often makes computational models less accurate. Why are these simplifications made? (1 point)
All initial values entered into the model are simply best guesses or estimates. By including more factors,
more estimates are included and the inaccuracies compound as a result.
Computational models don't need to be accurate in order to be informative.
The effects of the simplifications are often relatively small, the computations may become too complex if all
factors are included, or it isn't possible to know the initial values of all factors well enough to include them
Simplifications actually tend to make models more accurate, not less accurate, because the simpler the
model, the less chance there is for calculation error or other mistakes.
1 answer
The correct reason for making simplifications in computational models is:
The effects of the simplifications are often relatively small, the computations may become too complex if all factors are included, or it isn't possible to know the initial values of all factors well enough to include them.
This approach allows for more manageable computations while still providing useful results, as complicated interactions may not significantly change the overall outcome being studied.
