A reader pointed something out to me that was a fantastic facepalm moment. It’s another demonstration of how Bart Ehrman doesn’t know how epistemic probability works, and not only hasn’t read On the Historicity of Jesus, he doesn’t even know what it argues. This leads me to two general lessons I hope my audience has already learned, but that he certainly needs to learn. The first is that everything that isn’t logically impossible always has a nonzero epistemic probability of being true. And recognizing this is fundamentally essential to all sciences and knowledge-seeking fields. The second is that if you want to challenge an assigned probability, you have to first understand what the claimant is measuring—what are they saying their assigned probability is a probability of? And recognizing this is fundamentally essential to all sciences and knowledge-seeking fields.
I’ll explain these two general lessons using a recent example of Bart Ehrman failing to do his job as a historian. I’ve noted recently that often enough Ehrman does not read the peer reviewed literature on the subjects he has an emotional investment in—not just mine (which is bad enough, since my book is not only the latest but the only peer reviewed book ever published on the question of the historicity of Jesus in nearly a hundred years), but even articles I cite from the peer reviewed literature that support me (likewise the peer reviewed literature that supports others he is intent on disagreeing with, from Murdock to Doherty to Goodacre). And in result he argues against things I didn’t say, and then he doesn’t argue against established peer reviewed arguments to the contrary of his position. I also noted that he plays fast and loose with the facts, but more bizarrely, he insists that history has to be about probability while declaring probability theory inapplicable to history. I’ve said before that this is because he doesn’t know how probability works. Now we have an example. (And hat tip to Josh for calling my attention to it).
The Incident
On Ehrman’s blog, a reader posted a remark on November 6 (2016) that said, among other things:
[….] Speaking of proving Carrier wrong with mathematical precision, being a social scientist who regularly works with mathematical models, one of the things about his argument that I find insulting is his attempted use of math—specifically Bayesian statistics—to prove the improbability of an historical Jesus. His method is so error-ridden that he should be embarrassed to even express it in public, let alone actually publish it as a work of scholarship. For starters, Carrier starts off by assigning probabilities, not to the most mundane aspects of a living person, such as name, gender, nationality, time and place of birth, etc., but instead to the most conveniently ridiculous claims, such as virgin birth, resurrection, miracles, etc.
That’s like if I were to attempt to prove that the Buddha Siddhartha Gautama never existed by assigning probabilities to his sitting under a Bodhi tree for forty days, being born from his mother’s side (instead of through the birth canal), and eventually achieving Nirvana after death. I would have to pull those probablities out of the same place that Carrier gets his probabilities for Jesus; namely, ex rectum. I mean, let’s be serious. How does one find the probability of parthenogenesis? The best we can say is that the probably is either 0 or 1; that is, either it’s totally impossible or it’s possible, because if it’s determined to be possible (via observation or experimentation or whatnot) then it becomes plausible, and once it become plausible then it becomes probable.
And that’s why trying to assign probablities to miracles is a fool’s errand. Once you assign it a probability above zero, you’ve automatically made it “possible,” and if it’s “possible” then it’s not a “miracle”. That’s what logicians call a category error.
The next day Ehrman responded:
Thanks—that’s a lucid explanation of the problem of his use of Bayes statistics.
This is the facepalm moment. Because everything that reader said is false. It’s false even on basic probability theory, and yet he or she claims to be an expert in that because they are a “social scientist.” But it’s also false on the basic facts: I have never “assigned probabilities” in this debate to miracles actually happening. That is nowhere any part of my book—where in fact, exactly to the contrary, I declare I will not even consider models of the historical Jesus that propose any miracles, that I will only consider miracle-free historical Jesus models, and indeed only the most credible of them (see OHJ, pp. 14, 26, and 30, where I declare any miracle-reliant theory of historicity ignored for the remainder of the book). The only time I ever even “assign probabilities” to anything connected with miracles, I explicitly deal only with the frequency with which such stories are invented (not “actually happen”), for real persons vs. invented ones (OHJ, Ch. 6). And I am so explicit about that, there is no possible way anyone who read my book could mistake the matter. So that means this reader never read my book. It also means Ehrman never has, either. Because he actually thinks this commenter is correctly describing what’s in it!
Ehrman’s remark here is revealing in a way very similar to what happened in the Penis-Nosed Statue case, where Ehrman had remarked on his blog at how good and useful some research was that some fans posted there. Then their claims were shown to be shoddy and false, thus exposing the fact that Ehrman didn’t know that. Which means he never did any of that research himself, and thus actually didn’t meet even the most rudimentary moral responsibility of a historian when responding to Dorothy Murdock on the matter of the Penis-Nosed Statue—and then when he was caught, he tried lying about it instead of admitting he screwed up. (For summary, links, and evidence on that case, see my Ehrman Recap, Item 11.) Here he has done this again: with a single remark Ehrman has proved he doesn’t know what this reader is saying is false—both as to probability theory and as regards what I actually argue in OHJ. Such a man has no competence to debate this matter. No opinion he has of my work is valid, if he neither knows what I argue, nor how probability works.
A good analogy exists with Thomas Thompson’s case against the historicity of Moses—a conclusion now generally accepted by the mainstream consensus, despite having been fought by the same kind of uninformed and reactionary tactics used or endorsed by Ehrman on this question now. (See my summary of the Davies article on this whole point.) Imagine if a reader had posted on Ehrman’s blog that Thompson was an idiot because he was assigning probabilities to Moses actually parting the Red Sea and then from that concluding Moses didn’t exist (“What a fool! Hahhaha!”). Ehrman would probably not say “That’s a lucid explanation of why Thompson is wrong.” No, he would correct his reader by pointing out that that’s not what Thompson did at all, that Thompson doesn’t assume the only options are miracle Moses or no Moses, and that Thompson’s case has always been against a mundane Moses, not a miraculous one. (And that Thompson’s resulting conclusion is widely accepted—even by Ehrman himself.)
Of course, if Ehrman also understood probability theory, he’d also have corrected this reader’s atrocious epistemological logic. But I’ll get to that next. First, let’s explore the more important matter of what I (like Thompson) actually argued…
Taking the Second Point First: Know What Your Opponent Is Measuring
I nowhere assign probabilities to miracles in OHJ. Ehrman and this reader evidently have not read Chapter 6 of OHJ, where I explain what I am assigning the probabilities to, and the data I am deriving those probabilities from (in respect to where anything about a miracle comes up): the frequency not of such events (like parthenogenesis) happening, but the frequency of inventing so many details like that for a real man rather than a fictional one; and I derive that frequency from a long list of actual prior examples of that being done—to both real and fictional persons. A real social scientist would recognize that procedure as valid; it’s exactly what they would do. The only difference is that my database is small (relative to what social scientists deem adequate), so I need very wide margins of error to maintain validity, and indeed that’s exactly what I generate. If he or she wants to argue those margins should be wider, they need to make such an argument from the data, not the armchair. But it’s clear they don’t even know what my margins of error are, or that I even assigned any.
Nor do they know what I am measuring. That reader essentially is lying—insofar as he (or she) is representing himself (or herself) as actually knowing what I argue. If they actually knew, they would know that I am not measuring anything as absurd as “the frequency of parthenogenesis.” Rather, I am measuring the frequency of “inventing for a hero a myth of parthenogenesis.” And not merely that, but the frequency of inventing a huge array of mythical claims—not just that one; and not just any mythical claims, but a well-established popular homeostatic cluster of them. And not merely that, but the frequency of that being done for historical relative to nonhistorical persons. There is nothing here about measuring how often those absurd claims are true—of anyone. The only thing I’m measuring is how often such a set of claims gets generated for someone (and those claims being true or false is completely irrelevant to that question).
And as it happens, when you look at all past precedents of that happening (of which we have at least fourteen from the relevant causal historical context; fifteen counting Jesus, for whom it indisputably also happened), not a single one happened to a historical person. Fourteen cases. Not one historical. That’s a trend. And it entails that anyone to whom that same process happens, is not likely to be historical (OHJ, pp. 231-32). Historicity is thus not what the evidence of past cases indicates. It indicates that this particular process only happens to nonhistorical heroes. One can then say that maybe Jesus is the exception, the singular bizarre exception out of all known fifteen cases. And that’s true. And you can calculate a probability for that, and I did (OHJ, pp. 242-43; cf. pp. 239-44, on the Rank-Raglan type as my model). One can then say maybe it’s a fluke somehow, that it’s just a coincidence that no historical persons ever got typed that way, despite it happening fourteen (and now fifteen) times. And that’s true. And you can calculate a probability for that, and I did (ibid.), leaving me with my margins of error (which I classify as a judicantiori and a fortiori: OHJ, pp. 596-99).
So, again: The question I ask in OHJ is not “how frequently do such miraculous events actually happen” (nor do I then conclude the converse of that is “Jesus didn’t exist,” which indeed would be a violation of basic logic) nor is it even “how frequently do such miraculous events get made up,” but rather, “how frequently do such miraculous events get made up for actually historical persons.” I find the actual data supports a conclusion of never. And that’s not “ex rectum” (which is incorrect Latin, BTW; it should be ex recto, but whatever, this guy or gal doesn’t get anything correct, so why should I assume they know Latin?). To the contrary, it’s derived from actual data: at least fourteen known prior cases. To get any result other than “never” requires making assumptions not based in any data. Those assumptions can nevertheless be credible, if they derive from what is entailed by probability theory, namely, the probability it’s an “accident” that all fourteen prior times this happened not once did it happen to a historical person. Like parthenogenesis, we’ve never seen it happen to a historical person. But it’s much more causally plausible than parthenogenesis, if the right chance accidents just happened to have occurred. And accordingly, I account for that. This reader on Ehrman’s blog clearly has no idea I did that. Or even what I was doing at all.
In fact, Jesus meets even more markers for mythical persons than the Rank-Raglan type: he is, unlike most historical persons, a worshiped celestial savior deity (OHJ, pp. 96-108, 230), a dying-and-rising demigod (OHJ, pp. 168-73, 225-29), a revelatory space alien (137-41, 146, 197-206), a prophecy-fulfilling godman (OHJ, pp. 141-43, 230), an aetiological cult figure (OHJ, pp. 8-11, 159-63), and a counter-cultural hero (OHJ, pp. 222-25, 430-31; cf. Proving History, pp. 131-32). But these only reinforce the certainty we derive from the most abundant database we have, which is for the Rank-Raglan type. See Should the Gospels Count More Against Historicity? (Ultimately I find that, apart from what we can determine from and for the Rank-Raglan data, nothing in the Gospels argues for or against historicity: OHJ, pp. 395, 506-09.) One could ask the same of the Epistles, insofar as they establish, as even Ehrman now agrees, that the first Christians from the very beginning typed Jesus, again unlike most historical persons, as an eternal, temporally incarnate archangel living in outer space, and as a standard albeit Judaized “worshiped savior deity” with whom apostles communicated by revelation—though I left that to be counted among the determining evidence (in Chapter 11).
So what I am measuring is how often historical persons get that heavily mythotyped (and indeed that quickly, which should be near impossible for a historical person: OHJ, pp. 248-52), not how often historical persons are born by parthenogenesis, or any such nonsense. That Ehrman doesn’t know that, wholly disqualifies him from having a valid opinion on the merits of my case for ahistoricity. Yet in all the sciences, in all fields of knowledge, understanding what a researcher is measuring is fundamental to being able to evaluate the merits of what they are claiming. A social scientist sure as hell ought to know that. And Ehrman cannot call himself a competent historian if he doesn’t know that either.
And this is again, I have to remind you, just the prior probability I’m talking about. No matter what I get as the prior, evidence can always overcome it. So it does not matter that I find the prior probability of historicity to be 1 in 3, owing to how heavily mythotyped Jesus was compared to all other mythologized historical persons. All you need is a body of evidence that’s four times more likely if he existed than if he didn’t, to wipe that prior out and leave historicity as the more likely conclusion. So we still have to look at the evidence. Of course that doesn’t go well for the historicist, either (OHJ, Chapters 8, 9, 10, and 11). Though it could have gone much better (as it does for Socrates, Tiberius, Alexander the Great, Julius Caesar, Pontius Pilate, or Spartacus—even Herod Agrippa). But this reader (and evidently also Ehrman) doesn’t even know that much about probability theory, or about my argument in OHJ.
Taking the First Point Second: Every Logically Possible Thing Has a Nonzero Epistemic Probability
Now, contrary to this liar on Ehrman’s blog, I did not argue “miracles are improbable, therefore Jesus didn’t exist.” But let’s set that aside now (I’ve already addressed it above), so we can learn a lesson this fool evidently never did, about how epistemic probability works. In other words, what a social scientist sure as fuck should already know.
Anyone who actually knows anything substantive about probability theory—and epistemic probability especially—will have laughed their ass off already at the shocking, galling incompetence of this “social scientist” claiming “miracles” like parthenogenesis must have a probability “of either 0 or 1.” Holy fuckballs. Let’s assume they didn’t just commit the fallacy of foregone probability. Let’s assume they mean—and we’ll stick with the one example—that parthenogenesis is contrary to existing science and “therefore” must have a probability of 0. Only a fool who didn’t know how science worked would say such a thing.
Science would be impossible if we assumed everything that contradicted existing science had a zero probability of occurring. That’s the exact opposite of science: that’s dogma. In Bayesian terms, no matter how good the evidence, even if it gave us a likelihood ratio of 10^500 to 1 in favor of parthenogenesis having occurred, we would still have to conclude it never happens—even after seeing that much evidence for it—if we started with a 0 prior probability of it. Needless to say, no scientific progress would ever be possible if we did that. Therefore, “miracles” like parthenogenesis cannot have a 0 prior. And lo and behold, because real scientists don’t take the advice of this knob on Ehrman’s blog, they have discovered parthenogenesis actually does exist and happens quite a lot. Something they could not have done had they reasoned like he or she did.
Now, yes, we haven’t discovered it in humans, and we can produce a plausible, evidence-based explanation for why. But that does not mean we couldn’t be wrong, that no evidence of it will ever be found, that our causal models are infallible, that we are omniscient. And in fact, we can produce parthenogenesis in humans, using barely space-age technology—we’ve literally done it—so it clearly does not have a probability of 0. The same process we use to accomplish it may have an extremely low probability of occurring in nature, but anyone who confuses “extremely low” with “0” can’t really be a scientist. Likewise there may be ways it can happen unknown to us. Like, for example, sorcery. Or gods. There is nothing about science that entails these have a “zero” probability of existing. Science has accumulated enough data to make their existence extremely unlikely. But again, “extremely unlikely” is not “zero.” And no self-respecting scientist should ever be caught confusing the two.
The fact of the matter is, everything that isn’t logically impossible always has a nonzero epistemic probability of being true (Proving History, pp. 23-26; cf. pp. 107-14 and 266-75). And quite a lot of things are logically possible. Including our being in the Matrix; gods and sorcery of some kind being a thing; even parthenogenesis by quantum mechanical accident. So we have to seriously ask how likely these things are, on present background knowledge. For the purposes of determining something as mundane as the historicity of Jesus, we don’t really need to explore that, since we know a fortiori the requisite miracles are far less likely than even billions to one against (see Proving History, index, “a fortiori, method of” and “miracles”), so no theory of historicity that depends on miracles has any realistic chance of being true. That’s why non-delusional historians only consider non-miraculous theories of historicity. And those are, again, the only theories I consider as competing with mythicism in my analysis in OHJ.
But we don’t come to that conclusion because the probability of miracles is “either 0 or 1.” That’s ridiculous. And fantastically ignorant. It’s also fundamentally anti-scientific. As I noted, if we assumed everything contrary to existing science had a 0 probability of occurring, we could never ever be convinced by any amount of evidence that we were wrong, and all progress in science would cease. That’s how Creationists behave. That’s absolutely not how any real scientist, including a “social scientist,” should ever behave. It’s all the worse that he or she somehow wants to define miracle as “impossible,” such that anything that’s possible is ex definito not a miracle. Which is not at all how the word “miracle” is used by anyone, least of all anyone who believes in miracles.
It’s also a fallacy to say that parthenogenesis can never happen because someone calls it a miracle and “we” define “miracle” as “logically impossible.” Not only because they don’t define “miracle” that way, so what “we” define the word as is wholly irrelevant, but also because you can’t change reality by changing what you call it. You can’t “define” parthenogenesis into being impossible. Words don’t have that magical power. And alas, parthenogenesis has been documented, even in humans. So it isn’t impossible. But even if we didn’t have documentation of it yet, it’s still logically possible, and therefore has a nonzero probability of being true; it all depends on future evidence.
This is the difference between “probability” as some esoteric absolute, and what Bayes’ Theorem measures, which is epistemic probability: the probability that something is true given what we currently know—which means this is a probability that is conditional on information we both have and don’t have; which means information we don’t have can change that probability, and therefore that probability can never be zero. In an esoteric absolute sense everything has a probability of 0 or 1, it either actually is true or actually is false. Your probability of winning the lottery tomorrow (indeed, even if you didn’t buy a ticket; because there is a nonzero probability someone else did and you have it in a coat pocket by some accident or design unknown to you) is indeed literally “either 0 or 1,” because, thanks to causal determinism, it’s a foregone conclusion which it will be.
Everything in your life can be described that way. But that’s useless information. The problem you face is not determinism. The problem you face is not knowing stuff—such as what lottery number will be selected to win, or whether someone snuck a lottery ticket into your coat pocket; or indeed, whether someone’s phoning you to tell you you won, means you actually won. And that’s all a question of epistemic probability. When we have really good information, our epistemic probability converges on a physical probability—absent evidence you already won, the epistemic probability you will win a lottery is nearly the same as the actual physical frequency of your chosen number coming up on a hypothetically endless series of winning lottery number selections; it is only not exactly the same, because it has to be adjusted for the physical frequency of other factors that can affect the outcome, like fraud. Epistemic probability is what you end up with when all those factors are adjusted for (see Proving History, index, “epistemic probability”). And yes, this is what’s really going on even when Bayesians describe probabilities as degrees of belief (Proving History, pp. 265-80).
This even extends to the logically impossible, insofar as we can never be absolutely certain that what we think is logically impossible actually is (see The God Impossible), even when we have widely vetted and published logical proofs of the fact (Proving History, p. 25, with p. 297 n. 5). The exceptions are actually extremely limited—basically, just raw, uninterpreted, present experience (by Cartesian principle). Epistemically, everything else has a nonzero probability of being true or false. That includes magically parting seas and turning sticks into snakes, virgin births, psychic battles with demons, partying with Satan, and flying unaided into outer space. We have to take seriously that the evidence does not warrant our believing these things have a probability of zero; so far as we know, they yet could be true. The evidence does constrain how high that probability can be (there is a probability above which we would not be warranted in saying we know human parthenogenesis is more likely than that); but it also constrains how low it can be (there is a probability below which we would not be warranted in saying we know human parthenogenesis is less likely than that). Any scientist who doesn’t know that, sucks at science.
See Miracles and Historical Method for the full skinny on how historians logically must deal with miracle claims. It’s not like what this fool claims. Who doesn’t know jack about historical method. Or probability theory, apparently.
And that makes two strikes. They and Ehrman get wrong what my book argues, and how probability works. The general takeaway is that when this guy (or gal) on Ehrman’s blog says “his method is so error-ridden that he should be embarrassed to even express it in public, let alone actually publish it as a work of scholarship,” he (or she) is actually talking about himself (or herself).
Conclusion
So, a liar posts on Ehrman’s blog that “Carrier starts off by assigning probabilities, not to the most mundane aspects of a living person, such as name, gender, nationality, time and place of birth, etc., but instead to the most conveniently ridiculous claims, such as virgin birth, resurrection, miracles, etc.,” and Ehrman is so uninformed he actually thinks that lie is true. And then this liar becomes an antiscientific fool and makes a wildly false claim about the prior epistemic probability of paranormal events, and Ehrman is so ignorant of probability theory he doesn’t even know that. This is the guy whose opinion we are heeding on this subject? Please explain why. And please also explain why the only way anyone can rebut my case in OHJ…is by lying?
Apart from “tell the damned truth,” there are two lessons everyone should learn here; please do heed them: (1) You have to get right what someone is measuring when they are making claims about probability before you can criticize those claims. You cannot engage in rational discourse in any field of knowledge if you fail to do this. And (2) everything that’s logically possible has a nonzero epistemic probability of being true; so (a) you have to take seriously what its epistemic probability actually could be, and seriously ask what facts you justify that probability with, and (b) science fundamentally requires the axiomatic assumption that anything we are certain about could yet be false, and this absolutely requires us to assign every such thing a prior probability more than zero. You do not know how science works (or history for that matter), if you do not know that.





I believe that one needs to take a step back and look at the “population” of people engaging in the historicity of Jesus debate. You essentially have either a Christian apologist an ex-christian theologian or an atheist. The problem is only the latter group work with evidence by starting at zero and then trying to building it with whatever reliable evidence is available.
The first two groups start off high and then work their way down. As an example, an ex-theologian ex Christian will operate from an unfounded bias of “some of this must be true – let’s subtract what’s obviously false”. Essentially because of their previous relationship with the religion they have a bias even if they don’t still believe in it. There’s always something left in that faith jar which hasn’t been removed yet.
That is why evidence supporting mythicism would have the unusual appearance of being esoteric to the greater majority of the “population” because they are unable to approach the topic from a clean slate perspective.
One of the most interesting things Dr. Carrier mentioned when he began his investigation into the historicity of Jesus was when he said he took a fresh new look into Christianity and essentially hit the reset button and said “ok, let’s start again and work our way up and see what evidence really does exist for the existence Jesus”.
Unfortunately, I think for people like Bart Erhman, they are unable to hit this hard reset button. Perhaps it’s their previous faith that gets in the way or perhaps they are just old schooled and old habits take longer to die.
This would have been a better pic for the article:
http://i.stack.imgur.com/jiFfM.jpg
“Your probability of winning the lottery tomorrow (indeed, even if you didn’t buy a ticket; because there is a nonzero probability someone else did and you have it in a coat pocket by some accident or design unknown to you) is indeed literally “either 0 or 1,” because, thanks to causal determinism, it’s a foregone conclusion which it will be.”
Wouldn’t this contradict some of the fundamentals of quantum mechanics? The uncertainty principles and the probabilistic nature of quantum mechanical rules that govern the universe seem to contradict any strict absolute causal determinism
Maybe. We don’t really know. I discuss the options in Sense and Goodness without God, III.4, pp. 97-99.
We don’t know that quantum mechanics is not the emergent effect of a deterministic system below our ability to detect (apart from the superluminal weird stuff, quantum mechanical systems behave a lot like determistic macrosystems governed by statistical mechanics, and some physicists thinks that’s what’s going on, e.g. see Fluid Analogs in Quantum Mechanics).
And even if it’s somehow “purely” random (however that could be), we don’t know that it isn’t just another system subject to foregone probability (since relativity theory entails the future co-exists with the past, future quantum events have already been decided–they only haven’t “yet” from our perspective, because we are in a particular frame of reference in which we are not simultaneous with future points time, whereas there are particles, like photons, that are), and so whether a lottery is decided by classical or quantum mechanics, what the outcome will be is still always one or the other, true or false, 0 or 1.
Since an A Theory of time has been refuted by relativity theory in conjunction with observations (we have confirmed simultaneity is reference-frame dependent, i.e. whether one event is simultaneous with another differs according to reference frame, so there is no absolute clock, therefore A Theory cannot be true), the only possibility on which quantum mechanical outcomes are not foregone in the same way classical mechanical outcomes are would be something like many worlds theory, where in fact you both win and lose the lottery, and different timelines (literally different universes) form and proceed in parallel, those in which you won and those in which you lose. If that’s the case, then the probability that you will win the lottery tomorrow is simply always 1 and simultaneously always 0, depending solely on which universe you are talking about. And there is no meaningful sense in which you are in either universe until the event happens (because before the lottery number is determined, there is only the one universe that hasn’t yet been split by a quantum outcome), and then which one you are in is foregone. That’s the ultimate determinism. That’s even more deterministic than standard determinism…because literally everything necessarily will happen.
And even insofar as we want to ask what the effects are of quantum mechanical indeterminacy on an outcome (regardless of what causes that apparent indeterminacy), it’s also a complex if not impossible task to determine which macroevents are affected by quantum microprobabilities. For instance, a lottery ball selector is very unlikely to be at all affected by quantum indeterminacy. The probability of such an effect is so vastly small it wouldn’t even show up at any realistic rounding point. So even if there was some meaningful way we could say QM prevents foregone probabilities (and we probably can’t, per above), we still end up with the options in almost all cases having a probability of existing so near to 0 or 1 as to make no practical difference (this is more obvious when you think about the past: everything that happened, by having happened, has a 100% probability of having happened, likewise everything that didn’t happen, by not having happened, has a 0% chance of having happened…regardless of how indeterministic QM is; and as relativity tells us, the future is just a past we haven’t observed yet).
This, however, is not the probability we are talking about when we use Bayes’ Theorem to ascertain the probability that a belief we have is correct.
In the conclusion, paragraph 2, line 5, “it’s” should be “its”.
Thanks! Good catch. I’ll fix that when I get back from travel.
Something you don’t explicitly point out in this response is the commenter’s literal assertion of what I think you call the possibiliter fallacy:
“If it’s determined to be possible (via observation or experimentation or whatnot) then it becomes plausible, and once it becomes plausible then it becomes probable.”
Unless you want to take the charitable position that by ‘probable’ they mean ‘has nonzero probability’, this is about as pure a crystallisation of that fallacy as you could hope to find.
Oh! I missed that. Where exactly is that posted?
Because you are quite right.
In the blog remark you quote above, in the section ‘The Incident’, it’s the last part of the second paragraph.
Got it. It’s the sentence right after “The best we can say is that the probably is either 0 or 1.” Which is the boner mistake I focused on (my second section). I totally overlooked how they then used that false statement to argue possible means plausible, plausible means probable, and probable means 100%. Which is a whole series of non sequiturs!
The person who wrote that blog remark almost certainly does not understand Bayesianism compared to you, but allow me to play devil’s advocate for arguing against this: “The first is that everything that isn’t logically impossible always has a nonzero epistemic probability of being true”. You have written in many articles that probabilities in Bayesianism ultimately reduce to some kind of frequency, usually in regards to the rate of how often claims with similar evidence end up being correct. The problem is this: for claims that involve a disruption of the laws of the nature (note, not finding out that we were wrong about a certain law and a new replaced it), we quite literally have a frequency of zero. I am not saying that the probability of this must be assigned 0. But assigning a nonzero probability doesn’t also seem to be correct.
This can be illustrated using this example. Imagine you assign a really small probability to a law of nature holding throughout all of time and then suddenly being disrupted at a value of 1 in 10^-100. Now, one can create a uniformly distributed randomizer of 10^100 values where the probability of me predicting the right value is, approximately, 1 in 10^-100. But wait. Clearly, the first should be still smaller than the second. The randomizer spitting out that value doesn’t break any law of nature. One can now change the probability of a law breaking to an even smaller value. But then one can construct another randomizer to match that value, at which point it still seems unjustified to assign it the same value. This leads to a regress. In other words, no matter how low of a value we go to, it does not seem justified. I think the solution for this is to just have the probability be undefined. Or even 0 until we have evidence to justify that something is possible. Of course, many think that a prior probability of 0 implies that no amount of evidence can change your belief. But I think this stems from a misunderstanding of what we mean by probabilities. If probabilities do reduce to frequencies somewhat, then I’m not sure I see the issue in changing the frequency once we figure out that something is possible from 0 to the new rate. In other words, everything else about the system that you’ve talked about in your articles will still seem to work, even if one denies the claim that “everything that isn’t logically impossible always has a nonzero epistemic probability of being true”
Actually that’s incorrect. I discuss this explicitly under “Smell Test” (index) in Proving History. I also speak on it in How Not to Be a Doofus about Bayes’ Theorem (though from a different angle than yours).
Epistemic frequency approaches objective frequency as evidence approaches completeness (this is discussed in detail toward the end of chapter six of Proving History).
And just because all observations have a frequency of zero does not mean the phenomenon does. If there are 100 miracle claims and we can investigate 10, and find 0 pan out, we cannot say the result is 0 in the other 90 cases. So it is never the case that the frequency is zero until we are omniscient, which will never happen. Until then, all we can say is that the frequency of the unobserved cases matching the observed-case frequency goes up as observed cases go up.
This has been a known problem in probability theory since literally Thomas Bayes himself, who was in part attempting to solve it. It was actually solved a generation later by Pierre-Simon Laplace (possibly one of the most famous and influential mathematicians in history). The result (for straightforward cases) was Laplace’s Rule (which you will find in OHJ 243 and PH 337). The most sophisticated solution, developed a century later, is, indeed, the entire field of modern frequency statistics (which uses random sampling to generate confidence levels and intervals to accurately predict results in the unsampled data).
Laplace’s toy example for the LR was “will the sun come up tomorrow.” Obviously we cannot observe every day (most of them are in the future). So we cannot know what the “frequency” of sunrises is (there are ways to get to it through nuclear astrophysics but that wasn’t the problem Laplace was solving). But we can generate an expected frequency from past cases. We can do even better if the past cases are equivalent to a random sample. See my discussion of the modern example of “grue” in Hypothesis: Only Those Who Don’t Really Understand Bayesianism Are Against It.
When we are approximating an objective frequency with an epistemic frequency, this increases rather than decreases uncertainty, so it does not make us “more omniscient” but less, and therefore cannot ever get to a frequency of zero. For example, if a logical formula (evidence of kind E input to inference model M) is “right” 100% of the time because we’ve only applied it 10 times, it cannot be the case that that inference model is always right, because there are thousands of cases we have not yet applied it to and therefore we cannot say the frequency in those thousands of cases is the same as in the 10. Our data is incomplete.
So the observed frequency in the sample is (almost) never identical to the dataset being sampled. In frequency statistics this can be worked out: a random sample of size D will logically entail a probability, not a certainty, that the observed frequency is actual, and in fact for zeroes that probability is maximally low owing to permutation theory. And that probability is itself a frequency (of how many sets with those properties will match prediction). That is indeed why that entire field of study is called “frequency” statistics.
For example, imagine we have a large enough sample to say that that probability is 5%. That means there is a 5% chance, so far as we know on existing observations, that the inference will fail us (i.e. the inference that “observed case frequency is the actual case frequency”). So we have to accept that failure expectancy. We can never get it to zero. Indeed, not even with all data—because there is always some expected frequency (however low) that we are mistaken in having all the data or that we are mistaken in what the data are (e.g. mistaking hits as misses or vice versa).
I make this point in Proving History with deductive mathematical proofs and Condorcet’s Jury Theorem (297). There is always some nonzero frequency that something has gone wrong in the data formation or collection or observation. Even if that frequency is extremely small and thus “effectively” zero it is never formally (mathematically) zero.
This has been done.
For example, Carroll and Chen have calculated the effective quantum probability that your fingernail will at some point explode into a cosmic Big Bang (vaporizing you and the local universe): it’s 10^-10^10^56.
The relevance of this to your question is: precisely because we do not have (and will never have) infallible access to all the data (we can never observe every state our universe will ever be in or could ever be in), the frequency of our being wrong about something is always nonzero, because the frequency of “our being wrong assuming unobserved data match observed data” is always nonzero.
For example, even when we get a hit (the rare cases where we do get to observe all the data and confirm we were right, the frequency in the observed sample matched the frequency in the then-previously unobserved sample), we still don’t get this result 100% of the time (we always find cases that don’t match; indeed, frequency statistics even counts how often that happens). So we know the inference “observed frequency is actual” always has a nonzero frequency of being false.
And that’s just what we have to live with. We just have to accept it and work around it.
Notably even God is in the same bind, as even a God who believes themselves fallible and omniscient might be wrong and can never be absolutely sure they are not, because there is always some nonzero frequency in the set of all logical possibilities that God is a victim of a Cartesian Demon—it is logically impossible to be 100% certain you are not; it is only possible to be ~100% certain you are not. And this is a formal fact of set theory, so even God cannot escape this.