5 ms·

While we do need polymaths, the problem with a lot of self described polymaths is that they skip the hard part of different fields and only focus on a 'Gladwell

by Fede_V 9y ago

While we do need polymaths, the problem with a lot of self described polymaths is that they skip the hard part of different fields and only focus on a 'Gladwellesque' understanding. I think a better way to be a polymath is to be a real expert on a particular subject, and then augment that with ancilliary knowledge from other fields. If you claim to understand physics but don't know maths, or claim to be an expert on classical literature but don't know how to read greek/latin, then you aren't a polymath but a bullshit artist. The polymaths I respect are people like Peter Medawar, JD Bernal, etc.. My particular pet peeve lately is people (especially prevalent in the rationalist sphere) that talk about how Bayesian they are, but don't know what a conjugate prior is, or have no understanding of MCMC sampling. There is absolutely nothing wrong with not knowing either of those two things - but if you don't know either, then describing yourself as a Bayesian is just putting on airs.

deleted 9y ago
[deleted]
NHern031 9y ago
I have no idea what you are talking, but I love it. There is nothing more satisfying than seeing a peraon be so passionate about something. Thank you for posting this, it made me happy.
joe_the_user 9y ago
Yeah, The article kind of got off on the wrong foot by talking about trivia masters. I wouldn't say that the people who make important contributions through being polymaths are those who bring the insights of several fields together. I think someone like Julian Jaynes, who brought together strands from a variety of disciplines to formulate his theory of Bicameralism is an example of such a wide-ranging thinker [1]. Reasonably respected Marxist economist David Laibman[2] is also a well-know blues singer and well-know mainstream economist Kenneth Rogoff is a chess grandmaster and something of fraud. But the extra expertise of these two experts doesn't seem to matter either in their performance. [1] https://en.wikipedia.org/wiki/Julian_Jaynes https://en.wikipedia.org/wiki/Julian_Jaynes [2] https://en.wikipedia.org/wiki/David_Laibman https://en.wikipedia.org/wiki/David_Laibman [3] https://en.wikipedia.org/wiki/Kenneth_Rogoff https://en.wikipedia.org/wiki/Kenneth_Rogoff
eli_gottlieb 9y ago
>My particular pet peeve lately is people (especially prevalent in the rationalist sphere) that talk about how Bayesian they are, but don't know what a conjugate prior is, or have no understanding of MCMC sampling. There is absolutely nothing wrong with not knowing either of those two things - but if you don't know either, then describing yourself as a Bayesian is just putting on airs. Mine is related: philosophy types who don't actually know much about the not-philosophy fields they're trying to address. For instance, I once heard someone complain that Bayesianism sucks because of the Problem of New Hypotheses. I explained what nonparametric models are. They were very surprised and excited to hear that this was a thing. What.
westoncb 9y ago
If you are going to be an expert in one thing, and then augment with ancillary knowledge from other fields, you have to pick a subset of knowledge to learn from the other fields (if you weren't just learning this subset, you would also be an expert in the other fields). It looks to me like you are advocating one specific type of subset, which is something like implementation knowledge or foundations: math for physics, greek/latin for classical literature. Not to say that's incorrect, but I would be curious to hear why you believe that's the most important subset to go after. Personally, I see foundations as critical for the field that you're going to be an expert in, but if you're picking up ancillary fields, I believe the question is more open (of course you shouldn't be totally ignorant of foundations, but...). As an example of another subset you could take—which I think is fairly common and perhaps well-justified—meta-knowledge. That seems to be what you're complaining about with folks who are probably of a more philosophical bent who claim to be Bayesians, but don't know MCMC sampling (again, this is knowledge of a particular implementation of Bayesian ideas)—they focus instead on something more like a philosophy of probabilistic thinking, and so what's important to them is the relation of Bayesian probability to frequentist probability. They have no interest in performing calculations in either (doing so could help their goal of course, but it is not their end goal). So if you have to pick a subset (of knowledge of some field), should it be more about how the field relates to other fields, or should it be more about the foundations of the field? IMO it should probably be taken on a case-by-case basis, but perhaps more often than not it's knowledge about how fields relate to one another that ends up being more useful in your ancillary picks (that totally depends on what you're doing with the knowledge though).
- Fede_V 9y ago
  Thanks for the interesting post - however, I respectfully disagree with the example you've picked out. How can you do intelligent philosophy about probability if you don't have a good detailed understanding of what probability is? To make a concrete example: the best philosophers of logic (Kripke, Ruth Barcan Marcus, Quine, etc) were also superb logicians with an excellent understand of logic itself. Regardless of how you feel about his atheism, Richard Dawkins is in my mind the best philosopher of biology currently alive, and his first two books (Selfish Gene and Extended Phenotypes) are masterworks. Certain concepts in physics are much clearer when formulated in math - and you can only really explain them clearly once you are firmly grounded in the math. Speaking for myself, I've never met someone who I would describe as having a fantastic understand of QM yet skipped the formal mathematical training to deeply understand the equations.
aeorgnoieang 9y ago
> but if you don't know either, then describing yourself as a Bayesian is just putting on airs. The more charitable, and arguable more correct, interpretation is that there are (at least) two related but distinct meanings of 'Bayesian' being used. One is referencing Bayes' Rule and some of its implications; the other an entire sub-field of statistics. I'm guessing there isn't much overlap between the two groups of people. It also certainly doesn't seem impossible to be, e.g. "an expert on classical literature but [not] know how to read greek/latin". Surely there's a lot to know about classical literature that isn't strictly pertinent to the actual vocabulary or grammar of ancient Greek or Latin. Or are you claiming that there's only one right way, or only certain right ways, for someone to augment their primary expertise with ancillary knowledge? I haven't previously encountered the idea that being a polymath implied expertise in numerous areas, just knowledge, tho presumably more than most laypeople.
- pacaro 9y ago
  The challenge with the classics example is that if you aren't capable of translating yourself then you are dependent on other peoples translations and therefore their interpretations, so you are adding a layer of indirection, sometimes more than one. My Latin is woefully poor, but armed with a grammar and dictionary I can make a stab. Just doing that shows me just how much interpretation happens.
  - arstin 9y ago
    I doubt anyone would disagree "the world's foremost authorities" on classical lit need to know greek/latin. But carefully reading and comparing several translations while following centuries of commentary can surely make one an expert on, say, themes in Greek epic poetry. I mean, if it doesn't then I'd just want to introduce into our conversation a distinction or two in the area of "knowledge use" and just stipulate which branch I'm intending by "expert"... (This doesn't really matter for the previous point, but I actually think this just follows from agreeing with you and then taking even more seriously "how much interpretation happens".)
ineedasername 9y ago
I think in terms of, to keep it simple, a 1-10 scale. 1 is nothing, you may or may not have heard of the field. 7,8,9,10 are increasing degrees of expertise Given that sort of scale, I'd argue that a true polymath rates a 7 in three or more fairly distinct fields that don't have significant overlap with each other. I think the problem, or watering-down of the term, are the abundance of folks that are around a 3 or 4 in a bunch of areas, maybe enough to get an entry level job that requires knowledge of the field, or roughly equivalent to minor or concentration in an undergrad program. In short, Jack of All Trades != Polymath.
cttet 9y ago
Bayesian involves integration over priors, and conjugate prior/MCMC are tools for integration, which are implementation steps IMO, rather than essential part of understanding the Bayesian approach.

by Fede_V 9y ago

deleted 9y ago

[deleted]

NHern031 9y ago

I have no idea what you are talking, but I love it. There is nothing more satisfying than seeing a peraon be so passionate about something. Thank you for posting this, it made me happy.

joe_the_user 9y ago

Yeah, The article kind of got off on the wrong foot by talking about trivia masters. I wouldn't say that the people who make important contributions through being polymaths are those who bring the insights of several fields together. I think someone like Julian Jaynes, who brought together strands from a variety of disciplines to formulate his theory of Bicameralism is an example of such a wide-ranging thinker [1]. Reasonably respected Marxist economist David Laibman[2] is also a well-know blues singer and well-know mainstream economist Kenneth Rogoff is a chess grandmaster and something of fraud. But the extra expertise of these two experts doesn't seem to matter either in their performance. [1] https://en.wikipedia.org/wiki/Julian_Jaynes https://en.wikipedia.org/wiki/Julian_Jaynes [2] https://en.wikipedia.org/wiki/David_Laibman https://en.wikipedia.org/wiki/David_Laibman [3] https://en.wikipedia.org/wiki/Kenneth_Rogoff https://en.wikipedia.org/wiki/Kenneth_Rogoff

eli_gottlieb 9y ago

>My particular pet peeve lately is people (especially prevalent in the rationalist sphere) that talk about how Bayesian they are, but don't know what a conjugate prior is, or have no understanding of MCMC sampling. There is absolutely nothing wrong with not knowing either of those two things - but if you don't know either, then describing yourself as a Bayesian is just putting on airs. Mine is related: philosophy types who don't actually know much about the not-philosophy fields they're trying to address. For instance, I once heard someone complain that Bayesianism sucks because of the Problem of New Hypotheses. I explained what nonparametric models are. They were very surprised and excited to hear that this was a thing. What.

westoncb 9y ago

If you are going to be an expert in one thing, and then augment with ancillary knowledge from other fields, you have to pick a subset of knowledge to learn from the other fields (if you weren't just learning this subset, you would also be an expert in the other fields). It looks to me like you are advocating one specific type of subset, which is something like implementation knowledge or foundations: math for physics, greek/latin for classical literature. Not to say that's incorrect, but I would be curious to hear why you believe that's the most important subset to go after. Personally, I see foundations as critical for the field that you're going to be an expert in, but if you're picking up ancillary fields, I believe the question is more open (of course you shouldn't be totally ignorant of foundations, but...). As an example of another subset you could take—which I think is fairly common and perhaps well-justified—meta-knowledge. That seems to be what you're complaining about with folks who are probably of a more philosophical bent who claim to be Bayesians, but don't know MCMC sampling (again, this is knowledge of a particular implementation of Bayesian ideas)—they focus instead on something more like a philosophy of probabilistic thinking, and so what's important to them is the relation of Bayesian probability to frequentist probability. They have no interest in performing calculations in either (doing so could help their goal of course, but it is not their end goal). So if you have to pick a subset (of knowledge of some field), should it be more about how the field relates to other fields, or should it be more about the foundations of the field? IMO it should probably be taken on a case-by-case basis, but perhaps more often than not it's knowledge about how fields relate to one another that ends up being more useful in your ancillary picks (that totally depends on what you're doing with the knowledge though).

Fede_V 9y ago
Thanks for the interesting post - however, I respectfully disagree with the example you've picked out. How can you do intelligent philosophy about probability if you don't have a good detailed understanding of what probability is? To make a concrete example: the best philosophers of logic (Kripke, Ruth Barcan Marcus, Quine, etc) were also superb logicians with an excellent understand of logic itself. Regardless of how you feel about his atheism, Richard Dawkins is in my mind the best philosopher of biology currently alive, and his first two books (Selfish Gene and Extended Phenotypes) are masterworks. Certain concepts in physics are much clearer when formulated in math - and you can only really explain them clearly once you are firmly grounded in the math. Speaking for myself, I've never met someone who I would describe as having a fantastic understand of QM yet skipped the formal mathematical training to deeply understand the equations.

aeorgnoieang 9y ago

> but if you don't know either, then describing yourself as a Bayesian is just putting on airs. The more charitable, and arguable more correct, interpretation is that there are (at least) two related but distinct meanings of 'Bayesian' being used. One is referencing Bayes' Rule and some of its implications; the other an entire sub-field of statistics. I'm guessing there isn't much overlap between the two groups of people. It also certainly doesn't seem impossible to be, e.g. "an expert on classical literature but [not] know how to read greek/latin". Surely there's a lot to know about classical literature that isn't strictly pertinent to the actual vocabulary or grammar of ancient Greek or Latin. Or are you claiming that there's only one right way, or only certain right ways, for someone to augment their primary expertise with ancillary knowledge? I haven't previously encountered the idea that being a polymath implied expertise in numerous areas, just knowledge, tho presumably more than most laypeople.

pacaro 9y ago
The challenge with the classics example is that if you aren't capable of translating yourself then you are dependent on other peoples translations and therefore their interpretations, so you are adding a layer of indirection, sometimes more than one. My Latin is woefully poor, but armed with a grammar and dictionary I can make a stab. Just doing that shows me just how much interpretation happens.
- arstin 9y ago
  I doubt anyone would disagree "the world's foremost authorities" on classical lit need to know greek/latin. But carefully reading and comparing several translations while following centuries of commentary can surely make one an expert on, say, themes in Greek epic poetry. I mean, if it doesn't then I'd just want to introduce into our conversation a distinction or two in the area of "knowledge use" and just stipulate which branch I'm intending by "expert"... (This doesn't really matter for the previous point, but I actually think this just follows from agreeing with you and then taking even more seriously "how much interpretation happens".)

ineedasername 9y ago

I think in terms of, to keep it simple, a 1-10 scale. 1 is nothing, you may or may not have heard of the field. 7,8,9,10 are increasing degrees of expertise Given that sort of scale, I'd argue that a true polymath rates a 7 in three or more fairly distinct fields that don't have significant overlap with each other. I think the problem, or watering-down of the term, are the abundance of folks that are around a 3 or 4 in a bunch of areas, maybe enough to get an entry level job that requires knowledge of the field, or roughly equivalent to minor or concentration in an undergrad program. In short, Jack of All Trades != Polymath.

cttet 9y ago

Bayesian involves integration over priors, and conjugate prior/MCMC are tools for integration, which are implementation steps IMO, rather than essential part of understanding the Bayesian approach.