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.)
One can also point out that the opposite (seek the most complex model) is impossible, since you can always add additional irrelevant factors.
I’ve been slowly training one of my friends to use Occam’s Razor by complaining about needless complexity. It’s weird realizing how much of a conscious appreciation it has given me :)
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!
If model X is more simple, it can be more readily falsified and thus waste less.
Not necessarily. There are plenty of cases where models of different complexity have the same predictive power (because they can be shown to be mathematically equivalent). For example, you can use the path integral formalism for QM (complicated), or you can use the Schrodinger equation instead (simpler).
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 :)
Assume that science is about “useful model”
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.)
One can also point out that the opposite (seek the most complex model) is impossible, since you can always add additional irrelevant factors.
I’ve been slowly training one of my friends to use Occam’s Razor by complaining about needless complexity. It’s weird realizing how much of a conscious appreciation it has given me :)
I assume science is about falsifiable models, that are also hopefully more humane than not.
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!
Not necessarily. There are plenty of cases where models of different complexity have the same predictive power (because they can be shown to be mathematically equivalent). For example, you can use the path integral formalism for QM (complicated), or you can use the Schrodinger equation instead (simpler).
You are correct and I was mistaken—thank you! Up vote for you!
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 :)