“At what point do you consider yourself to have read enough?”
How about a single statistics class at a university. At that point one might appreciate the set of things one might not know about yet. In reality, though, I feel that if you want to blog about technical topics, you should be an expert on said technical topics. If you are not an expert, it seems you should listen, not talk.
“Also, I don’t see how this creates negative externalities on people.”
Conditional on you being wrong, you should expect no negative externalities only if you expect the activity of blogging to be akin to pissing into the wind—you don’t expect folks to take you seriously anyways. If you are not an expert yet, you should not be very confident about avoiding being wrong on technical topics.
If folks do take you seriously, they either get the wrong idea, or have to spend energy correcting you, or leave it alone, and let you mislead others.
How about a single statistics class at a university.
Not sure why you would say this (assuming I haven’t even done that) and then immediately admit that you expect something much higher. What that level of expertise is I’m not sure, but probably having a Ph.D in statistics?
I have an undergraduate degree in math / physics, and I’ve been working at a data science job for 3 years, while spending most of my free time studying these subjects. I wouldn’t call myself an expert, but at least personally, I think I’ve reached a point where I can feasibly have discussions with people about statistics / ML, and not say things that are totally far off from where at least a certain mode of experts are on the subject.
Of course, the topic I was discussing is actually somewhere on the border of statistics, mathematics, and philosophy, and my guess is there are few academic programs that focus specifically on that overlapping region. That makes it very unlikely for anyone on this site to be at the level of expertise you demand. And if the subject is really that esoteric, it also makes it more unlikely that someone would somehow damagingly misuse what they read here. There are no infohazards (as far as I know) in my post, and there really aren’t any concrete suggestions for actions to take, either.
Maybe there is a cultural/generational difference here.
I have seen very little on Bayes out of LW over the years I agree with—take it as a datapoint if you wish. Most of it is somewhere between at least somewhat wrong and not even wrong.
Hanson had a post somewhere on how folks should practice holding strong opinions and arguing for them, but not taking the whole thing very seriously. Maybe that’s what you are doing.
LessWrong has tended towards skepticism (though not outright rejection) of academic credentials ( consider Eliezer’s “argument trumps authority” discussions in the Sequences). However, this site is more or less a place for somewhat informal intellectual discussion. It is not an authoritative information repository, and as far as I can tell, does not claim to be. Anyone who participates in discussions here is probably well aware of this fact, and is fully expected to be able to consider the arguments here, not take them at face value.
If you disagree with some of the core ideas around this community (like Bayesian epistemology), as well as what you perceive to be the “negative externalities” of the tendency towards informal / non-expert discussion, then to me it seems likely that you disagree with certain aspects of the culture here. But you seem to have chosen to oppose those aspects, rather than simply choosing not to participate.
I don’t really have time to “oppose” in the sense you mean, as that’s a full time job. But for the record this aspect of LW culture is busted, I think.
“somewhat informal intellectual discussion”
All I am saying is, if you are going to talk about technical topics, either: (a) know what you are talking about, or (b) if you don’t or aren’t sure, maybe read more and talk less, or at least put disclaimers somewhere. That’s at least a better standard than what [university freshmen everywhere] are doing.
If you think you know what you are talking about, but then someone corrects you on something basic, heavily update towards (b).
I try to adhere to this also, actually—on technical stuff I don’t know super well. Which is a lot of stuff.
The kind of meaningless trash talk MrMind is engaged in above, I find super obnoxious.
All I am saying is, if you are going to talk about technical topics, either: (a) know what you are talking about, or (b) if you don’t or aren’t sure, maybe read more and talk less, or at least put disclaimers somewhere. That’s at least a better standard than what [university freshmen everywhere] are doing.
But this is a philosophical position you’re taking. You’re not just explaining to us what common decency and protocol should dictate—you’re arguing for a particular conception of discourse norms you believe should be adopted. And probably, in this community, a minority position at that. But, the way that you have stated this comes across like you think your position is obvious, to the point where it’s not really worth arguing for. To me, it doesn’t seem so obvious. Moreover, if it’s not obvious, and if you were to follow your own guidelines fully, you might decide to leave that argument up to the professional, fully credentialed philosophers.
Anyway, what you are bringing up is worth arguing about in my opinion. LW may be credential-agnostic, but it also would be beneficial to have some way of knowing which arguments carry the most weight, and what information is deemed the most reliable—while also allowing people of all levels of expertise to discuss it freely. Such a problem is very difficult, but I think following your principle of “only experts talk, non-experts listen” is sort of extreme and not really appropriate outside of classrooms and lecture halls.
I am saying there is a very easy explanation on why the stats community moved on and LW is still talking about this: LW’s thinking on this is “freshman level.”
I don’t think “know what you are talking about” is controversial, but perhaps I am just old.
I think it’s ok for non-experts to talk, I just think they need to signal stuff appropriately. Wikipedia has a similar problem with non-expert and expert talk being confused, which is why it’s not seen as a reliable source on technical topics.
Being “credential-agnostic” is sort of being a bad Bayesian—you should condition on all available evidence if you aren’t certain of claims (and you shouldn’t be if you aren’t an expert). Argument only screens authority under complete certainty.
Non-experts may not know the boundary of their own knowledge, and may also have trouble knowing where the boundaries of the knowledge of others are as well.
In fact, I think that quite frequently even experts have trouble knowing the extent of their own expertise. You can find countless examples of academics weighing in on matters they aren’t really qualified for. I think this is a particularly acute problem in the philosophy of science. This is a problem I had a lot when I read books by authors of pop-sci / pop-philosophy. They sure seem like experts to the non-initiated. I attribute this mainly to them becoming disconnected from academia and living in a bubble containing mostly just them and their fans, who don’t offer much in the way of substantive disagreement. But this is one of the reasons I value discussion so highly.
When I began writing this post, I did not honestly perceive my level of knowledge to be at the “freshman” level. As I’ve mentioned before, many of the points are re-hashes of stuff from people like Jaynes, and although I might have missed some of his subtle points, is there any good way for me to know that he represents a minority or obsolete position without being deeply familiar with the aspects of that field, as someone with decades of experience would?
The simplest solution is just to read until I have that level of experience with the topics as measured by actual time spent on it, but I feel like that would come at the very high cost of not being able to participate in online discussions, which are valuable. But even then, I probably would still not know where my limits are until I bump into opposing views, which would need to occur through discussion.
You can find countless examples of academics weighing in on matters they aren’t really qualified for.
Yes, absolutely. See also SMBC’s “send in the bishops, they can move diagonally” (chess masters on the Iraq war).
is there any good way for me to know that he represents a minority or obsolete position.
I don’t know if Jaynes represents a minority position (there are a lot of Bayesian statisticians). It’s more like the field moved on from this argument to more interesting arguments. Basically smart Bayesians and frequentists mostly understood each other’s arguments, and considered them mostly valid.
This is the type of B vs F argument people have these days (I linked this here before):
If you really want the gory details, you can also read the Robins/Ritov paper. But it’s a hard paper.
Full disclosure: Robins was my former boss, and I am probably predisposed to liking his stuff.
Re: “what’s a good way to know”: I would say ask experts. Stat profs love talking about this stuff, you can email your local one, and try to go for coffee or something.
Re: “freshman level,” this was perhaps uncharitable phrasing. I just perceive, perhaps incorrectly, a lot of LW discussions as the type of discussion that takes place in dorms everywhere.
This is the type of B vs F argument people have these days (I linked this here before):
I skimmed this a bit, and it seems like the argument went several rounds but was never actually resolved in the end? See Chris Sim’s last comment here which Robins and Wasserman apparently never responded to. Also, besides this type of highly technical discussion, can you point us to some texts that explains the overall history and current state of the F vs B debate in the professional stats community? I’d like to understand how and why they moved on from the kinds of discussion that LW is still having.
There is a recent bookComputer Age Statistical Inference by Efron and Hastie (who are well-respected statisticians). They start by distinguishing three kinds of statistics—frequentist (by which they mean Neyman and Pearson with some reliance on Fisher); Bayesian which everybody here knows well; and Fisherian by which they mean mostly maximum likelihood and derivatives. They say that Fisher, though the was dismissive of the Bayesian approach, didn’t fully embrace the frequentism either and blazed his own path somewhere in the middle.
We can ask Chris and Larry (I can if/when I see them).
My take on the way this argument got resolved is that Chris and Larry/Jamie agree on the math—namely that to “solve” the example using B methods we need to have a prior that depends on pi. The possible source of disagreement is interpretational.
Larry and Jamie think that this is Bayesians doing “frequentist pursuit”, that is using B machinery to mimic a fundamentally F behavior. As they say, there is nothing wrong with this, but the B here seems extraneous. Chris probably doesn’t see it that way, he probably thinks this is the natural way to do this problem in a B way.
The weird thing about (what I think) Chris’ position here is that this example violates the “likelihood principle” some Bayesians like. The likelihood principle states that all information lives in the likelihood. Of course here the example is set up in such a way that the assignment probably pi(X) is (a) not a part of the likelihood and (b) is highly informative. The natural way for a Bayesian to deal with this is to stick pi(X) in the prior. This is formally ok, but kind of weird and unnatural.
How weird and unnatural it is is a matter of interpretation, I suppose.
This example is very simple, there are much more complicated versions of this. For example, what if we don’t know pi(X), but have to model it? Does pi(X) still go into the prior? That way lie dragons...
I guess my point is, these types of highly technical discussions are the discussions that professionals have if B vs F comes up. If this is too technical, may I ask why even get into this? Maybe this level of technicality is the natural point of technicality for this argument in this, the year of our Lord 2017? This is kind of my point, if you aren’t a professional, why are you even talking about this?
It’s a good question about a history text on B vs F. Let me ask around.
edit: re: dragons, I guess what I mean is, it seems most things in life can be phrased in F or B ways. But there are a lot of phenomena for which the B phrasing, though it exists, isn’t really very clarifying. These might include identification and model misspecification issues. In such cases the B phrasing just feels like carrying around ideological baggage.
My philosophy is inherently multiparadigm—you use the style of kung fu that yields the most benefit or the most clarity for the problem. Sometimes that’s B and sometimes that’s F and sometimes that’s something else. I guess in your language that would be “instrumental rationality in data analysis.”
I don’t think that having a conversation with someone who’s wrong is necessarily bad for myself. Arguing against someone who’s wrong can help me to clarify my own thoughts on a topic.
CFAR supports the notion that one of the best ways to learn is to teach. Mixing reading textbooks passively with active argument is good for learning a subject well.
What did you expect with “Very partisan / opinionated”? I don’t think that’s how the average academic expert would preface his professional position if academics would be in the habit of stating the epistemic status.
I was hoping that “very partisan” would signal that I recognize there are a sizable chunk of people with very different views on the subject, and that recognition indicates some kind of epistemic humility. I was wrong about that, and in the future I’ll try to indicate that more explicitly.
“At what point do you consider yourself to have read enough?”
How about a single statistics class at a university. At that point one might appreciate the set of things one might not know about yet. In reality, though, I feel that if you want to blog about technical topics, you should be an expert on said technical topics. If you are not an expert, it seems you should listen, not talk.
“Also, I don’t see how this creates negative externalities on people.”
Conditional on you being wrong, you should expect no negative externalities only if you expect the activity of blogging to be akin to pissing into the wind—you don’t expect folks to take you seriously anyways. If you are not an expert yet, you should not be very confident about avoiding being wrong on technical topics.
If folks do take you seriously, they either get the wrong idea, or have to spend energy correcting you, or leave it alone, and let you mislead others.
Not sure why you would say this (assuming I haven’t even done that) and then immediately admit that you expect something much higher. What that level of expertise is I’m not sure, but probably having a Ph.D in statistics?
I have an undergraduate degree in math / physics, and I’ve been working at a data science job for 3 years, while spending most of my free time studying these subjects. I wouldn’t call myself an expert, but at least personally, I think I’ve reached a point where I can feasibly have discussions with people about statistics / ML, and not say things that are totally far off from where at least a certain mode of experts are on the subject.
Of course, the topic I was discussing is actually somewhere on the border of statistics, mathematics, and philosophy, and my guess is there are few academic programs that focus specifically on that overlapping region. That makes it very unlikely for anyone on this site to be at the level of expertise you demand. And if the subject is really that esoteric, it also makes it more unlikely that someone would somehow damagingly misuse what they read here. There are no infohazards (as far as I know) in my post, and there really aren’t any concrete suggestions for actions to take, either.
Maybe there is a cultural/generational difference here.
I have seen very little on Bayes out of LW over the years I agree with—take it as a datapoint if you wish. Most of it is somewhere between at least somewhat wrong and not even wrong.
Hanson had a post somewhere on how folks should practice holding strong opinions and arguing for them, but not taking the whole thing very seriously. Maybe that’s what you are doing.
There may indeed be a cultural difference here.
LessWrong has tended towards skepticism (though not outright rejection) of academic credentials ( consider Eliezer’s “argument trumps authority” discussions in the Sequences). However, this site is more or less a place for somewhat informal intellectual discussion. It is not an authoritative information repository, and as far as I can tell, does not claim to be. Anyone who participates in discussions here is probably well aware of this fact, and is fully expected to be able to consider the arguments here, not take them at face value.
If you disagree with some of the core ideas around this community (like Bayesian epistemology), as well as what you perceive to be the “negative externalities” of the tendency towards informal / non-expert discussion, then to me it seems likely that you disagree with certain aspects of the culture here. But you seem to have chosen to oppose those aspects, rather than simply choosing not to participate.
I don’t really have time to “oppose” in the sense you mean, as that’s a full time job. But for the record this aspect of LW culture is busted, I think.
“somewhat informal intellectual discussion”
All I am saying is, if you are going to talk about technical topics, either: (a) know what you are talking about, or (b) if you don’t or aren’t sure, maybe read more and talk less, or at least put disclaimers somewhere. That’s at least a better standard than what [university freshmen everywhere] are doing.
If you think you know what you are talking about, but then someone corrects you on something basic, heavily update towards (b).
I try to adhere to this also, actually—on technical stuff I don’t know super well. Which is a lot of stuff.
The kind of meaningless trash talk MrMind is engaged in above, I find super obnoxious.
But this is a philosophical position you’re taking. You’re not just explaining to us what common decency and protocol should dictate—you’re arguing for a particular conception of discourse norms you believe should be adopted. And probably, in this community, a minority position at that. But, the way that you have stated this comes across like you think your position is obvious, to the point where it’s not really worth arguing for. To me, it doesn’t seem so obvious. Moreover, if it’s not obvious, and if you were to follow your own guidelines fully, you might decide to leave that argument up to the professional, fully credentialed philosophers.
Anyway, what you are bringing up is worth arguing about in my opinion. LW may be credential-agnostic, but it also would be beneficial to have some way of knowing which arguments carry the most weight, and what information is deemed the most reliable—while also allowing people of all levels of expertise to discuss it freely. Such a problem is very difficult, but I think following your principle of “only experts talk, non-experts listen” is sort of extreme and not really appropriate outside of classrooms and lecture halls.
I am saying there is a very easy explanation on why the stats community moved on and LW is still talking about this: LW’s thinking on this is “freshman level.”
I don’t think “know what you are talking about” is controversial, but perhaps I am just old.
I think it’s ok for non-experts to talk, I just think they need to signal stuff appropriately. Wikipedia has a similar problem with non-expert and expert talk being confused, which is why it’s not seen as a reliable source on technical topics.
Being “credential-agnostic” is sort of being a bad Bayesian—you should condition on all available evidence if you aren’t certain of claims (and you shouldn’t be if you aren’t an expert). Argument only screens authority under complete certainty.
Non-experts may not know the boundary of their own knowledge, and may also have trouble knowing where the boundaries of the knowledge of others are as well.
In fact, I think that quite frequently even experts have trouble knowing the extent of their own expertise. You can find countless examples of academics weighing in on matters they aren’t really qualified for. I think this is a particularly acute problem in the philosophy of science. This is a problem I had a lot when I read books by authors of pop-sci / pop-philosophy. They sure seem like experts to the non-initiated. I attribute this mainly to them becoming disconnected from academia and living in a bubble containing mostly just them and their fans, who don’t offer much in the way of substantive disagreement. But this is one of the reasons I value discussion so highly.
When I began writing this post, I did not honestly perceive my level of knowledge to be at the “freshman” level. As I’ve mentioned before, many of the points are re-hashes of stuff from people like Jaynes, and although I might have missed some of his subtle points, is there any good way for me to know that he represents a minority or obsolete position without being deeply familiar with the aspects of that field, as someone with decades of experience would?
The simplest solution is just to read until I have that level of experience with the topics as measured by actual time spent on it, but I feel like that would come at the very high cost of not being able to participate in online discussions, which are valuable. But even then, I probably would still not know where my limits are until I bump into opposing views, which would need to occur through discussion.
Yes, absolutely. See also SMBC’s “send in the bishops, they can move diagonally” (chess masters on the Iraq war).
I don’t know if Jaynes represents a minority position (there are a lot of Bayesian statisticians). It’s more like the field moved on from this argument to more interesting arguments. Basically smart Bayesians and frequentists mostly understood each other’s arguments, and considered them mostly valid.
This is the type of B vs F argument people have these days (I linked this here before):
https://normaldeviate.wordpress.com/2012/08/28/robins-and-wasserman-respond-to-a-nobel-prize-winner/
If you really want the gory details, you can also read the Robins/Ritov paper. But it’s a hard paper.
Full disclosure: Robins was my former boss, and I am probably predisposed to liking his stuff.
Re: “what’s a good way to know”: I would say ask experts. Stat profs love talking about this stuff, you can email your local one, and try to go for coffee or something.
Re: “freshman level,” this was perhaps uncharitable phrasing. I just perceive, perhaps incorrectly, a lot of LW discussions as the type of discussion that takes place in dorms everywhere.
I skimmed this a bit, and it seems like the argument went several rounds but was never actually resolved in the end? See Chris Sim’s last comment here which Robins and Wasserman apparently never responded to. Also, besides this type of highly technical discussion, can you point us to some texts that explains the overall history and current state of the F vs B debate in the professional stats community? I’d like to understand how and why they moved on from the kinds of discussion that LW is still having.
There is a recent book Computer Age Statistical Inference by Efron and Hastie (who are well-respected statisticians). They start by distinguishing three kinds of statistics—frequentist (by which they mean Neyman and Pearson with some reliance on Fisher); Bayesian which everybody here knows well; and Fisherian by which they mean mostly maximum likelihood and derivatives. They say that Fisher, though the was dismissive of the Bayesian approach, didn’t fully embrace the frequentism either and blazed his own path somewhere in the middle.
The book is downloadable as a PDF via the link.
We can ask Chris and Larry (I can if/when I see them).
My take on the way this argument got resolved is that Chris and Larry/Jamie agree on the math—namely that to “solve” the example using B methods we need to have a prior that depends on pi. The possible source of disagreement is interpretational.
Larry and Jamie think that this is Bayesians doing “frequentist pursuit”, that is using B machinery to mimic a fundamentally F behavior. As they say, there is nothing wrong with this, but the B here seems extraneous. Chris probably doesn’t see it that way, he probably thinks this is the natural way to do this problem in a B way.
The weird thing about (what I think) Chris’ position here is that this example violates the “likelihood principle” some Bayesians like. The likelihood principle states that all information lives in the likelihood. Of course here the example is set up in such a way that the assignment probably pi(X) is (a) not a part of the likelihood and (b) is highly informative. The natural way for a Bayesian to deal with this is to stick pi(X) in the prior. This is formally ok, but kind of weird and unnatural.
How weird and unnatural it is is a matter of interpretation, I suppose.
This example is very simple, there are much more complicated versions of this. For example, what if we don’t know pi(X), but have to model it? Does pi(X) still go into the prior? That way lie dragons...
I guess my point is, these types of highly technical discussions are the discussions that professionals have if B vs F comes up. If this is too technical, may I ask why even get into this? Maybe this level of technicality is the natural point of technicality for this argument in this, the year of our Lord 2017? This is kind of my point, if you aren’t a professional, why are you even talking about this?
It’s a good question about a history text on B vs F. Let me ask around.
edit: re: dragons, I guess what I mean is, it seems most things in life can be phrased in F or B ways. But there are a lot of phenomena for which the B phrasing, though it exists, isn’t really very clarifying. These might include identification and model misspecification issues. In such cases the B phrasing just feels like carrying around ideological baggage.
My philosophy is inherently multiparadigm—you use the style of kung fu that yields the most benefit or the most clarity for the problem. Sometimes that’s B and sometimes that’s F and sometimes that’s something else. I guess in your language that would be “instrumental rationality in data analysis.”
I don’t think that having a conversation with someone who’s wrong is necessarily bad for myself. Arguing against someone who’s wrong can help me to clarify my own thoughts on a topic.
CFAR supports the notion that one of the best ways to learn is to teach. Mixing reading textbooks passively with active argument is good for learning a subject well.
That’s fine, but can OP at least preface with [Epistemic status: may not know what I am talking about]?
What did you expect with “Very partisan / opinionated”? I don’t think that’s how the average academic expert would preface his professional position if academics would be in the habit of stating the epistemic status.
I was not asking for a signal “I am not an academic.” I was asking for a signal “don’t take this too seriously, dear reader.”
There is a big difference between having strong opinions and being wrong.
I was hoping that “very partisan” would signal that I recognize there are a sizable chunk of people with very different views on the subject, and that recognition indicates some kind of epistemic humility. I was wrong about that, and in the future I’ll try to indicate that more explicitly.