Does the previous belief count as a hit or miss for the purposes of meta-certainty?
A miss. I would like to be able to quantify how far off certain predictions are. I mean sometimes you can quantify it but sometimes you can’t. I have previously made a question posts about it that got very little traction so I’m gonna try to solve this philosophical problem myself once I have some more time.
One could also mean that a belief like “probability for world war” could get different odds when asked in the morning, afternoon or night while dice odds get more stable answers.
This could be a possible bias in meta-certainty that could be discovered (but isn’t the concept of meta-certainty itself).
“conviction” could describe it but I think subjective degrees of belief are not supposed point to things like that.
Conviction could be an adequate word for it, but I’ll stick with meta-certainty to avoid confusion. You could rank your meta-certainty in “order of defense”, but I would start out explaining it in the way that I did in my response to ChristianKl.
Well it clarifies that the first of the three kind of directions was intended.
If that is a miss what do hits look like? If I have a belief of 50%, 50% coin at what point can I say that the distribution is “confirmed”. If the true distribution is 49.9999% vs 50.0001% and that counts as a miss that would make almost all beliefs to be misses with hits being rare theorethical possibiliies. So within rounding error all beliefs that reference probablities not 1 or 0 have meta-certainty 0.
Note that in calculating p-values the null hypothesis is not ever delineated a clear miss but there always remains a finite possiblity that noise was the source of the pattern.
I was trying to convey the same problem, although the underlying issue has much broader implications. Apparently johnswentworth is trying to solve a related problem but I’m currently not up to date with his posts so I can’t vouch for the quality. Being able to quantify empirical differences would solve a lot of different philosophical problems in one fell swoop, so that might be something I should look into for my masters degree.
A miss. I would like to be able to quantify how far off certain predictions are. I mean sometimes you can quantify it but sometimes you can’t. I have previously made a question posts about it that got very little traction so I’m gonna try to solve this philosophical problem myself once I have some more time.
This could be a possible bias in meta-certainty that could be discovered (but isn’t the concept of meta-certainty itself).
Conviction could be an adequate word for it, but I’ll stick with meta-certainty to avoid confusion. You could rank your meta-certainty in “order of defense”, but I would start out explaining it in the way that I did in my response to ChristianKl.
Well it clarifies that the first of the three kind of directions was intended.
If that is a miss what do hits look like? If I have a belief of 50%, 50% coin at what point can I say that the distribution is “confirmed”. If the true distribution is 49.9999% vs 50.0001% and that counts as a miss that would make almost all beliefs to be misses with hits being rare theorethical possibiliies. So within rounding error all beliefs that reference probablities not 1 or 0 have meta-certainty 0.
Note that in calculating p-values the null hypothesis is not ever delineated a clear miss but there always remains a finite possiblity that noise was the source of the pattern.
I was trying to convey the same problem, although the underlying issue has much broader implications. Apparently johnswentworth is trying to solve a related problem but I’m currently not up to date with his posts so I can’t vouch for the quality. Being able to quantify empirical differences would solve a lot of different philosophical problems in one fell swoop, so that might be something I should look into for my masters degree.