"Computer models are useful tools in many branches of science. Through models, astronomers envision the nature of the interiors of stars and planets, meteorologists predict the paths of hurricanes, and geophysicists predict the impacts of changing ocean currents.
But models are not the same as realities: they are only simulations of realities. The complexity of many phenomena, such as historical and future climate, challenge simulation. The reliability of computer models depends on the accuracy and completeness of empirical data used as input to the models, on the applicability of equations used, and on their track record of congruence with observational data.
Increasingly, scientists rely on software written by non-scientists. These programs often make simplifying assumptions to save processing power.
Q: How can scientists be sure that all the factors were considered and balanced?
Q: What unknowns were acknowledged? What “unknown unknowns” could increase the error in conclusions?
It is risky to use off-the-shelf computer models without knowing the assumptions used by the programmers.
While computer models can be useful as heuristic tools, they cannot replace observation. Observation is one of the pillars of science that presumably separates it from other forms of knowledge: observe a phenomenon, make a hypothesis to explain the phenomenon, test the hypothesis with measurements. Models are one step removed from this classical Baconian method, leaning on someone else’s understanding of how things work."
While computer models can be useful as heuristic tools, they cannot replace observation. Observation is one of the pillars of science that presumably separates it from other forms of knowledge: observe a phenomenon, make a hypothesis to explain the phenomenon, test the hypothesis with measurements. Models are one step removed from this classical Baconian method, leaning on someone else’s understanding of how things work."
CEH