How do you check your simulations against reality?

1 Define your objectives

Before you run your simulations, you need to define your objectives clearly and explicitly. What are you trying to achieve with your simulations? What are the questions you want to answer, the hypotheses you want to test, or the criteria you want to evaluate? Your objectives will guide your choice of simulation methods, data sources, parameters, and outputs. They will also help you to determine how to measure the performance and quality of your simulations, and how to compare them with reality.

2 Choose appropriate methods

Depending on your objectives, you may choose different methods for simulating your system. For example, you may use analytical methods, such as equations or algorithms, to model the behavior and interactions of your system elements. Alternatively, you may use empirical methods, such as experiments or observations, to collect data and generate statistics about your system. You may also use hybrid methods, such as agent-based models or Monte Carlo simulations, to combine both analytical and empirical approaches. You should choose the methods that best suit your system complexity, data availability, and computational resources.

3 Validate your data

Your simulations are only as good as the data you use to feed them. Therefore, you need to validate your data sources, and ensure that they are relevant, reliable, and representative of your system. You should check the quality, accuracy, and completeness of your data, and identify any gaps, outliers, or inconsistencies. You should also check the assumptions, limitations, and uncertainties associated with your data, and how they affect your simulation results. You should use multiple data sources, if possible, to cross-validate and triangulate your data.

4 Verify your model

Once you have your data and methods, you need to verify your model, and ensure that it is correctly implemented and executed. You should check the logic, structure, and parameters of your model, and make sure that they are consistent with your objectives and system specifications. You should also check the code, software, and hardware of your simulation, and make sure that they are free of errors, bugs, or glitches. You should use debugging tools, testing techniques, and documentation standards to verify your model.

5 Compare with reality

After you run your simulations, you need to compare them with reality, and evaluate their accuracy and usefulness. You should use validation criteria, such as metrics, indicators, or benchmarks, to measure the degree of correspondence between your simulation outputs and the actual system outcomes. You should also use sensitivity analysis, uncertainty analysis, and risk analysis to assess the robustness, reliability, and variability of your simulations. You should use graphical, numerical, or statistical methods to compare and visualize your simulations and reality.

6 Update and improve

Finally, you need to update and improve your simulations, and incorporate the feedback and learning from your comparison with reality. You should identify the strengths and weaknesses of your simulations, and the gaps and discrepancies between your simulations and reality. You should also identify the opportunities and challenges for improving your simulations, and the trade-offs and constraints that affect your simulation design. You should use iterative, adaptive, and participatory methods to update and improve your simulations.

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?