Freethought & Rationalism Archive

ApostateAbe · 12-25-2011, 08:05 AM

Quote:

Originally Posted by Doug Shaver

Quote:

Originally Posted by ApostateAbe

Reciprocal Expectations is a methodology for making belief decisions of any sort, and I think it is at the heart of any other good methodology for belief decisions.

Epistemologists have been discussing the making of belief decisions for centuries. What shortcomings in the theories they have formulated up to now do you think are addressed by your new terminology?

Sorry, I didn't mean to imply that my formulation is pioneering. I am almost completely certain that at least one other person published the same formulation already (or something similar but better), but I am not so deeply involved in the literature of the philosophy of science that I have come across it. I don't know their shortcomings. I would love to have your opinion on my proposed methodology.

ApostateAbe · 12-25-2011, 09:32 AM

A summary of the "covering law" model is a formulation that is closer to my own. It is summarized at WisdomSupreme.com:

Covering Law Model

Discipline: Philosophy

A model of explanation associated especially with German logician Carl Gustav Hempel (1905-1997), who regarded it as adequate for all types of explanation.

Basically a statement is explained if it is derived from a set of laws together with certain factual statements, as we might explain 'Fido barks' by saying 'All dogs bark and Fido is a dog'.

The laws must be true general statements, and subject to certain restrictions to exclude accidental 'explanations' like 'Fido barks because he is a pet of mine and all my pets (as it happens) bark'.

The laws, however -though general (for example not mentioning particular objects) - need not be universal, and the derivation of the conclusion may be inductive and not deductive; explanations can be statistical or probabilistic as well as 'deductive-nomological'.

Problems concern the scope of the theory, what restrictions must be placed on the relevant general statements, and the relevance of background knowledge.

Source:
C G Hempel, Aspects of Scientific Explanation (1965), ch. 12

This would be my explanatory power criterion, and the "background knowledge" indeed would be relevant, because that would be my plausibility criterion.

mountainman · 12-25-2011, 12:35 PM

Quote:

Originally Posted by ApostateAbe

Reciprocal Expectations is a methodology for making belief decisions of any sort, and I think it is at the heart of any other good methodology for belief decisions.

Reciprocal Expectations is:

For a theory to be most probable, the evidence should expect the theory (plausibility) and the theory should expect the evidence (explanatory power). If, for a given theory, both criteria are fulfilled significantly more than for all competing theories, then the given theory is most probable.

Someone might like to ask Abe how this methodology compares to Bayes Theorem, and specifically whether it contradicts Bayes theorem.

Doug Shaver · 12-26-2011, 04:46 AM

Quote:

Originally Posted by ApostateAbe

I would love to have your opinion on my proposed methodology.

If memory serves, I responded to a similar request in a previous thread where you raised this same topic. Rather than look for that thread in order to link to it, though, I'll just say here that the language in which you describe your methodology is hopelessly confusing.

Doug Shaver · 12-26-2011, 04:48 AM

Quote:

Originally Posted by mountainman

Someone might like to ask Abe how this methodology compares to Bayes Theorem, and specifically whether it contradicts Bayes theorem.

I have no idea what Abe will say, but Bayes' Theorem rules. Whatever contradicts it, is wrong.

ApostateAbe · 12-26-2011, 05:54 AM

Quote:

Originally Posted by Doug Shaver

Quote:

Originally Posted by ApostateAbe

I would love to have your opinion on my proposed methodology.

If memory serves, I responded to a similar request in a previous thread where you raised this same topic. Rather than look for that thread in order to link to it, though, I'll just say here that the language in which you describe your methodology is hopelessly confusing.

OK, I do tend to be confusing. I would be glad to clarify further if you are interested. Or maybe you can instead tell me about Bayes' Theorem as it relates to decisions of history, because as far as I can tell it can be applied only by assuming numerical probability ratios, and it seems a difficult method, though maybe not impossible. I have just never seen it done. I challenged advocates of Bayes' Theorem in another forum to use to determine the probability that "James" in Galatians 1:19 refers to the literal sibling of Jesus, which I take to be the simplest debate concerning the historical Jesus, but the challenge was never met.

tanya · 12-26-2011, 06:18 AM

Quote:

Originally Posted by Doug Shaver

...Bayes' Theorem rules. Whatever contradicts it, is wrong.

Ok, I acknowledge that, I am not certain whether or not Doug is writing, tongue in cheek here. I guess he is poking fun at someone, but I am not sure who, or why, and so, there may be a slender possibility that he is sincere (admittedly improbable, given his propensity for wit....)

Quote:

Originally Posted by Detrano et al, Circulation 69, No. 3, 541-547, 1984

When these patients were divided into low-,intermediate-, and high-probability subgroups according to their pretest probabilities, noninvasive test results analyzed by Bayesian probability analysis appropriately advanced 17 of them by at least one probability subgroup while only seven were moved backward. Of the 76 patients without disease, 34 were appropriately moved into a lower probability subgroup while 10 were incorrectly moved up. We conclude that posttest probabilities calculated from Bayes' theorem more accurately classified patients with and without disease than did pretest probabilities, thus demonstrating the utility of the theorem in this application.

Whether or not "Bayes' Theorem rules", for certain activities known to Doug, when it comes to predicting likelihood of cardiac disease, one clearly will discover information contradicting the prediction gained from performing the Bayes' calculation, as Detrano and colleagues illustrated.

Coronary angiography, not Bayes' theorem, RULES....

ApostateAbe · 12-26-2011, 06:25 AM

Quote:

Originally Posted by tanya

Quote:

Originally Posted by Doug Shaver

...Bayes' Theorem rules. Whatever contradicts it, is wrong.

Ok, I acknowledge that, I am not certain whether or not Doug is writing, tongue in cheek here. I guess he is poking fun at someone, but I am not sure who, or why, and so, there may be a slender possibility that he is sincere (admittedly improbable, given his propensity for wit....)

Quote:

Originally Posted by Detrano et al, Circulation 69, No. 3, 541-547, 1984

When these patients were divided into low-,intermediate-, and high-probability subgroups according to their pretest probabilities, noninvasive test results analyzed by Bayesian probability analysis appropriately advanced 17 of them by at least one probability subgroup while only seven were moved backward. Of the 76 patients without disease, 34 were appropriately moved into a lower probability subgroup while 10 were incorrectly moved up. We conclude that posttest probabilities calculated from Bayes' theorem more accurately classified patients with and without disease than did pretest probabilities, thus demonstrating the utility of the theorem in this application.

Whether or not "Bayes' Theorem rules", for certain activities known to Doug, when it comes to predicting likelihood of cardiac disease, one clearly will discover information contradicting the prediction gained from performing the Bayes' calculation, as Detrano and colleagues illustrated.

Coronary angiography, not Bayes' theorem, RULES....

He wasn't being tongue in cheek. Bayes' Theorem is taken seriously as a good methodology for history by at least one Jesus-minimalist with a PhD whom the members of the forum tend to respect. It has never actually been effectively applied, as far as I am aware, but I think the appeal of it is its promise to correct or remove the subjectivity from the study of history. Richard Carrier claims to have been working on a case for Bayes' Theorem, and it is long overdue.

aa5874 · 12-26-2011, 06:42 AM

Quote:

Originally Posted by Doug Shaver

Quote:

Originally Posted by mountainman

Someone might like to ask Abe how this methodology compares to Bayes Theorem, and specifically whether it contradicts Bayes theorem.

I have no idea what Abe will say, but Bayes' Theorem rules. Whatever contradicts it, is wrong.

Empiricism Rules.

See http://en.wikipedia.org/wiki/Empiricism

Quote:

Empiricism is a theory of knowledge that asserts that knowledge comes only or primarily via sensory experience. One of several views of epistemology, the study of human knowledge, along with rationalism, idealism and historicism, empiricism emphasizes the role of experience and evidence, especially sensory perception, in the formation of ideas, over the notion of innate ideas or traditions.[1]

Empiricism in the philosophy of science emphasizes evidence, especially as discovered in experiments. It is a fundamental part of the scientific method that all hypotheses and theories must be tested against observations of the natural world rather than resting solely on a priori reasoning, intuition, or revelation....

DCHindley · 12-26-2011, 10:33 AM

Abe,

Per this web page:

Quote:

Bayes’ theorem gives you the actual probability of an event given the measured test probabilities. For example, you can:

■Correct for measurement errors. If you know the real probabilities and the chance of a false positive and false negative, you can correct for measurement errors.

■Relate the actual probability to the measured test probability. Bayes’ theorem lets you relate Pr(A|X), the chance that an event A happened given the indicator X, and Pr(X|A), the chance the indicator X happened given that event A occurred. Given mammogram test results and known error rates, you can predict the actual chance of having cancer.

Doesn't it require that we at least know what percentage of times a test correctly predicts or rules out a situation? It basically applies a correction for testing errors. How one can apply this to ancient history is difficult to fathom. We have no good idea of what we do not have preserved from the past, so there is nothing concrete known to which a testing correction can be applied.

Wash of the Hog, I say.

DCH

Quote:

Originally Posted by ApostateAbe

... Bayes' Theorem is taken seriously as a good methodology for history by at least one Jesus-minimalist with a PhD whom the members of the forum tend to respect. It has never actually been effectively applied, as far as I am aware, but I think the appeal of it is its promise to correct or remove the subjectivity from the study of history. Richard Carrier claims to have been working on a case for Bayes' Theorem, and it is long overdue.

12-25-2011, 09:32 AM	#12
ApostateAbe Contributor Join Date: Sep 2002 Location: MT Posts: 10,656	A summary of the "covering law" model is a formulation that is closer to my own. It is summarized at WisdomSupreme.com: Covering Law Model Discipline: Philosophy A model of explanation associated especially with German logician Carl Gustav Hempel (1905-1997), who regarded it as adequate for all types of explanation. Basically a statement is explained if it is derived from a set of laws together with certain factual statements, as we might explain 'Fido barks' by saying 'All dogs bark and Fido is a dog'. The laws must be true general statements, and subject to certain restrictions to exclude accidental 'explanations' like 'Fido barks because he is a pet of mine and all my pets (as it happens) bark'. The laws, however -though general (for example not mentioning particular objects) - need not be universal, and the derivation of the conclusion may be inductive and not deductive; explanations can be statistical or probabilistic as well as 'deductive-nomological'. Problems concern the scope of the theory, what restrictions must be placed on the relevant general statements, and the relevance of background knowledge. Source: C G Hempel, Aspects of Scientific Explanation (1965), ch. 12 This would be my explanatory power criterion, and the "background knowledge" indeed would be relevant, because that would be my plausibility criterion.

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