the scenarios it can predict are plausible. This is different from verification, which focuses on the technical accuracy of the model’s implementation and ensuring it runs without errors. It’s also not just about keeping outputs within plausible bounds or simply debugging the software; those checks don’t by themselves establish that the model truly represents reality. In short, validation asks: does the model accurately reflect the real system’s behavior under the conditions of interest, so its insights and risk estimates are trustworthy?