The assumption that simulation models' behavior is bounded by their validity domains has several potential limitations:
Incomplete Validity Domains: Validity domains may not capture all possible scenarios or edge cases, leading to behavior outside the specified bounds. This limitation can be addressed by continuously refining and expanding the validity domains based on feedback from simulation results and real-world data.
Modeling Errors: Errors in modeling the validity domains can lead to inaccurate assumptions about the behavior of simulation models. Conducting thorough validation and verification of the validity domains can help identify and rectify modeling errors.
Complex Interactions: Simulation models may exhibit complex interactions that are not fully captured by the validity domains. Addressing this limitation requires a more comprehensive analysis of the interactions between simulation models and their impact on overall system behavior.
Non-Deterministic Behavior: Some simulation models, especially those involving machine learning or probabilistic components, may exhibit non-deterministic behavior that is challenging to bound within validity domains. Techniques such as sensitivity analysis and uncertainty quantification can help address this limitation.
Runtime Variability: Variability in runtime conditions or inputs can cause simulation models to behave differently from what is specified in their validity domains. Implementing robust monitoring and feedback mechanisms during simulation execution can help detect and address such variability.
By acknowledging these limitations and implementing strategies to mitigate them, such as continuous validation, error correction, and comprehensive analysis, the assumption that simulation models' behavior is bounded by their validity domains can be strengthened and made more reliable.