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Thanks! I feel explicitly encouraged.
This all sounds good to me. In fact, I believe that researchers in the humanities are especially (perhaps overly) sensitive to the reciprocal relationship between theory and observation.
I may have overstated the ignorance of the current situation. The scholarly community has already made some claims connecting the Big Book to Print Shops [x,y,z]. The problem is that those claims are either made on non-quantitative bases (eg, "This mark seems characteristic of this Print Shop's status.") or on a very naive frequentist basis (eg, "This mark comes up N times, and that's a big number, so it must be from Print Shop X"). My project would take these existing claims as priors. Is that valid?
Yes. He said that I should be careful about sharing my project because, otherwise, I'll be reading about it in a journal in a few months. His warning may exaggerate the likelihood of a rival researcher and mis-value the expansion of knowledge, but I'm deferring to him as a concession of my ignorance, especially regarding rules of the academy.
Sorry to interrupt a perfectly lovely conversation. I just have a few things to add:
I may have overstated the case in my first post. We have some information about print shops. Specifically, we can assign very small books to print shops with a high degree of confidence. (The catch is that small books don't tend to survive very well. The remaining population is rare and intermittent in terms of production date.)
There are some hypotheses that could be treated as priors, but they're very rarely quantified (projects like this are rare in today's humanities).
Yep. It's not the Bible. I suspect that there are already good stats compiled on the Q-source, etc.
In a way it's not only futile but limiting to play the guessing game. There are lots of possible applications of Bayesian methods to the humanities. Maybe this discussion will help more projects than my own.
Yes, I see an accord between your statement and Vaniver's. As I said below, most tools have very slow repair cycles.
I was openly warned by a professor (who will likely be on the dissertation committee) not to talk about this project widely.
The capitalized nouns are to highlight key terms. I believe the current description is specific enough to describe the situation accurately and without misleading people, but not too specific to break my professor's (correct) advice.
Have I broken LW protocol? Obviously, I'm new here.
I have just such a thing, referred to as "Marks." I haven't yet included that in the code, because I wanted to explore the viability of the method first. So to retreat to the earlier question, why does my proposal strike you as a GIGO situation?
Fortunately, we know which tool types leave which marks. We also have a very strong understanding of the ways in which tools break and leave marks.
Thanks again for entertaining this line of inquiry.
Have to define your features somehow.
I don't understand what this means. Can you say more?
That's a hell of a summary, thanks!
I'm glad you mentioned the repair cycle of tools. There are some tools that are regularly repaired (let's just call them "Big Tools") and some that aren't ("Little Tools"). Both are expensive at first and to repair, but it seems the Print Shops chose to repair Big Tools because they were subject to breakage that significantly reduced performance.
I should add another twist since you mentioned sheets of known origins: Assume that we can only decisively assign origins to single sheets. There are two problems stemming from this assumption: first, not all relevant Marks are left on such sheets; second, very few single sheet publications survive. Collations greater than one sheet are subject to all of the problems of the Big Book.
I'm most interested in the distinction between unsupervised and supervised learning. And I will very likely PM you to learn more about machine learning. Again, thanks for your help!
EDIT: I just noticed a mistake in your summary. Each sheet is produced by a set of tools, not a single tool. Each mark is produced by a single tool.
Very helpful points, thanks. The scholarly community already has a pretty good working knowledge of the Tools, and thus the theoretical model of Tool breakage ("breakage" may be more accurate than "decay," since the decay is non-incremental and stochastic). We know the order in which parts of the Tools break, and we have some hypotheses correlating breakage to gross usage. The twist is that we don't know when any Print Shops produced the Big Book, so we can only extrapolate a timeline based on Tool breakage
Can you say more about the holdout sample? Should the holdout sample be a randomly selected sample of data, or something suspected to be associated with Print Shops [x,y,z] ? Print Shops [a,b,c] ?
Interesting feedback.
It's the Bible, isn't it.
Ha, I wish. No, it's more specific to literature.
How can you possibly get off the ground if you have no information about any of the Print Shops, much less how many there are? GIGO.
We have minimal information about Print Shops. I wouldn't say the existing data are garbage, just mostly unquantified.
Have you considered googling for previous work?
Yes, but thanks to you I know the shibboleth of "Bayesian stylometry." Makes sense, and I've already read some books in a similar vein, but there are some problems. Most fundamentally, I have trouble translating the methods to a different type of data: from textual data like word length to the aforementioned Marks. Otherwise, my understanding of most stylometric analysis was that it favors frequentist methods. Can you clear any of this up?
EDIT: I have a follow-up question regarding GIGO: How can you tell what data are garbage? Are the degrees of certainty based on significant digits of measurement, or what?
Thanks for the feedback. I actually cleared up the technical language considerably. I don't think there's any need to get lost in the weeds of the specifics while I'm still hammering out the method.
Hi everyone,
I'm a humanities PhD who's been reading Eliezer for a few years, and who's been checking out LessWrong for a few months. I'm well-versed in the rhetorical dark arts, due to my current education, but I also have a BA in Economics (yet math is still my weakest suit). The point is, I like facts despite the deconstructivist tendency of humanities since the eighties. Now is a good time for hard-data approaches to the humanities. I want to join that party. My heart's desire is to workshop research methods with the LW community.
It may break protocol, but I'd like to offer a preview of my project in this introduction. I'm interested in associating the details of print production with an unnamed aesthetic object, which we'll presently call the Big Book, and which is the source of all of our evidence. The Big Book had multiple unknown sites of production, which we'll call Print Shop(s) [1-n]. I'm interested in pinning down which parts of the Big Book were made in which Print Shop. Print Shop 1 has Tools (1), and those Tools (1) leave unintended Marks in the Big Book. Likewise with Print Shop 2 and their Tools (2). Unfortunately, people in the present don't know which Print Shop had which Tools. Even worse, multiple sets of Tools can leave similar Marks.
The most obvious solution that I can see is
- to catalog all Marks in the Big Book by sheet (a unit of print production, as opposed to the page), then
- sort sheets by patterns of Marks, then
- make some associations between the patterns of Marks and Print Shops, and then
- propose Print Shops [x,y,z] to be the sites of production for the Big Book.
If nothing else, this method can define the n-number of Print Shops responsible for the Big Book.
The Bayesian twist on the obvious solution is to add some testing onto the associations, above. Specifically,
find some books strongly associated with Print Shops [x,y,z], in order to
assign probability of patterns of Marks to each Print Shop, then
revise initial associations between Print Shops [x,y,z] and the Big Book proportionally.
I'm far from an expert in Bayesian methods, but it seems already that there's something missing here. Is there some stage where I should take a control sample? Also, how can I find a logical basis for the initial association step, when there are many potential Print Shops? Lastly, how can I account for the decay of Tools, thus increasing Marks, over time?