The Blind Men and the Elephant -- a post-modern parable

It's an ancient parable; a group of blind men are lead to an elephant and asked to describe what they feel.  One feels a tusk, another a foot, a third the tail, and so on, and of course they disagree entirely about what it is they are feeling. This tale is usually used as an illustration of the subjectivity of our view of reality, but I think it's more than that.

I heard a talk by Anthropologist Agustin Fuentes here at Penn State the other day, on blurring the boundaries between science and the humanities.  He used the parable to illustrate why science needs the humanities and vice versa; each restricted view of the world is enhanced by the other to become complete.

But, this assumes that the tales that science tells, and the tales that the humanities tell are separate but equally true -- scientists feel the tail, humanities feel the tusk and accurately report what they feel.  Once they listen to each other's tales, they can describe the whole elephant.

"Blind monks examining an elephant" by Hanabusa Itchō (Wikipedia)

But I don't think so.  I don't think that all that scientists are missing is a humanities perspective, and vice versa.  I think in a very real sense we're all blind all of the time, and there's no way to know what we're missing and when it matters.  You feel the tusk, and you might be able to describe it, but you have no clue what it's made of.  Or, you feel the tail but you have no idea what the elephant uses it for, if anything.

Here's my own personal version of the same parable -- some years ago we purchased a new landline with answering machine.  Oddly, we have a lot of power outages here, and it seemed that every time I set the time and day on the answering machine, we'd have another outage and the time and day would disappear, having to be set once again.  I decided that was a nuisance, and I stopped setting time and day.

The next time the machine said we had a message, I listened to it, but it was blank. There was no message!  Naturally enough (I thought), I concluded that the time and day had to be set for the machine to record a message.  Unhappy consumers, we contacted the maker, and they said no, the machine should record the message anyway.  Which of course it would have if the caller had left a message, as was proven the next time someone called on unknown day at unknown time and ... left a message.

My conclusion was reasonable enough for the data I had, right? It just happened not to be based on adequate data (aka reality).  But, we always think we've got enough data to draw a conclusion, no matter how much we're in fact missing.  This is true in epidemiology, genetics, medical testing, the humanities, interpersonal relationships; we think we know enough about our partner to commit to marrying him or her, but half of us turn out to be wrong.  Indeed, if all you've seen are white swans, you'll conclude that all swans are white -- until you see your first black one.

No, you say, we did power tests and we know we've got enough subjects to conclude that gene X causes disease Y.  But, it's possible that all your subjects are from western Europe, or even better, England, say, and what you've done is identify a gene everyone shares because they share a demographic history.  You won't know that until you look at people with the same disease from a different part of the world -- until you collect more data.  Until you see your first black swan.

But, you say, no one would make such an elementary mistake now -- you've drawn you controls from the same population, and they will share the same population-specific allele, so differences between cases and controls will be disease-specific.  But, western Europe is a big area, and even England is heterogeneous, and it's possible that everyone with your disease is more closely related than people without.  So, you really might have identified population structure rather than a disease allele but you can't know, until you collect more data -- you look at additional populations, or more people in the same population.

Even then, say you look at additional populations and you don't find the same supposedly causal allele.  You can't know why -- is it causal in one population and not another?  Is it not causal in any population, and your initial finding merely an artifact of ill-conceived study design?

Without belaboring this particular example any further, I hope the point is clear.  You feel the tail, but that doesn't tell you everything about the tail.  But you can't know what you're missing until you ask more questions, and gather more data.

Darwin explained inheritance with his idea of gemmules.  He was wrong, of course, but he had no way to know how or why, and it wasn't until Mendel's work was rediscovered in 1900 that people could move on.  Everything we know about genetics we've learned since then, but that doesn't mean we know everything about genetics.  But theories of inheritance (and much else) don't include acknowledgement of glaring holes: "My theory is obviously inadequate because, as always, there is a lot we don't yet understand but we don't know what that is so I'm leaving gaps, but I don't know how big or how many."  And, in a related issue that we write about frequently here, it's also true that instead of coming clean, we often claim more than we know (and often we know what we're doing in doing so).

Even very sophisticated theories just 15 or 20 years ago had no way to include, say, epigenetics, or the importance of transcribed but untranslated RNAs (that is, RNA not coding for genes but doing a variety of other things, some of them still unknown), or interfering RNAs, and so on, and we have no idea today what we'll learn tomorrow.  But, like the blind men, we act as though we can draw adequate conclusions from the data we've got.

Science is about pushing into the unknown.  But, because it's unknown, we have no idea how far we need to push.  I think in most cases, there's always further, we're never done, but we often labor under the illusion that we are.  Or, that we're close.

But, should ductal cancer in situ, a form of breast cancer, be treated?  And how will we know for sure?  Systems biology sounds like a great idea, but how will we ever know we've taken enough of a given system into account to explain what we're trying to explain?  Will physicists ever know whether the multiverse, or the symmetry theory is correct (whatever those elusive ideas actually mean!)?

Phlogiston was once real, as were miasma and phrenology, the four humors, and the health benefits of smoking.  It's not that we don't make progress -- we do now know that smoking is bad for our health (even if only 10% of smokers get lung cancer; ok, smoking is associated with a lot of other diseases as well, so better not to smoke) -- but we've always got the modern equivalent of phlogiston and phrenology.  We just don't know which they are.  We're still groping the elephant in the dark.