Science is the art of taking no chances.
Wonderful weekend ahead,
Best regards,
Eduardo Corte-Real

On 10-02-2012 16:50, Don Norman wrote:
> I thought it might be both useful to look briefly at several places
> where science fails. In my attempts to understand phenomena and
> fields, I always prefer a balanced approach, one that looks at the
> strengths and weaknesses, at the virtues and the flaws.
>
> 1. Science makes progress through measurement, but not everything can
> be measured
>
> I once stated, "Science measures what it can measure and defines the
> rest to be unimportant."  Many others have said similar things, often
> much more strongly.   Einstein, for example said "Not everything that
> can be counted counts, and not everything that counts can be counted."
> (Footnote 1)
>
> There is an attempt to use precision, even if the thing that is being
> measured is not really what is of concern. In Finance, for example,
> the riskiness of a stock is specified as a single number, Beta, that
> refers to the ratio of the variability of an asset to its portfolio
> divided by the variability of the portfolio (Footnote 2). In fact,
> this fails to capture what most of us would think of as risk. Risk to
> most of us is not simply variability. But the financial community
> likes it because it is a simple measure that is easy to compute and
> makes the math work.  See footnote 2.
>
> In general, science has limitations on measurement, so it measures
> what it can, often defining that to be the item of interest. In the
> first few years of the method, it is roundly criticized, but as time
> passes it becomes accepted and people no longer think to question it.
>
> 2. Oversimplification.
>
> For numerous reasons, science dramatically oversimplifies the
> phenomena under study. The major reasons have to do with measurement,
> control, and mathematics.
>
> A. Oversimplification caused by measurement
>
> Science measures what it can, and quite often this is very restricted.
> See Footnote 1.  I see this in the practical problem of design. In the
> development of intelligent automobile automation, the automation needs
> to know how slippery the road is, so the engineers look at the
> automobile wipers: if on, it must be raining. This is a 0th order
> approximation to truth and dangerous, because roads are most slippery
> when roads are damp, caused by light drizzle. Many drivers do not yet
> use their wipers. (Most dangerous because light rain brings out the
> oil in the road. heavy rain washes the oil away.)
>
> It is easy to find situations where a simplified measurement is taken
> to represent a complex situation. SOmetimes this is a good
> approximation. Often it works well in the laboratory, but fails in the
> world.
>
>
> B. Oversimplification caused by control.
>
> It is very important to be able to control the phenomena under study.
> This is why so many studies are done in the laboratory, where all the
> variables defined to be extraneous and irrelevant can be controlled
> (temperature, humidity, light, noise, ... ). In the human and social
> sciences, this means putting people in extremely unnatural situations
> and asking them unnatural questions, questions they would not normally
> encounter, or even if they encountered them, they would be deeply
> embedded in a rich context.
>
> B. Oversimplification caused by mathematics
>
> Mathematics is a powerful tool in enabling precise explanations and
> predictions. But mathematics can get quite complex very rapidly. Until
> recently, non-linear systems could not be handled, so everything was
> linearized. Most important real phenomena are non-linear, which means
> that much of theory was a simple approximation to reality over a
> restricted range or, in the case of wildly non-linear phenomena, that
> they couldn't be studied.
>
> Economics is a good example of the dangers of oversimplification. The
> fundamental assumptions in much of economic theory are logical and
> sensible, but quite wrong as accurate descriptors of real human or
> institutional behavior. Nonetheless, they greatly simplify the
> calculations. As a result, much of the edifice of economics is built
> on false premises. This fact has been known for decades (Herb Simon,
> among others, made this point. But even when he got the Nobel for this
> work, the economists ignored him. "What a waste of the prize," one
> economist told me. I repeated the quote to Herb, who smile patiently.)
> Now, with a second Nobel awarded to yet another truth-sayer (Danny
> Kahneman) are mainstream economists starting to take this seriously.
> (See item 4, below: Scientists are human.)
>
> Today the maths are more powerful and computer simulation has added to
> the power of formal models, but the need for simplification still
> exists. But there is hope. The power of computers and the huge
> databases that can now be assessed, plus the power of modern
> visualization tools means that we can start looking at things a
> complex, interdependent, systems (and dynamical systems), which is
> much more appropriate for many of the real problems that we face.
> These are still simplifications, but nonetheless, an improvement.
>
> If you want to see some extreme simplifications in the field of
> design, I point you to such works as Axiomatic Design. (Footnote 3)
>
>
> 3. Paradigms, Frameworks, and Fads
>
> As philosophers of science like to point out (my favorite writer on
> the topic is Bruno Latour), scientists are human (see point 4, below),
> and subject to all sorts of fads.  Each generation of science has some
> accepted paradigm, and studies that go outside the framework of that
> paradigm are often ignored, or rejected by the high prestige journals.
> Fads come and go.  Every so often, the young Turks come along and
> introduce a new paradigm. This takes decades, and many die along the
> way. Worse, once the new paradigm is accepted, it starts to explain
> whole new phenomena (which is good), but often ignores a lot that had
> been learned in previous paradigms (which is bad).   I have seen
> numerous such shifts.
>
> I was a bystander watching this young kid Noam Chomsky overthrow
> linguistics to the point where for a while much expertise in languages
> was ignored in favor of theories of highly abstract and simplified
> syntactical constructions (completely ignoring the difference between
> written and spoken language).  Molecular biology threw out the
> systemic and general biologists who studied real animals and plants. I
> was one of the young Turks who threw out behaviorism in psychology in
> favor of, in temporal order, information processing, cognitive
> psychology, cognitive science, cognitive neuroscience. Then came
> connectionism (born in the office next door to mine) and dynamical
> systems.  I am now one of those old fogies whose work has been
> overthrown by todays young Turks. (Which is as it should be.)
>
> 4. Scientists are human
>
> Scientists are people, subject to the same failures as the rest of us,
> so as they jockey for recognition and power, they tend to exaggerate
> the importance of their findings and, much worse, conceal data, and
> then even far worse, lie and fabricate. Scientists form cliques in the
> honorary societies and in grant awards, where they reinforce their
> colleagues and keep out their dissenters (or people who follow other,
> opposing paradigms).  Science is supposed to be open-minded. In fact,
> science is one of the most conservative of fields.  In many ways that
> is good, because it does help prevent the rush to the latest fad. But
> having a closed mind is never a good thing.
>
> 5. Although the scientific method means that science is
> self-correcting, it may take decades, generations, or even centuries
> for the process to play out for any given topic.
>
> Yes, the scientific method is open and testable, which means that
> eventually it converges upon an accurate description. But this process
> might be too slow to help those who are caught in the pettiness.
>
> Footnotes:
>
> 1. In my Google search to get the Einstein quote right I came across a
> wonderful article about the powers and dangers of measurement. It
> applies very much to the debate on this discussion group:
> "Measurement, a blessing and a curse."
> http://www.medrants.com/archives/2968
>
> 2. Beta as a measure of risk. See the Wikipedia article, especially
> the section called "Criticism":
> http://en.wikipedia.org/wiki/Beta_(finance)#Criticism
>
> 3. An excellent introduction to and comparison of Axiomatic Design and
> Triz can be found at
> http://www.triz-journal.com/archives/2000/08/d/index.htm
>
>
> Don Norman
> Nielsen Norman Group
> [log in to unmask]   www.jnd.org http://www.core77.com/blog/columns/
> IDEO Fellow. Latest book: "Living with Complexity"
>