How do you validate and verify the results from dynamic system modeling tools?

1 Initial Checks

Before diving into complex validation techniques, start with initial checks. Examine your model's output for any obvious errors or inconsistencies. Check if the results align with your expectations based on known behaviors of the system you're modeling. If the output deviates significantly without a clear reason, it could be a sign that there's an error in the model setup or the input data. This step is akin to proofreading your work before submission; it's simple but can catch glaring issues that might otherwise undermine the model's credibility.

3 Sensitivity Analysis

Sensitivity analysis helps you understand how changes in input variables affect your model's output. By systematically varying inputs and observing the results, you can identify which parameters have the most significant impact on the model. This process not only validates the model's behavior under different conditions but also highlights areas where the model may be overly sensitive to certain inputs, which could indicate an issue with the model structure or assumptions.

4 Scenario Testing

Scenario testing involves running your model under different hypothetical situations to observe how it behaves. Create a range of plausible scenarios, both typical and extreme, to challenge your model. This type of testing can reveal whether the model can handle unexpected or rare events without breaking down. It also provides a way to verify that the model's results are consistent with real-world outcomes when similar conditions are encountered.

5 Peer Review

Engage with peers to review your dynamic system model. A fresh set of eyes can often spot issues that you might have missed. Your colleagues can provide critical feedback, suggest additional tests, or even identify assumptions that need re-evaluation. Peer review is a widely accepted practice in many fields for quality assurance, and it's just as valuable in the context of dynamic system modeling.

6 Documentation Review

Finally, ensure that all aspects of your model are well-documented. This includes the initial requirements, model design, assumptions made, data sources, and any transformations applied to the data. A thorough documentation review can often reveal discrepancies between the intended design and the implemented model. Moreover, good documentation is essential for future maintenance, updates, or audits of the model.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?