Dear all
I'm on a iPad making it Hard to respond at any length. But it's important to add this to what don has written.
It is measurement, and I take all the points made here. However, scientific method (yes, there's one) allows for, and necessitates, the identification or description of objects and relations between objects, and neither is a process of measurement. The objects could be -- thinking of Feynman's lecture -- sand on the beach, and rocks, and the question.is whether the rocks are "other than" the sand etc.
The process of determining whether or not this is the case can be falsified once the set of criteria have been established, These aren't mere assumptions but the roadmap of thought needed to make sense of claims about the world. That's why all science has to grounded on some philosophy of science. and while the philosophy of science is debated, it is not debated to the point of reducio ad absurdum (or, as I prefer, reducio ad nauseum).
I can't talk about action research on an iPad bu I will say this. If you come to phenomena with a Pre-existing interpretative framework -- such as, it's all about power, or it's all about gender -- then you cannot be engaged in science. Why? because your claims cannot be falsified based on evidence, In other words, all observations confirm, by fiat, one's ready interpretation. This can lead to infuriatation in daily life too lest anyone think this is all super intellectual silliness, as in:
"You are repressed."
"No I am not."
"See? That proves it."
replicability needs to serve FALSIFIABILITY which Don sort of suggested but didn't really land on, and that's why we replicate. and in replicating and failing to falsify, we don't so much prove that something is right as fail to prove it is wrong and therefore gain confidence, until the very paradigm of understanding e world comes to be overturned through a "revolution" in scientifiic thought. But this does not disprove everything before it. Lightbulbs worked before and after quantum theory.
Lastly, there ARE empirical and scientific methods of qualitative research, and That is precisely my own training. As Clifford Geertz said in Local Knowledge, it is not an experimental science but an interpretive science in search of meaning. That can be done scientifically.
It al depends on what we a trying to accomplish! Design does not have to be scientific! But if we make claims that purport to be science, then it does.
Sent from my iPad
On Feb 10, 2012, at 5:50 PM, Don Norman <[log in to unmask]> 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"
|