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This seems extremely pertinent for LW: a paper by Andrew Gelman and Cosma Shalizi. Abstract:
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science.
I'm still reading it so I don't have anything to say about it, and I'm not very statistics-savvy so I doubt I'll have much to say about it after I read it, but I thought others here would find it an interesting read.
I stole this from a post by mjgeddes over in the OB open thread for July (Aside: mjgeddes, why all the hate? Where's the love, brotha?)
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Okay, here's something that could grow into an article, but it's just rambling at this point. I was planning this as a prelude to my ever-delayed "Explain yourself!" article, since it eases into some of the related social issues. Please tell me what you would want me to elaborate on given what I have so far.
Title: On Mechanizing Science (Epistemology?)
"Silas, there is no Bayesian ‘revival’ in science. There is one amongst people who wish to reduce science to a mechanical procedure." – Gene Callahan
“It is not possible … to construct a system of thought that improves on common sense. … The great enemy of the reservationist is the automatist[,] who believes he can reduce or transcend reason. … And the most pernicious [of them] are algorithmists, who believe they have some universal algorithm which is a drop-in replacement for any and all cogitation.” – "Mencius Moldbug"
And I say: What?
Forget about the issue of how many Bayesians are out there – I’m interested in the other claim. There are two ways to read it, and I express those views here (with a bit of exaggeration):
View 1: “Trying to come up with a mechanical procedure for acquiring knowledge is futile, so you are foolish to pursue this approach. The remaining mysterious aspects of nature are so complex you will inevitably require a human to continually intervene to ‘tweak’ the procedure based on human judgment, making it no mechanical procedure at all.”
View 2: “How dare, how dare those people try to mechanize science! I want science to be about what my elite little cadre has collectively decided is real science. We want to exercise our own discretion, and we’re not going to let some Young Turk outsiders upstage us with their theories. They don’t ‘get’ real science. Real science is about humans, yes, humans making wise, reasoned judgments, in a social context, where expertise is recognized and a rewarded. A machine necessarily cannot do that, so don’t even try.”
View 1, I find respectable, even as I disagree with it.
View 2, I hold in utter contempt.
That it should be possible to Algorithmize Science seems clear from that the human brain can do science and the human brain should be possible to describe algorthmically. If not at a higher level, so at least -- in principle -- by quantum electrodynamics which is the (known and computable in principle) dynamics of electrons and nuclei that are the building blocks of the brain.( If it should be possible to do in practice it would have to be done at a higher level but as a proof of principle that argument should be enough.)
I guess, however, that what is actually meant is if the scientific method itself could be formalised (algorithmized), so that science could be "mechanized" in a more direct way than building human-level AIs and then let them learn and do science by the somewhat informal process used today by human scientists. That seems plausible. But has still to be done and seems rather difficult. The philosophers of science is working on understanding the scientific process better and better, but they seem still to have a long way to go before an actually working algorithmic description has been achieved. See also the discussion below on the recent article by Gelman and Shalizi criticizing bayesianism.
EDIT "done at a lower level" changed to "done at a higher level"