On Mon, Dec 30, 2013 at 3:57 PM, Blue Yogin <blue....@gmail.com
> On Mon, Dec 30, 2013, Alisa Zinov'yevna Rosenbaum <petrogradp...@gmail.com
>> On Mon, Dec 30, 2013 at 3:57 PM, Blue Yogin <blue....@gmail.com
>> Would you agree that, among the US population of the 1950s:
>> - Wearing earings was highly heritable?
> Technically this is also a matter for experiment. In order to find out
> whether it's heritable you need to isolate the genes from environment. You
> do this chiefly through studying identical twins who have been adopted into
> different families - these are called twin adoption studies. Twins who have
> been adopted have the same genes but different environment. This allows us
> to isolate the variables and find out how much of a phenotype can be
> explained by genetics. These twin studies have been done for a few hundred
> pairs of twins reared apart, the most famous study of which is the so-called
> Jim Twins study.
Yes. The ways in which twin studies isolate genes from the environment
are not straightforward or obvious; they involve complicated ideas,
and those ideas may be wrong. Studying the heritability of traits is
humans much more complicated than studying the heritability of traits
in varieties of corn.
> My guess is that wearing earrings is surprisingly
> heritable, because most things are surprisingly heritable, but again, that's
> a matter for experiment.
In the US in the 1950s, almost all women wore earrings, and almost no
men did. So, in that environment and among that population, variations
in the XX/XY chromosome would almost entirely account for the
variation in number of earrings worn, which would result in the trait
of wearing earrings being highly heritable among that population. In
the America of 2013, it's somewhat more common for men to wear
earrings, so I guess that trait would be less heritable for that
> - Number of digits (fingers + toes) was roughly 0% heritable?
> I don't know if dactyly variations are heritable. My guess is that some
> syndromes are and some aren't. Similar to above, you find this kind of
> information by finding families with histories of deviant dactyly, or if you
> get really lucky you find some identical twins with either one or both
> exhibiting the phenotype. If both twins have the phenotype, they are called
> concordant and it's likely genetic. If just one twin has the pheno, they are
> called discordant for that pheno and it's strong evidence against a genetic
> cause. (As an interesting aside, identical twins tend to be discordant for
> homosexuality, (75% of the time), which provides strong evidence that the
> root cause of homosexuality is not genetic.)
There are good explanations for why most of the variation in numbers
of fingers and toes are due not to genes but to the environment -
industrial accidents, mothers taking thalidomide during pregnancy,
etc. In light of this, wouldn't it be rather surprising if the number
of digits turned out to be highly heritable?
> In both of these cases, the question of heritability is a matter for experiment, not philosophy.
Doing experiments is a good way of evaluating some theories, but
experiments can be done in the wrong way, or give the wrong result the
first time and need to be repeated, etc. Philosophy helps us choose
which experiments to carry out, design them properly, and interpret
Identical twins would have been born at the same time, so they would
learn to talk in a roughly similar manner to the way everyone else in
the country did at that time. They were also born in the same place,
so unless they were reared apart, they would learn to talk from people
in the same general area. If they were reared apart, we would have to
consider that the families that make adoptions are not randomly
distributed - the fathers tend to have higher IQ, for one thing. Also,
maybe white children are more likely to be adopted by white parents,
and this would affect they way those children learned to talk. So it
there are a variety of explanations for why twins, even those raised
apart, would have more similar accents than two randomly selected
members of the population. We can try to control for all the relevant
factors in our experiments, but in order to decide what's relevant in
the first place, we need an explanation for the phenomena we are
> you're doing here is failing to appreciate the sophistication which modern
> statisticians bring to bear on the problem of separating correlation from
> causation. Yes there are a lot of bullshit "Eating bananas is correlated
> with diabetes!" type studies published in the science sections of mainstream
> dailies, but you can't hold that against the researchers. They have to make
> a living somehow!
We can't control everything that could possibly interfere with our
experiments, and so our experiments are necessarily inadequate.
Philosophy and our theories about the world help us decide which
things to control for and how to interpret the results of our
>>> It is the tribe's definition of "poisonous" that is "operational" - to use
>>> your terminology. The definition is "something is poisonous if eating it
>>> causes you to die."
>> There’s a lot hidden behind the word “causes”. All statistics can say is
>> that eating the berries is correlated with death, right? It’s up to our
>> interpretations/ideas/theories to make sure the causation is as we think it
> In this case, this is all that statistics can say. Statistics can also be
> used - in conjunction with sound experimental design - to tease apart
> correlation and causation.
Yes. My point is that explanatory theories are exactly what we need in
order to make a sound experimental design in the first place.
>>> The g theory of intelligence is much more well-developed, but you're willing
>>> to accept the red mushroom taboo and not the g theory.
>> I only said that if given their limited time and limited knowledge, maybe
>> not eating the mushrooms is the right thing. If they really need food and
>> seem to be the only food around, then maybe should try to understand what
>> causes the deaths of people who eat those mushrooms. If it's a farmer with a
>> gun, maybe we try to find a way to sneak over to the mushrooms at night. If
>> it's poison, maybe they look for an antidote, or maybe they look for some
>> different food. Does that count as “accepting the red mushroom taboo?"
> Why is not eating the mushrooms the right thing?
There's a philosophical field that deals with making choices about
what to do. I've heard this field referred to as "morality". One
principle of morality that's relevant here is: if no one has a good
reason why we shouldn't act on a theory, then it's OK to act on it.
> It must be because we've
> accepted the theory that mushrooms are poisonous as the best we've got right
Although the poisonous mushroom theory is the villagers' best theory,
it's also the ONLY viable theory they have for what killed their
fellows the other day. Coming to this conclusion requires all sorts of
knowledge about how the world works.
As an aside, it's usually the case that we have only one good theory
at a time for any particular phenomenon. Rarely do we have multiple,
competing good theories. Offhand, the only potential exception I can
think of is the case of quantum theory and general relativity. They
are both good theories and yet I am given to understand that they make
different predictions about the world at some scales. However, this
not a great example, because for any particular problem we care about,
we usually know which of the two theories we should use, so even then,
there's only one good theory in any given situation.
>> That didn't explain to me the difference between the kinds of interventions
>> that are warranted when all we know is that the trait is roughly 0% vs 100%
>> heritable. Did you mean that there is no difference between the kinds of
>> interventions in those two cases?
> In other words, 100% heritability of a trait means no intervention will make
> any difference.
>> I didn't say exactly 100%, I said roughly 100%, like the heritability for
>> number of digits. Does that number have any bearing on what interventions
>> be effective?
> The heritability figure gives upper limits on the size of the effect that an
> intervention can achieve.
Suppose we want to increase the number of people wearing earrings, and
let's stipulate that among the population of 1950s adult americans,
almost all women wear them and almost no men do, making the wearing of
earrings about 95% heritable. What can we conclude from this about the
upper limit on the size of effect that an intervention could achieve?
> We say a "heritability of 70%" and mean "70% of
> the variance in this population can be explained on the basis of genes
I agree that this is a common phrasing, but I quibble with the use of
"explained" there. I would prefer to say that "heritability of 70%"
means "For some trait within a particular fixed environment and
population, the ratio of variance in the relevant genes to variance in
the trait itself was 70%."
> We also are implying that genes caused 70% of the variance.
Isn't it a rather big leap to go from heritability to genetic causes?
Wearing earrings was highly heritable in the 50s, but that doesn't
mean the genes themselves CAUSED the variation. A better explanation
is that the variation was caused by cultural traditions and gender
> The point of making a distinction between heritability and environment is to
> tease out the causal mechanisms of the pheno. This means that if we say 70%
> heritable - and we're correct - only 30% of the variance, at best, is
> amenable to intervention. Roughly 100% heritable would mean roughly 0% of
> the variance is amenable to intervention.
I think it is a misconception to say that "roughly 100% heritable
would mean roughly 0% of the variance is amenable to intervention".
For example, the trait of being a slave in the U.S. south was highly
heritable in the mid 1800s, and yet that variance was eminently
amenable to intervention by the North.
To go back to the other examples from above, wearing earrings is
highly heritable, while number of digits should be almost totally
non-heritable. Yet I don't think this tells us anything about how
amenable to intervention each trait is.
> Ok, but my original usage was
> A statistician is perfectly happy to run a regression analysis and then
> assert that IQ explains 65±5% of a US citizen's lifetime earning capacity,
> without having a fully baked idea of why that might be so.
> And you seemed to take exception with that as not a normal usage.
Yes, good point. I didn't realize how common the "X explains Y"
terminology was among statisticians when I complained about it
>> I suggest that in this discussion we use "predictor
>> variables" for what statistics is talking about, and "explanations" for
>> explanations in the usual sense. Thoughts?
> Yes, that will work for me. Can you unfold the difference between
> explanation per se and prediction? I think that might be fruitful to help us
> agree on terminology. In statistics they are conflated, but here they seem
> distinct. This might be a key point.
An explanation is a model of the world, an idea about *why* things are
the way the are. It is an idea about what is actually happening in the
world, about the mechanisms that cause things to happen. An example
from The Beginning of Infinity is the axis-tilt explanation of seasons
A prediction, on the other hand, is simply an assertion about the
future. For example, one may predict that in the Northern hemisphere
next year, the average temperature will be lower in December than in
>> I would say, "Statistics, plus the ways of interpreting them, are *a*
>> way of quantifying the goodness or badness of a
>> theory along one particular axis."
>> Also, there are non-statistical ways to evaluate the relative goodness
>> or badness
>> of a theory. For example, the theory that "there exist an infinite
>> number of primes"
>> can be evaluated without reference to statistics.
> Math is special. I don't even think you can rightly call them theories.
When you say that we can't rightly call them theories, by "them" I
guess you mean "mathematical theorems we have proved." I think they
are still theories. First of all, we can be mistaken about them. There
are many examples of incorrect proofs. Also, mathematics has
conjectures, things we think are true but haven't yet proved. For
instance, there are a lot of explanations for why the
is true, even though it hasn't
been proven. Same for the
I would like to point out that in neither case would we use statistics
to evaluate how good the theory is.
> At any event, the epistemology of math is different the epistemology of pretty
> much everything else - wouldn't you agree?
Epistemology consists of general ideas about what knowledge is, how it
is created, how to evaluate it, etc. I think these general ideas
apply to all fields. There aren't any exceptions for math. Also, I'm
not sure if there is a good way to separate knowledge of math from
knowledge of physics. (Maybe there is, do you know of one?) For
instance, I can say that it's impossible to arrange 79 pebbles into a
rectangular grid, because 79 is prime. This is a falsifiable theory,
ostensibly about the real world, and yet we wouldn't use statistics to