Assume that science is about "useful model"
I assume science is about falsifiable models, that are also hopefully more humane than not.
If model X and model Y produce equally useful predictions, they have equal value. If model X is simpler, then it gets you more value for your costs (time spent thinking, any exhaustion of "mental muscles", etc.)
Here I think we are saying similar things with different words. If model X is more simple, it can be more readily falsified and thus waste less.
Occam's Razor is so-so for science, but a real treat for everyday use in deciding between otherwise apparently equal choices or avoiding waste. Hats off to Occam's Razor for all the non-science things it does!
I would say it's less a question of "falseness" and more "accuracy" - if Newtonian physics can get you to the moon, it doesn't really matter that Relativity came along later and demonstrated that the model was incomplete. The important thing was that Newtonian physics was the system most likely to correctly answer the question of how to get to the moon at that time.
I emphasize accuracy because sometimes you just can't get 100% - weather prediction does a great job of statistics, but it can't guarantee me that it will rain on Tuesday, and not on Friday.
That said, I'd agree that the ability to measure accuracy (and thus to falsify it) is very important :)
Today's post, Where Recursive Justification Hits Bottom was originally published on 08 July 2008. A summary (taken from the LW wiki):
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