Ought implies can. Most people can’t do Bayesian reasoning correctly. So Bayesian reasoning is not how they ought to reason. In particular, a reduction of epistemic ought to the kinds of probability fcts that are involved in Bayesian reasoning fails.
I suppose the main worry with this argument is that perhaps only an ought governing voluntary activity implies can. But the epistemic life is in large part involuntary. An eye ought to transmit visual information, but some eyes cannot—and that is not a problem because seeing is involuntary.
However, it is implausible to think that we humans ought to do something that nobody has been able to do until recently and even now only a few can do, and only in limited cases, even if the something is involuntary.
If Bayesian reasoning isn’t how we ought to reason, what’s the point of it? I am inclined to think it is a useful tool for figuring out the truth in those particular cases to which it is well suited. There are different tools for reasoning in different situations.
5 comments:
Maybe the relevant ought facts are like this: Even if we can't reason in Bayesian way, we can acquire that ability, and we ought to. So we ought to do something such that, if we do it, then we ought to reason in a Bayesian way.
I can imagine someone saying something like, if we ought to phi, and phi-ing implies that we ought to psi, then we ought psi.
Consider something as plain as it being the case that I ought to place the item on the shelf (I promised to, or I work at a grocery store). But I can't, since I haven't picked up the item, and so how can I place the item on the shelf? Clearly, we say that I can place the item, because I can pick it up first.
I realise as I type that this example isn't exactly what I started with, so maybe this example illustrates the following principle:
Principle: If you can and ought do something X, such that by doing X, you can do something Y, and the ability to do Y is sufficient for it being the case that you ought to do Y, then you can do Y and you ought to do Y.
In the item-shelving case, X is pick up the item and Y is place the item on the shelf. In the epistemology case, X is acquire Bayesian reasoning skills and Y is reasoning in a Bayesian way. Of course, this only applies to those who can learn, which is probably most adults.
I have no idea how plausibly I find this. I'm just playing around with possibilities.
Even if you acquire the basic mathematical skills, keeping track of probabilities and conditionalizing on all the evidence is simply beyond our capabilities. I am constantly receiving vast amounts of data. I just can't conditionalize on it. All I can do is to pick out some small subset of the data that seems relevant, and conditionalize on that. Take the lab scientist who sees an instrument display "3.445". Maybe, though even that is a stretch, they can conditionalize on the instrument displaying "3.445". But that's such a small part of their evidence: there is, for instance, the particular pattern of lights and shadows playing over the instrument display, the flow of air from the vents, etc. Sure, one normally approximates by assuming all that other stuff is independent of what one cares about in the experiment. But the fact remains that one is failing to conditionalize on all one's data.
Could it be that the epistemic responsibility is to "go where the evidence points", and that Bayesianism is just the most rigorous form of that? It would be like saying that we ought to measure carefully when cutting the pieces to build someone's house, but that we can only do as well as our available instruments let us, and that that is sufficient. Bayesian reasoning as such may not be an "available instrument" for most of us, but we ought to approximate it as much as we can.
I think Steven Pinker just wrote a book in which he equates rationality with something like Bayesianism. I haven't read it yet, but it's on my list!
I agree and not just people not capable of correctly and properly Bayesian reasoning shouldn't Bayesian reason, but also people not capable of correctly and properly estimating things and reasoning in general should not estimate things and reason in general.
Just look at what happend in Sally Clark's case:
"Making A Math Murderer" by Vsauce2
I guess, that for good reasons "Meadow's law" is no longer a law.
But if "Meadow's law" is no longer a law, then why are so many theists and apologists so fond of Plantinga's poker analogy?!?
I guess, because even though they can not correctly and properly Bayesian reason and therefore shouldn't do it, they are doing it regardless of the possibility of them being so irrational with it.
Post a Comment