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Archive for January 29th, 2012

There is a saying in the martial arts: “When the student is ready, the teacher will appear.”  I believe the saying has a more general application to life and to continuous education.
A little while back I was reading Wired Magazine and stumbled upon an article titled: “Trials and Errors: Why Science Is Failing Us“.   (The article was written by Jonah Lehrer and appears in the January 2012 issue.)
I found the article to be fascinating in itself, but also because it ties together with some of the other ideas I’ve been reading about in sports, science and philosophy: namely, we want to achieve intimate knowledge (“Grok”) by breaking processes down into fundamental pieces.  In science research this is known as “reductionism”.  Anyway, Lehrer’s article seems very harmonic with some of the other things I’ve been reading, so I’m offering up an (extensive) excerpt here, instead of just a couple of sentences as a quote.  I highly recommend reading the entire article for more insight…
…Because scientists understood the individual steps of the cholesterol pathway at such a precise level, they assumed they also understood how it worked as a whole.
This assumption — that understanding a system’s constituent parts means we also understand the causes within the system — is not limited to the pharmaceutical industry or even to biology.  It defines modern science. In general, we believe that the so-called problem of causation can be cured by more information, by our ceaseless accumulation of facts.  Scientists refer to this process as reductionism.  By breaking down a process, we can see how everything fits together; the complex mystery is distilled into a list of ingredients.  And so the question of cholesterol — what is its relationship to heart disease? — becomes a predictable loop of proteins tweaking proteins, acronyms altering one another.  Modern medicine is particularly reliant on this approach.  Every year, nearly $100 billion is invested in biomedical research in the US, all of it aimed at teasing apart the invisible bits of the body.  We assume that these new details will finally reveal the causes of illness, pinning our maladies on small molecules and errant snippets of DNA.  Once we find the cause, of course, we can begin working on a cure.
The problem with this assumption, however, is that causes are a strange kind of knowledge.  This was first pointed out by David Hume, the 18th-century Scottish philosopher.  Hume realized that, although people talk about causes as if they are real facts — tangible things that can be discovered — they’re actually not at all factual.  Instead, Hume said, every cause is just a slippery story, a catchy conjecture, a “lively conception produced by habit.”  When an apple falls from a tree, the cause is obvious: gravity.  Hume’s skeptical insight was that we don’t see gravity — we see only an object tugged toward the earth.  We look at X and then at Y, and invent a story about what happened in between.  We can measure facts, but a cause is not a fact — it’s a fiction that helps us make sense of facts.
The truth is, our stories about causation are shadowed by all sorts of mental shortcuts.  Most of the time, these shortcuts work well enough.  They allow us to hit fastballs, discover the law of gravity, and design wondrous technologies.  However, when it comes to reasoning about complex systems — say, the human body — these shortcuts go from being slickly efficient to outright misleading.
There’s a fundamental mismatch between how the world works and how we think about the world.
The good news is that, in the centuries since Hume, scientists have mostly managed to work around this mismatch as they’ve continued to discover new cause-and-effect relationships at a blistering pace.  This success is largely a tribute to the power of statistical correlation, which has allowed researchers to pirouette around the problem of causation.  Though scientists constantly remind themselves that mere correlation is not causation, if a correlation is clear and consistent, then they typically assume a cause has been found—that there really is some invisible association between the measurements.
Researchers have developed an impressive system for testing these correlations.  For the most part, they rely on an abstract measure known as statistical significance, invented by English mathematician Ronald Fisher in the 1920s.  This test defines a “significant” result as any data point that would be produced by chance less than 5 percent of the time.  While a significant result is no guarantee of truth, it’s widely seen as an important indicator of good data, a clue that the correlation is not a coincidence.
But here’s the bad news: The reliance on correlations has entered an age of diminishing returns.  At least two major factors contribute to this trend.  First, all of the easy causes have been found, which means that scientists are now forced to search for ever-subtler correlations, mining that mountain of facts for the tiniest of associations.  Is that a new cause?  Or just a statistical mistake?  The line is getting finer; science is getting harder.   Second — and this is the biggy — searching for correlations is a terrible way of dealing with the primary subject of much modern research: those complex networks at the center of life.  While correlations help us track the relationship between independent measurements, such as the link between smoking and cancer, they are much less effective at making sense of systems in which the variables cannot be isolated.  Such situations require that we understand every interaction before we can reliably understand any of them.  Given the byzantine nature of biology, this can often be a daunting hurdle, requiring that researchers map not only the complete cholesterol pathway but also the ways in which it is plugged into other pathways.  (The neglect of these secondary and even tertiary interactions begins to explain the failure of torcetrapib, which had unintended effects on blood pressure.  It also helps explain the success of Lipitor, which seems to have a secondary effect of reducing inflammation.)  Unfortunately, we often shrug off this dizzying intricacy, searching instead for the simplest of correlations.  It’s the cognitive equivalent of bringing a knife to a gunfight.
David Hume referred to causality as “the cement of the universe.”  He was being ironic, since he knew that this so-called cement was a hallucination, a tale we tell ourselves to make sense of events and observations.  No matter how precisely we knew a given system, Hume realized, its underlying causes would always remain mysterious, shadowed by error bars and uncertainty.  Although the scientific process tries to makes sense of problems by isolating every variable — imagining a blood vessel, say, if HDL alone were raised — reality doesn’t work like that.  Instead, we live in a world in which everything is knotted together, an impregnable tangle of causes and effects.  Even when a system is dissected into its basic parts, those parts are still influenced by a whirligig of forces we can’t understand or haven’t considered or don’t think matter.  Hamlet was right: There really are more things in heaven and Earth than are dreamt of in our philosophy.
This doesn’t mean that nothing can be known or that every causal story is equally problematic.  Some explanations clearly work better than others, which is why, thanks largely to improvements in public health, the average lifespan in the developed world continues to increase. (According to the Centers for Disease Control and Prevention, things like clean water and improved sanitation — and not necessarily advances in medical technology — accounted for at least 25 of the more than 30 years added to the lifespan of Americans during the 20th century.)  Although our reliance on statistical correlations has strict constraints — which limit modern research — those correlations have still managed to identify many essential risk factors, such as smoking and bad diets.
And yet, we must never forget that our causal beliefs are defined by their limitations.  For too long, we’ve pretended that the old problem of causality can be cured by our shiny new knowledge.  If only we devote more resources to research or dissect the system at a more fundamental level or search for ever more subtle correlations, we can discover how it all works.  But a cause is not a fact, and it never will be; the things we can see will always be bracketed by what we cannot.  And this is why, even when we know everything about everything, we’ll still be telling stories about why it happened.  It’s mystery all the way down.
There is a lot in there to think about – and even more in the entire article…
One final observation, while reviewing the article for this blog entry, I really noticed how much easier it is to follow Lehrer’s writing across various individual topics in Wired and in his other periodic publications.  This article was significantly longer than most of his columns in Wired.  It was really interesting to go back through the stream of his columns and recognize how much I’d enjoyed those “snipets” in a completely different context in the past without ever recognizing (consciously) they were the same author or the same theme.  In this case, with the monthly delay removed, I could recognize the spectrum of application of his own studies / observations across multiple “decision making” themes.  Now, when I get my new issues in the mail (yes, I still use “snail mail”), I can look forward to his columns like they are letters / updates from an old friend…
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If you are uncomfortable walking around your team’s workplace, awkward and out of place, you are a disconnected leader — not really part of the team.  Sitting in your office with the door closed and issuing edicts from on high is not communication, and is certainly not collaborative leadership.
  —  Bill Walsh
From his book:  “The Score Takes Care Of Itself
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