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My review of Richard Carrier's "On the Historicity of Jesus"

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  • My review of Richard Carrier's "On the Historicity of Jesus"

    I've posted a fairly comprehensive review of Richard Carrier's book "On the Historicity of Jesus" here:
    members.optusnet.com.au/gakuseidon/Carrier_OHJ_Review.html

    Any comments welcomed!

    Also, I'm looking for positive reviews of Carrier's "Proving History", where he describes using Bayes's Theory for questions of history. But the reviews should be by mathematicians, or at least someone with experience in using it. I've added some negative reviews into my OHJ review, since there appears to be issues around using BT for history. But I'd like to balance that by adding positive reviews. In fact, any positive review around using BT for questions of history, related to Carrier's work or not, would be good.

    I know very little about BT, but I think it would be great if we could use it for history!

  • #2
    I'll give it a read! While I haven't yet done a full review of Carrier's book, you may be interested in my thoughts on his assertion that there was a Pre-Christian Jewish belief in a cosmic being named Jesus.

    As for Bayes' Theorem, I generally agree with the first review you quote on that subject. Carrier is playing fast and loose with his probabilities, then pretending that they prove something incontrovertibly. That's not simply reflective of a lack of rigor; it's mathematically irresponsible and logically fallacious.
    "[Mathematics] is the revealer of every genuine truth, for it knows every hidden secret, and bears the key to every subtlety of letters; whoever, then, has the effrontery to pursue physics while neglecting mathematics should know from the start he will never make his entry through the portals of wisdom."
    --Thomas Bradwardine, De Continuo (c. 1325)

    Comment


    • #3
      Originally posted by Boxing Pythagoras View Post
      Carrier is . . . pretending that they prove something incontrovertibly.
      How could he be pretending to do that, when he explicitly denies any claim of incontrovertibility?

      Comment


      • #4
        Originally posted by Boxing Pythagoras View Post
        I'll give it a read! While I haven't yet done a full review of Carrier's book, you may be interested in my thoughts on his assertion that there was a Pre-Christian Jewish belief in a cosmic being named Jesus.
        Thanks BP. I read your webpage, and I agree with your thoughts there. Carrier does write about "Philo's Jesus" in OHJ, but I decided to not cover it in my review.

        Originally posted by Boxing Pythagoras View Post
        As for Bayes' Theorem, I generally agree with the first review you quote on that subject. Carrier is playing fast and loose with his probabilities, then pretending that they prove something incontrovertibly. That's not simply reflective of a lack of rigor; it's mathematically irresponsible and logically fallacious.
        I'm not an expert on BT, so I'd like to understand how Carrier's use is mathematically irreseponsible. Do you have any thoughts on what would be a mathematically responsible use of BT for questions of history?

        I might need to raise this on a board specializing in BT questions, if there is one. But my concerns are:
        (1) Can we use Bayes's Theorem for some questions of history, like the existence of Julius Caesar, whose existence is unanimously agreed upon? If so, what are the rules around its use for questions of history?
        (2) If we can't use Bayes's Theorem for other questions of history (like the historicity of Jesus), does that not suggest that there is not good enough data to make any conclusion on those questions?

        Comment


        • #5
          It's just another example of social scientists using equations to make their work look more rigorous.

          I've posted elsewhere on these forums about the subjectivity of the numbers chosen. It's been a while so I can't remember if I posted about errors or uncertainty, but the guy you quote puts it well:

          Look back at the first or second figure, it shows that, if P(E|H) and P(E|~H) are both small, then the range will be very badly behaved. In other words, if the evidence is genuinely unusual, in both cases, then we’ve got a problem. So it isn’t just as trivial as saying “let’s use a conservative value of X”, because behind that value, may be a big change in the output.

          This is a problem because, almost by definition, when dealing with events such as the founding of major religions, or the possibility of a human being having a divine parent, or the likelihood of a resurrection, we’re dealing with insanely small probabilities. Exactly the times when Bayes’s Theorem isn’t well behaved.

          If P(E|~H) is high, and P(E|H) is low, then things behave quite well. But if we want to be conservative (Carrier’s ‘a fortiori’ method) about P(E|~H), say, and allow the possibility that it is small, then the errors can swamp any useful conclusions.

          Comment


          • #6
            Originally posted by Paprika View Post
            It's just another example of social scientists using equations to make their work look more rigorous.

            I've posted elsewhere on these forums about the subjectivity of the numbers chosen.
            I'm not overly concerned about the subjectivity of the numbers, to be honest. To me, they are a necessary evil and are more a guide to get the conversation started.

            Originally posted by Paprika View Post
            It's been a while so I can't remember if I posted about errors or uncertainty, but the guy you quote puts it well:

            Look back at the first or second figure, it shows that, if P(E|H) and P(E|~H) are both small, then the range will be very badly behaved. In other words, if the evidence is genuinely unusual, in both cases, then we’ve got a problem. So it isn’t just as trivial as saying “let’s use a conservative value of X”, because behind that value, may be a big change in the output.

            This is a problem because, almost by definition, when dealing with events such as the founding of major religions, or the possibility of a human being having a divine parent, or the likelihood of a resurrection, we’re dealing with insanely small probabilities. Exactly the times when Bayes’s Theorem isn’t well behaved.

            If P(E|~H) is high, and P(E|H) is low, then things behave quite well. But if we want to be conservative (Carrier’s ‘a fortiori’ method) about P(E|~H), say, and allow the possibility that it is small, then the errors can swamp any useful conclusions.
            Yes, that does seem to be the consensus of the reviews of Carrier's "Proving History" from those who know something about BT, unfortunately. I'd like to see someone make the case that BT can be used for questions of history, or at least if there are rules about what can be used.

            Comment


            • #7
              Originally posted by GakuseiDon View Post
              I'm not overly concerned about the subjectivity of the numbers, to be honest. To me, they are a necessary evil and are more a guide to get the conversation started.
              The means cannot justify the ends (get the conservation started) in this case. The are not necessary nor useful.

              Yes, that does seem to be the consensus of the reviews of Carrier's "Proving History" from those who know something about BT, unfortunately. I'd like to see someone make the case that BT can be used for questions of history, or at least if there are rules about what can be used.
              Standard methodology of academic history without the religious bias of trying to prove what one believes. There are a number of sources available. Historians, in general, do not try and 'prove' anything about history.
              Last edited by shunyadragon; 05-23-2015, 03:32 PM.
              Glendower: I can call spirits from the vasty deep.
              Hotspur: Why, so can I, or so can any man;
              But will they come when you do call for them? Shakespeare’s Henry IV, Part 1, Act III:

              go with the flow the river knows . . .

              Frank

              I do not know, therefore everything is in pencil.

              Comment


              • #8
                Originally posted by GakuseiDon View Post
                I know very little about BT, but I think it would be great if we could use it for history!
                I have only a cursory understanding of BT, but that leads me to think it cannot be properly used for history, and I had a length discussion with someone before the forum crash on that subject, so I too would be interested to know more about this subject.
                My Blog: http://oncreationism.blogspot.co.uk/

                Comment


                • #9
                  As far as I'm aware the only one who's seriously defended Bayes' Theorem here in AD (After Dizzle) times is Doug Shaver, regarding the probability that the events described in the Trial of Jesus episodes from the four Gospels occurred. That discussion began at around this post.

                  Comment


                  • #10
                    Originally posted by GakuseiDon View Post
                    I'd like to see someone make the case that BT can be used for questions of history, or at least if there are rules about what can be used.
                    It depends on what you mean by "questions of history." If you mean questions like "What happened?" or "Did such-and-such-happen?" then Bayes won't give you an answer, and nobody should be saying it will.

                    But given a claim that a certain body of evidence justifies believing that a certain event happened, the question Bayes will answer is: "Is that claim itself justified?"

                    Comment


                    • #11
                      Originally posted by Doug Shaver View Post
                      It depends on what you mean by "questions of history." If you mean questions like "What happened?" or "Did such-and-such-happen?" then Bayes won't give you an answer, and nobody should be saying it will.

                      But given a claim that a certain body of evidence justifies believing that a certain event happened, the question Bayes will answer is: "Is that claim itself justified?"
                      Thanks Doug. My interest is whether there are any rules defined around the quantity or quality of the body of evidence for a claim before Bayes should be used to answer that claim.

                      Also, I'm interested in the proposition: if we can't use Bayes because the evidence for a claim is so weak, then maybe it means the claim cannot be evaluated at all (i.e. if Bayes doesn't work for a historical Jesus because the evidence is not high enough quality, then agnosticism on the question is the most valid solution.)

                      Probably best if I take this to a Bayes board, if there is one, since those questions aren't directly relevant to history, but to the limitations of Bayes.

                      Comment


                      • #12
                        Originally posted by GakuseiDon View Post
                        Thanks Doug. My interest is whether there are any rules defined around the quantity or quality of the body of evidence for a claim before Bayes should be used to answer that claim.
                        The quantity issue seems straightforward to me: You can't reach a final conclusion until you have examined all the evidence that you're aware of. If you discover more evidence after running the numbers, then you have to run the numbers again.

                        After for quality, that translates to the conditional probabilities P(E|H) and P(E|~H). If you get, approximately, P(E|H) = P(E|~H), then that's poor evidence because it doesn't prove much of anything about the hypothesis. If you get P(E|H) > P(E|~H) by a significant amount then the evidence supports the hypothesis, and if P(E|H) < P(E|~H), then the evidence is contrary to the hypothesis.

                        As it turns out, no matter what prior probability you assign the hypothesis (P[H]), the consequent probability P(H|E) will be the same, higher, or lower depending on whether P(E|H) = P(E|~H), P(E|H) > P(E|~H), or P(E|H) < P(E|~H), and in the latter two cases the change will depend on how much greater or less. If P(H) happens to be quite low, then you need a P(E|H) to be much larger than P(E|~H) in order to get a consequent P(H|E) that is well above 0.5. That represents, I believe, the (only) proper interpretation of "Extraordinary claims require extraordinary evidence."

                        Originally posted by GakuseiDon View Post
                        Also, I'm interested in the proposition: if we can't use Bayes because the evidence for a claim is so weak, then maybe it means the claim cannot be evaluated at all (i.e. if Bayes doesn't work for a historical Jesus because the evidence is not high enough quality, then agnosticism on the question is the most valid solution.)
                        Evidence is insufficient if it produces a consequent probability that is about the same as the prior probability. Agnosticism would be justified in such a case if the prior probability is close to 0.5. Otherwise, your justified belief is whatever the prior probability indicates. If it's high enough, then you should believe the hypothesis, and if it's low enough, you should think it's false.
                        Last edited by Doug Shaver; 05-29-2015, 11:09 PM.

                        Comment


                        • #13
                          Originally posted by Doug Shaver View Post
                          The quantity issue seems straightforward to me: You can't reach a final conclusion until you have examined all the evidence that you're aware of. If you discover more evidence after running the numbers, then you have to run the numbers again.
                          That's true, you plug in the numbers after examining all the evidence, but in real life terms how much is enough evidence? That's what I'd like to understand from someone who uses Bayes in real life situations.

                          Originally posted by Doug Shaver View Post
                          After for quality, that translates to the conditional probabilities P(E|H) and P(E|~H). If you get, approximately, P(E|H) = P(E|~H), then that's poor evidence because it doesn't prove much of anything about the hypothesis. If you get P(E|H) > P(E|~H) by a significant amount then the evidence supports the hypothesis, and if P(E|H) < P(E|~H), then the evidence is contrary to the hypothesis.
                          Again, true enough, but what about in real life terms? For the evidence from the epistles, Carrier has P(E|H) vary from (best case) 3-to-1 to (worst case) 3-to-50. That's a variation of 5000%! Can data with such variation be used in Bayes? Again, that's what I'd like to understand from someone who uses Bayes in real life situations.

                          In Section 2.1 of my review, I give some quotes from reviews of Carrier's "Proving History" on how he uses it, here:
                          http://members.optusnet.com.au/gakus...tml#Section2.1

                          One reviewer wrote:
                          So, what can we learn?

                          Well, for one, the inputs to Bayes’s Theorem matter. Particularly small inputs. When we’re dealing with rare evidence for rare events, then small errors in the inputs can end up giving a huge range of outputs, enough of a range that there is no usable information to be had.

                          And those errors come from many sources, and are difficult to quantify. It is tempting to think of errors only in terms of the data acquisition error, and to ignore errors of choice and errors of reference class.

                          These issues combine to make it very difficult to make any sensible conclusions from Bayes’s Theorem in areas where probabilities are small, data is low quality, possible reference classes abound, and statements are vague. In areas like history, for example

                          When Carrier produces a difference of 50 times in the data range for the same piece of evidence, then I take notice about "small errors in the inputs can end up giving a huge range of outputs, enough of a range that there is no usable information to be had". But I think it really needs an expert in the practical applications of Bayes to give a verdict on this.

                          Comment


                          • #14
                            Originally posted by GakuseiDon View Post
                            but in real life terms how much is enough evidence? That's what I'd like to understand from someone who uses Bayes in real life situations.
                            Good question, but it has nothing to do in particular with Bayes Theorem. No matter what method you use to answer a historical question, if your method includes evidence, you have to ask when you've got enough. The only answer that makes any sense to me is: You can't use evidence that you don't have, but you've got to use all the evidence that you do have. There is no method that will guarantee a correct answer. The most we can hope for is an answer that is logically consistent with everything else that we think we know about the real world.

                            Originally posted by GakuseiDon View Post
                            For the evidence from the epistles, Carrier has P(E|H) vary from (best case) 3-to-1 to (worst case) 3-to-50. That's a variation of 5000%! Can data with such variation be used in Bayes?
                            Sure it can. Presumably, you have some opinion as to where in that range P(E|H) ought to go, and hopefully, you have some way to defend that opinion. If so, then you have some justification for whatever consequent probability you derive by using that value.

                            Let's suppose that you favor the best-case estimate and I favor the worst-case estimate. We're going to get very different consequent probabilities P(H|E), obviously. What Bayes has just told us is one particular source of our disagreement about the likelihood of Jesus' existence. In that case, if we wish to try to resolve our disagreement, we know where we need to focus our discussion. We need to be discussing our reasons for assigning those particular probabilities to the epistolary evidence.

                            Originally posted by Irreducible Complexity
                            When we’re dealing with rare evidence for rare events, then small errors in the inputs can end up giving a huge range of outputs, enough of a range that there is no usable information to be had.
                            Nonsense. The information that we don't have sufficient evidence, or good enough evidence, to justify any conclusion is not useless. Neither is it useless to discover the reasons why we disagree about what the evidence actually proves beyond reasonable doubt. As Carrier has noted on several occasions, Bayes forces us, if we use it in good faith, to confront the assumptions we bring to these debates.

                            Originally posted by Irreducible Complexity
                            And those errors come from many sources, and are difficult to quantify.
                            And therefore, what? We should pretend they don't exist? If, in attempting to use Bayes, we are forced to admit that we don't know as much as we thought we knew, that is A Good Thing.

                            Originally posted by Irreducible Complexity
                            It is tempting to think of errors only in terms of the data acquisition error, and to ignore errors of choice and errors of reference class.
                            Yeah, we're all tempted, and always tempted, to do things we shouldn't do. If Bayes forces us to remember this, then good for Bayes.


                            Originally posted by Irreducible Complexity
                            These issues combine to make it very difficult to make any sensible conclusions from Bayes’s Theorem in areas where probabilities are small, data is low quality, possible reference classes abound, and statements are vague.
                            Yes, very difficult. Very difficult indeed. That's just too bad for people who prefer to do their history the easy way.

                            Comment


                            • #15
                              Originally posted by Doug Shaver View Post
                              Sure it can. Presumably, you have some opinion as to where in that range P(E|H) ought to go, and hopefully, you have some way to defend that opinion. If so, then you have some justification for whatever consequent probability you derive by using that value.

                              Let's suppose that you favor the best-case estimate and I favor the worst-case estimate. We're going to get very different consequent probabilities P(H|E), obviously. What Bayes has just told us is one particular source of our disagreement about the likelihood of Jesus' existence. In that case, if we wish to try to resolve our disagreement, we know where we need to focus our discussion. We need to be discussing our reasons for assigning those particular probabilities to the epistolary evidence.
                              Yep, and that's why I'd dearly love to see Bayes's Theorem as a useable tool, because it really makes us really spell out what evidence we are using and the weight assigned to it.

                              But I still have concerns over how to ensure that data collected can be used in BT. I've been asking for feedback from various sites, and someone sent me a scathing review of Carrier's use of BT in the Fine-Tuning Argument by Luke Barnes. I've updated my review with quotes from Barnes. Barnes also wrote about the applicability of using BT for history. From my updated review: http://members.optusnet.com.au/gakus...HJ_Review.html

                              Next, Luke Barnes, a Postdoctoral Researcher at the Sydney Institute for Astronomy, University of Sydney, Australia, analyses Carrier's application of BT in Carrier's article on the Fine-Tuning Argument. Barnes has also argued against apologists like William Lane Craig who have used BT. Barnes has written a four-part series on Carrier's use of BT, and concludes (rather harshly):
                              This is from a guy who has lectured on Bayes’ theorem (heaven help those students) and written books and articles with “Bayes’ theorem” in the title. Carrier’s faults are not slips of notation, minor technicalities or incorrect arithmetic. While presenting himself as a modern Bayesian, he is actually a finite frequentist, subscribing to an outdated, overly restrictive and practically useless interpretation of probability. He offers no defence of finite frequentism, fails to mention its tension with Bayesianism and ignores its clear failure to account for probability as it is used in cosmology (relevant to fine-tuning), scientific discovery, and history (Carrier’s own field). Carrier can’t even apply his own half-baked ideas consistently, abandoning them when convenient.

                              Further, when the time comes to demonstrate the use of Bayes’ theorem, Carrier bypasses it. He tries to argue that likelihoods are irrelevant to posteriors. The whole point of Bayes’ theorem is to use likelihoods (and priors) to calculate posteriors. No scientist, no statistician does probability like this, and for good reason.

                              Carrier responds here, with Barnes also commenting on Carrier's blog page. Jeffery Jay Lowder, co-founder of the Infidels Internet website, examines the correspondence between Carrier and Barnes, and, while there are times that Lowder agrees with Carrier over Barnes, nevertheless considers Barnes' criticisms of Carrier's use of BT to form 'a prima facie devastating critique'.

                              In his 'Part 1' article, Barnes describes his thoughts of using BT for history. Barnes writes:
                              It is baffling that, of all the people to be advocating finite frequentism, Carrier is a historian. Given the documentary and archaeological evidence, what is the probability that Caesar crossed the Rubicon in 49 BC? Well, how many times in our past experience has that evidence been associated with a known case of Caesar crossing the Rubicon? None out of none. Thus, the probability that Caesar crossed the Rubicon in 49 BC is undefined. Broadening the reference class would be subjective and arbitrary – to which other events in history should we compare? In general, all historical hypotheses that invoke unique events have undefined probability. That includes all historical hypotheses (since time is linear and thus all events are unique), and thus the study of history is impossible given Carrier’s approach to probability.
                              That said, Barnes doesn't seem necessarily to be against BT being used for history, but rather Carrier's approach.

                              Comment

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