You are over-simplifying Bayesian reasoning. Giving partial credence to propositions doesn’t work; numerical values representing partial credence must be attached to the basic conjunctions.
For example, if the propositions are A, B, and C, the idea for coping with incomplete information that every-one has, is to come up with something like P(A)=0.2, P(B)=0.3, P(C)=0.4 This doesn’t work.
One has to work with the conjunctions and come up with something like
P(A and B and C) = 0.1
P(A and B and not C) = 0.1
P(A and not B and C) = 0.1
P(A and not B and not C) = 0.2
P(not A and B and C) = 0.1
P(not A and B and not C) = 0.1
P(not A and not B and C) = 0.1
P(not A and not B and not C) = 0.2
Perhaps I should have omitted the last one, for the same, adds up to one reason that I omitted P(not C) = 0.6. One actually has to work with seven numbers not three.
Ordinarily I would approve of simplifying Bayesian reason in this way; it helps you get to your point quickly. The reason that I criticize it as an over-simplification is that you proceed to talk about fuzzing the propositions in four ways: vagueness, approximation, context-dependence, and sense vs nonsense. Propositions or basic conjunctions?
A big problem with Bayesian reasoning is that the number of basic conjunctions increases exponentially with the number of propositions. This makes Bayesian reason rather impractical. One must resort to various ugly hacks to tame this exponential explosion. I believe that the problem is not actually with Bayesian reasoning, but with having incomplete information. Any attempt to cope with missing information will suffer from this exponential explosion and need hacky fixes.
Maybe you can cope with vagueness, approximation, etc, by fuzzing the propositions, but when you try to accommodate missing information you will have to work with basic conjunctions. If proposition A has category boundaries that are fluid and amorphous in one way, and proposition B has category boundaries that are fluid in a different way, you will need some kind of product structure on fluidity so that you can cope with “A and B” and also “A and not B”, “not A and B”, and finally “not A and not B”. Maybe you can postulate that the fluidity of A is just the same whether B is true or false, but this is basically a hack to try to contain the exponential explosion of the inherent difficulties.
You are over-simplifying Bayesian reasoning. Giving partial credence to propositions doesn’t work; numerical values representing partial credence must be attached to the basic conjunctions.
For example, if the propositions are A, B, and C, the idea for coping with incomplete information that every-one has, is to come up with something like P(A)=0.2, P(B)=0.3, P(C)=0.4 This doesn’t work.
One has to work with the conjunctions and come up with something like
P(A and B and C) = 0.1
P(A and B and not C) = 0.1
P(A and not B and C) = 0.1
P(A and not B and not C) = 0.2
P(not A and B and C) = 0.1
P(not A and B and not C) = 0.1
P(not A and not B and C) = 0.1
P(not A and not B and not C) = 0.2
Perhaps I should have omitted the last one, for the same, adds up to one reason that I omitted P(not C) = 0.6. One actually has to work with seven numbers not three.
Ordinarily I would approve of simplifying Bayesian reason in this way; it helps you get to your point quickly. The reason that I criticize it as an over-simplification is that you proceed to talk about fuzzing the propositions in four ways: vagueness, approximation, context-dependence, and sense vs nonsense. Propositions or basic conjunctions?
A big problem with Bayesian reasoning is that the number of basic conjunctions increases exponentially with the number of propositions. This makes Bayesian reason rather impractical. One must resort to various ugly hacks to tame this exponential explosion. I believe that the problem is not actually with Bayesian reasoning, but with having incomplete information. Any attempt to cope with missing information will suffer from this exponential explosion and need hacky fixes.
Maybe you can cope with vagueness, approximation, etc, by fuzzing the propositions, but when you try to accommodate missing information you will have to work with basic conjunctions. If proposition A has category boundaries that are fluid and amorphous in one way, and proposition B has category boundaries that are fluid in a different way, you will need some kind of product structure on fluidity so that you can cope with “A and B” and also “A and not B”, “not A and B”, and finally “not A and not B”. Maybe you can postulate that the fluidity of A is just the same whether B is true or false, but this is basically a hack to try to contain the exponential explosion of the inherent difficulties.