Is this acutally a bad thing? In both cases, Bob and Sally not only succeeded in their initial goals, but also made some extra progress.
Also, fictional evidence. It is not implausible to imagine a scenario when Bob does all the same things and learns French, German and then fails on e.g. Spanish. The same thing for Sally.
In general, if you have tried some strategy and succeeded, it does make sense to go ahead and try it on other problems (until it finally stops working). If you have invented e.g. a new machine learning method to solve a specific practical problem, the obvious next step is to try to apply it to other problems. If you found a very interesting article in a blog it makes sense to take a look at other articles in it. And so on. A method being successful is an evidence for it being successful in the future / on other sets of problems / etc.
So, I wouldn’t change to change those mistakes into successes, because they weren’t mistakes in the first place. An optimal strategy is not guaranteed to succeed every single time; rather it should have the maximal success probability.
It is not implausible to imagine a scenario when Bob does all the same things and learns French, German and then fails on e.g. Spanish. The same thing for Sally.
A valid point.
If you have invented e.g. a new machine learning method to solve a specific practical problem, the obvious next step is to try to apply it to other problems.
There’s a crucial difference here. Your machine learning method does not get tired, or bored. It does not say “to hell with this, I’ve had enough”.
The stories point out the difference between having a successful method to do something, and having motivation to do it.
Is this acutally a bad thing? In both cases, Bob and Sally not only succeeded in their initial goals, but also made some extra progress.
Also, fictional evidence. It is not implausible to imagine a scenario when Bob does all the same things and learns French, German and then fails on e.g. Spanish. The same thing for Sally.
In general, if you have tried some strategy and succeeded, it does make sense to go ahead and try it on other problems (until it finally stops working). If you have invented e.g. a new machine learning method to solve a specific practical problem, the obvious next step is to try to apply it to other problems. If you found a very interesting article in a blog it makes sense to take a look at other articles in it. And so on. A method being successful is an evidence for it being successful in the future / on other sets of problems / etc.
So, I wouldn’t change to change those mistakes into successes, because they weren’t mistakes in the first place. An optimal strategy is not guaranteed to succeed every single time; rather it should have the maximal success probability.
A valid point.
There’s a crucial difference here. Your machine learning method does not get tired, or bored. It does not say “to hell with this, I’ve had enough”.
The stories point out the difference between having a successful method to do something, and having motivation to do it.