For any instrumental activity, done to achieve some other end, it makes sense to check that specific examples are in fact achieving the intended end.
Most beliefs may have as their end the refinement of personal decisions. For such beliefs it makes sense not only to check whether they effect your personal experience, but also whether they effect any decisions you might make; beliefs could effect experience without mattering for decisions.
On the other hand, some beliefs may have as their end effecting the experiences or decisions of other creatures, such as in the far future. And you may care about effects that are not experienced by any creatures.
For any instrumental activity, done to achieve some other end, it makes sense to check that specific examples are in fact achieving the intended end.
Only if you have reason to believe your naive pattern matching of expectations to observation isn’t already updating your expectations about instrumental activity.
Otherwise, your ″privileging the hypothesis″ that you are in fact wrong.
It’s kind of like smoothing in machine learning. It will have costs and benefits.
For any instrumental activity, done to achieve some other end, it makes sense to check that specific examples are in fact achieving the intended end.
Most beliefs may have as their end the refinement of personal decisions. For such beliefs it makes sense not only to check whether they effect your personal experience, but also whether they effect any decisions you might make; beliefs could effect experience without mattering for decisions.
On the other hand, some beliefs may have as their end effecting the experiences or decisions of other creatures, such as in the far future. And you may care about effects that are not experienced by any creatures.
Only if you have reason to believe your naive pattern matching of expectations to observation isn’t already updating your expectations about instrumental activity.
Otherwise, your ″privileging the hypothesis″ that you are in fact wrong.
It’s kind of like smoothing in machine learning. It will have costs and benefits.