I am also confused that on LW subjective and objective Baysian probability is not typically divided.
In my—may be incorrect—understanding the subjective Baysian probability is the probability distribution over all my beliefs, like “Typically I am rights in 6 cases from 10, so a priori probability of truth of any my idea is 0.6”. This could be used in cases of logical uncertainty and other unmeasurable situations, like claims about possibility of AI.
Objective Byaesian probability is about some real situation, like the problem “If you meet a person in glasses in a port city, what is more likely: if he librarian or a sailor”. It doesn’t make any assumptions about my believes or bets, but use straightforward calculations using information provided in the example. In this case, the correct answer is sailors, as there are much more sailors in the port city than librarian, and number of sailors in glasses overweights number of librarians in glasses.
In ideally calibrated person both probabilities should converge.
“If you meet a person in glasses in a port city, what is more likely: if he librarian or a sailor” is not a statement about a real situation but a question about an abstract situation a quite narrow set of information is known and a decision was made to model the situation in a certain way.
Further there is someone to do this observing and know that they are seeing a librarian or a sailor. There is no “objective” unless you shove the observer outside the frame of reference so you can pretend to get objectivity.
There’s no evidence of any kind that doesn’t require a subject to reason or observe. That should suggest that “no subjects involved” is too high a bar for objectivity, and in order to have a non-empty set of objective facts, you need some other criterion , such as “multiple subjects who are out of communication are able to converge”.
Perhaps, but I think that kind of use of the word “objective” only makes sense in a context where we can reclaim it from it’s normal meaning. I realize such a thing has happened within Bayesianism, but it causes significant confusion for the uninitiated reader.
I am also confused that on LW subjective and objective Baysian probability is not typically divided.
In my—may be incorrect—understanding the subjective Baysian probability is the probability distribution over all my beliefs, like “Typically I am rights in 6 cases from 10, so a priori probability of truth of any my idea is 0.6”. This could be used in cases of logical uncertainty and other unmeasurable situations, like claims about possibility of AI.
Objective Byaesian probability is about some real situation, like the problem “If you meet a person in glasses in a port city, what is more likely: if he librarian or a sailor”. It doesn’t make any assumptions about my believes or bets, but use straightforward calculations using information provided in the example. In this case, the correct answer is sailors, as there are much more sailors in the port city than librarian, and number of sailors in glasses overweights number of librarians in glasses.
In ideally calibrated person both probabilities should converge.
“If you meet a person in glasses in a port city, what is more likely: if he librarian or a sailor” is not a statement about a real situation but a question about an abstract situation a quite narrow set of information is known and a decision was made to model the situation in a certain way.
Further there is someone to do this observing and know that they are seeing a librarian or a sailor. There is no “objective” unless you shove the observer outside the frame of reference so you can pretend to get objectivity.
There’s no evidence of any kind that doesn’t require a subject to reason or observe. That should suggest that “no subjects involved” is too high a bar for objectivity, and in order to have a non-empty set of objective facts, you need some other criterion , such as “multiple subjects who are out of communication are able to converge”.
Perhaps, but I think that kind of use of the word “objective” only makes sense in a context where we can reclaim it from it’s normal meaning. I realize such a thing has happened within Bayesianism, but it causes significant confusion for the uninitiated reader.
I think it has happened much more widely, and the “normal” meaning is a historical curiosity.