Limitations of Simulation
Limitations of Simulation
Simulation is a powerful decision-support tool, but it is not a crystal ball. Its results are only as good as its inputs, its assumptions, and the people behind it. Understanding these limitations helps you use simulation responsibly and interpret its results with appropriate confidence.
A model is only as reliable as the data feeding it. Incomplete, outdated, or inaccurate inputs — flight schedules, passenger profiles, process times — produce misleading results, however sophisticated the model.
Every simulation simplifies reality. The assumptions made about behavior, priorities, and boundary conditions shape the outcome, so they must be made transparent and tested rather than hidden
Outcomes rely heavily on the experience of the simulation team. Experts who understand both the subject matter and the tool can interpret data, refine inputs, and build models that hold up under scrutiny.
Well-trained users working with established, purpose-built tools make far fewer errors and produce higher-quality results than ad-hoc models built in general-purpose software.
Simulation informs decisions; it does not make them. Results should support — not replace — expert review and stakeholder discussion.
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None of these limitations are insurmountable. A well-designed simulation tool, backed by strong support, validated models, and trained users, keeps even complex studies manageable and minimizes the risks that come from poor input data or incorrect use. The goal is not a perfect prediction of the future, but a reliable, well-understood basis for better decisions.
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With this glossary we would like to give short, basic explanations and definitions for important and frequently used terms in the fields of Analysis, Runway and Terminal Capacity Assessment and Airport Simulation/Allocation. These are based on our experience of practical application in the industry. We would be happy to provide you with more detailed definitions and further explanations. Please feel free to contact us.
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