The idea behind simulation models is that we can use them to make predictions of what might happen in the real world. As we want often want to use the results of these simulations to inform decision making it is important for the results to be accurate and precise. The way we do this is by keeping a careful eye on the errors.
Traditionally people have focused on trying to minimise the error that crops up in the actual simulation itself, the technical name for this is the ‘simulation-estimation error‘.
However there is another important error that is often overlooked and needs to be taken into account as well, the ‘input-uncertainty error‘. This is to do with the uncertainty in the actual values you plug into your simulation to start with.