Richard Carrier and The Bayes Craze (part 2)

Part 1 to this article)

Richard Carrier wants to indict all historiography not based upon Bayes Theorem. Carrier reasons that we can think of the past as future in those areas where we don't have the info. (see part 1). The problem is past events can't be quantified as to their probability because they happened. We may not know the outcome and we think of the likelihood or unlikelihood of an event, but the more information we get the less probable it becomes, yet I'm talking about events that happened. Events that we know happened are not probable they are done deals. The future is not like that because future events are always probable until they happen. We can wind up thinking event X is not likely to have happened when it did happen. An example, how likely is it that an eye witness to a great battle would get the date wrong? Yet Josephus did. The problem is what assumptions do we make (with Bayes where to set the prior probability). I think that's the real reason historians never put numbers to historical probability.

I base this upon a discussion with my old professor, Church historian William S. Babcock, although he never used Bayes for history but he was a math major at Brown. He says history is always matter of probability but we don't put numbers top historical probability because there is no way to know what numbers to put. In order to start the process of using Bayes you have to know something definite. In dealing with the past one is not dealing with a true probability but with an unkown which is actually happened. If we find it happened it's not probable. Until we do find that we don't know what assumption to make.There are only certain things for which Bayes can be used.

R. Joseph Hoffmann says:

What is known by people who use Bayes’s theorem to advantage is that there are only certain conditions when it is appropriate to use it. Even those conditions can sound a bit onerous: In general, its use is warranted when a problem warrants its use, e.g. when:

•The sample is partitioned into a set of mutually exclusive events { A1, A2, . . . , An }.
•Within the sample space, there exists an event B, for which P(B) > 0.
•The analytical goal is to compute a conditional probability of the form: P ( Ak | B ).
•You know at least one of the two sets of probabilities described below. •P( Ak ∩ B ) for each Ak
•P( Ak ) and P( B | Ak ) for each Ak ...The key to the right use of Bayes is that it can be useful in calculating conditional probabilities: that is, the probability that event A occurs given that event B has occurred. Normally such probabilities are used to forecast whether an event is likely to occur...

So far, you are thinking, this is the kind of thing you would use for weather, rocket launches, roulette tables and divorces since we tend to think of conditional probability as an event that has not happened but can be predicted to happen, or not happen, based on existing, verifiable occurrences. How can it be useful in determining whether events “actually” transpired in the past, that is, when the sample field itself consists of what has already occurred (or not occurred) and when B is the probability of it having happened? Or how it can be useful in dealing with events claimed to be sui generis since the real world conditions would lack both precedence and context?[1]...

That's where Carrier tries to think of the past like the future."To put it crudely: Not knowing whether something will happen can be treated in the same way as not knowing whether something has happened by jiggering the formula. Managed properly, he is confident that Bayes will sort everything out in short order:"?[2]

Carrier puts it:

If you treat every probability you assign in the Bayesian equation as if it were a syllogism in an argument and defend each premise as sound (as you would for any other syllogism) Bayes’s theorem will solve all the problems that have left [Gerd] Theissen and others confounded when trying to assess questions of historicity. There is really no other method on the table since all the historicity criteria so far have been shown to be flawed to the point of being in effect (or in fact) entirely useless. ).:"?[3]

I may have made this point in part 1, Hoffmann makes it as well: If all the historical works of the past is useless upon what, pray tell, does one set the prior? If t5he data used for Bayes is useless then Bayes is useless as well. More importantly, Hoffmann points out, "Bayes does not generate criteria and method; it depends on them, just as the solution to Marie’s dilemma depends on real world events, not on prophecy."[4]

Hoffmann tells an anecdote that his former student was speaking of Carrierer's assertion thqat Bayes can be used "where no 'real world data and conditions' can be said to apply." The Student remarked, "Is this insistence [Carrier’s] of trying to invoke Bayes’ theorem in such contexts a manifestation of some sort of Math or Physics envy? Or is it due to the fact that forcing mathematics into one’s writings apparently confers on them some form of ‘scientific’ legitimacy?"[5]

At this point Hoffmann backs my assertion that Bayes, when applied to an area of belief for which we have little real data is entirely subjective. It's a matter of guessing and prejudice as to what basic assumptions to make. As he says plug in different values you get different answers. The next point he makes backs up my point about the illusion of technique. That is the point that it's a gimmick, he's trying to lend scientific credibility to his opinions. "Do there exist good reasons to suppose the methods commonly used in different areas that have grown over time are somehow fatally flawed if they are not currently open to some form of mathematization?"(Emphasis mine).[6]

In his review of Carrier's book James McGrath emphasizes applying Bayes to history is just a matter of applying formal logic to historical reasoning."It does not turn history into mathematics, but nor does it guarantee that those who purport to use it will always manage to adequately assess their own blind spots, shortcomings, and clutching at straws in an attempt to reach a desired conclusion."[7]

In one respect I disagree with McGrath. He says that in using Bayes for history, those who do are not being less precise and that Carrier rightly (according to him) points out that numbers lurk behind the assessments of all historians."All probabilities that historians currently use have numbers hidden behind them, as Carrier emphasizes. And so it is useful to be forced to articulate more clearly whether we think that the likelihood is closer to 70% or closer to 90%..."."[8]

There's a lot wrong with this. First, I disagree that they have numbers behind them. What they really have behind them is events that are not probable but have already happened. In trying to attach numbers that can't be there they are merely trying to ad a verification they don't really have and can't have. All the numbers really tell us is what our guesses are. Secondly I don't think we need the pretense of numbers to force u8s to clarify because we choose according to our prejudices so the numbers only serve to reinforce our prejudices. The numbers enable us to rationalize our prejudices and give them the illusion of scientific acuity.

For some reason McGrath tried to be conciliatory to Carrier and it's absurd. He says:

If the internet mythicists of today were to take this advice to heart, they might work on trying to actually publish scholarly works in appropriate venues, instead of complaining about their being dismissed in a manner that Carrier rightly says is appropriate when people without relevant expertise try to bypass peer review and scholarly discussion and promote their ideas directly to a public ill prepared to assess them...."[9]

Carrier has tried to involve some academics in his work but if he has done peer reviewed work in journals I don't see it. I do agree that Carrier has some good pointds but then he ruins it by makes such strident claims. He essentially says he's the nly valid historian. If that's not snearing at peer review I don't know what is. Then I go over to The Secular Outpost to argue with Lowder some more and find someone illustrating the kind of thing I' talking about in the use of Bayes. Those outpost guys are mad for Bayes. The context here is argument over resurrection. One of them says:

Jesus didn't appear to anyone. The dead do not appear. For that matter, many of the followers of Satya Sai Baba (not just some ancient books that claim that the followers claimed that) claim that he appeared to them, etc. - but he didn't; neither did Jesus.One may simply point out that the resurrection did not happen, on account of the astronomically slim prior probability, [emphasis mine] and the lack of any evidence sufficient to overcome that, or even coming close to overcome that, or even to introduce reasonable doubt. The probability that Jesus was never crucified - even if low - is surely much higher than the probability that he resurrected, even if counting all of the evidence or alleged evidence in support of the crucifixion.

I have to wonder if this guy understands the concept of probability. He doesn't understand the concept of miracles. Probability is just odds given the number of chances that a thing will occur. While a miracle is something impossible, that is not supposed to happen; something that left to its own devises nature could never come up with on its own. Miracles have no probability they are impossible. God is either necessary or impossible. God cannot be a maybe God cannot be something that might or might not have existed. If God exists he has to exist and if God does not exist (apologies to Paul Tillich)[10] it's because it's impossible for there to be God. So in setting a prior for God at all one is begging the question. There can't be a probability for something that has to be or that if it is not, cannot be. This just illustrates my point that atheists refuse to study theology or ace the hard issues of theology.

1 R. Joseph Hoffmann, "proving what?," The New Oxonian: Religion and culture for the intellectually impatientPosted on May 29, 2012 Blog, URL:
https://rjosephhoffmann.wordpress.com/2012/05/29/proving-what/ (accessed 10/30/150)

following graduation from Harvard Divinity School and the University of Oxford, R. Joseph Hoffmann was tutor in Greek at Keble College and Senior Scholar at St Cross College, Oxford, and Wissenschaftlicher Assistant in Patristics and Classical Studies at the University of Heidelberg. He's an editor of Sources of the Jesus Tradition, Carrier is a contributor.

2 Ibid.

3 (Carrier, “Bayes Theorem for Beginners,” in Sources of the Jesus Tradition, Amherst NY:Prometheus Books, R. Joseph Hoffman (ed.), 2010 - Religion 107.

4 Hoffmann, Ibid.

5 Ibid.

6 Ibid..

7 James F. McGrath, "Review of Richard C. Carrier, Proving History."August 7, 2012, Exploring Our Matrix, blog, URL:
http://www.patheos.com/blogs/exploringourmatrix/2012/08/review-of-richard-c-carrier-proving-history.html (accessed 10/30/15). 8 Ibid..

9 Ibid.

10 Tillich believed in not using the term "existence" (or exist) in relation to God. Contingent things exist, God is not a thing existing in creation, God is the basis of all that is, being itself. Hartshorne did get him to accept the use of the term exist if it was used metaphorically.
I*'ve written extensively about this on Metacrock's blog, see "God and Being Itself," just one ofr many.