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by throwawaygh
2134 days ago
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> The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges. This is true of simulation in general, not just data-drive models. E.g., a lot of applied mathematics uses PDE models that don't have closed-form solutions and so you just run a ton of simulations sweeping a parameter space. > For example, a computer model of the movement of people within a confined space who are fleeing from a threat--they are in a panic--shows that putting a column about one meter in front of an exit door, slightly to either side, actually increases the flow of people out the door. The crux of this type of science is that you don't know whether the computer simulations are telling you anything about reality. You just have to run real-world experiments and see what happens. And even if the experiment turns out to work, you still don't know for sure that your model was reasonable. |
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One decent place to start is this National Academies report [1] on Verification, Validation, and Uncertainty Quantification.
Verification = did you implement the math correctly in the computer;
Validation = does the implemented mathematical model compare against the real system in controlled experimentss
Uncertainty Quantification = analysis and prediction of the accuracy of the model approximation.
This work was given a big push by the nuclear test ban treaty - you have to really validate the model predictions in this case.
[1] https://www.nap.edu/catalog/13395/assessing-the-reliability-...