Dear Rosan and Ken and Gunnar and all,
There is a different and potentially more fruitful way of viewing this
situation - taking an overview of how the situation is analysed, i.e. a
meta-analysis.
What follows is a kind of overview of 'meta-analysis of theory-making'
about innovation. (I've now learned to draw attention to when something is a
meta-analysis of theory. Hopefully to avoid it being seen as being about
application of particular theories - a PhD student/colleague has just had
a paper bounced because of that misunderstanding!). It's usually a useful
thing to do in any design situation where there is some contraditions...
One thing often overlooked is that all disciplines use the same structure
for their theory models. In fact, all disciplines use the same theories;
with different issues such as innovation, price, aesthetics, radiation,
social capital etc being fitted into the same theory structures . One can
easily map 'all theory models across all disciplines' as simple
combinations of theory building bricks. These are found much the same in
all disciplines. There is a limited number of these theory structures and it
isn't that large.
In other words, theories in most disciplines are much the same, except for
some variation between disciplines in the average level of complication of
theory structures that they use.
Different disciplines use slightly different selections from these 'theory
blocks, but there is substantial overlap in basic theory models across all
disciplines.
I suggest the above similarity of theory across all disciplines is simply
the result of the biologically limitations of human thinking. We make
similar theories across disciplines because we are all humans and humans
think this way. Yes, I know, it's tempting to feel the similarity of theory
across disciplines is from some deep mystical thread connecting all aspects
of the world. It just doesn't make as much sense as that its limitation of
humans - after all it's humans that make up the theories, not the world.
So across all disciplines, we all use the same bunch of theory 'patterns'
that can be roughly divided into three different levels of complexity
* Some are really simple (e.g. the pattern found in 'equals',
balance, justice, equations, weight of evidence...)
* Some are a little more complicated (e.g. multiplication, the idea
of 'factors' acting on a situation, simple reversibility, linear modelling,
adding components, simple additive combinations between models, models that
use variables that have multiple dimensions, .g. multi-dimensional vectors,
)
* Some theory patterns are much more complex (e.g. theory patterns
in in which different factors transform each other and themselves in ways
that vary across space and time or even across abstract aspects of reality)
Theories used to model design and innovation, for example, typically use
theory patterns from the lower (simpler) end of the middle group.
Descriptions of innovation activities/processes and types of organisation
(vertical integrated/distributed/star etc), and theories of causality fall
mainly into these simpler middlish kinds of linear categories of theory. I
suggest the reason they often don't work very well is primarily because they
are too simple for the situations being theorised about.
From observation, however, the problem here is more about people and
education than about how well particular theories fit situations. Which
theory patterns get used in particular disciplines depends more on the
education and skills of the people in the discipline than on whether the
theories fit the world. Using complicated theories is difficult! Mostly, it
requires special education. Using *really* complicated theories is hard and
usually requires strong ability in mathematics (degree level and beyond).
Typically, those who have found out how to make theories that predict and
explain reality have done it the hard way. The difference between that and
the easy way is that the hard way works (thanks TP).
I suggest the reason the simpler theory patterns get used in design
research is mainly because of human limits in which people are not
comfortable with using more complex theory structures. Thus the question of
innovation and design and whether and which Bell Labs, Facebook and IBM
illustrate whether innovation is best described as linear or due to
oppressive genius, is a furphy in light of the types of theory being used.
If the theory isn't complex enough to represent the situation, then no
amount of careful choosing will solve the problem ('will a half litre glass
or pint glass best hold this 20,000 litres of orange juice...').
The overall problem is perhaps easiest understood in terms of Ashby's Law
of Requisite Variety in which control system (theory model) variety has to
be at least as great as system variety before starting to discuss which
control system (theory model) is the best choice.
This leaves the field of design in an unusual position. The theories that
are used are not complex enough to do justice to the situations being
discussed , and most of us doing the discussing haven't been educated to use
theory patterns with the complexity necessary (of course there is always the
option to try to force the situation into the theory by
simplification...aargh.).
Fortunately, the world has a discipline that specialises in the abstract
understanding of theory patterns - mathematics. This is not exactly the
complete box and dice as mathematics gets a bit fuzzy round the edges about
the relation between the theory patterns and the real world bits that are
being represented and that also needs some good skills in epistemology and
ontology. Maths, however, gives access to the treasure chest of theory
patterns and how to use them. People can simply open the chest and revel
in the treasures to improve their creative understanding of situations.
So where does this go in terms of your problem and questions about
theories about comparative evaluation of the drivers and cause of
innovation (and Bell Labs and Facebook ) ? (...perhaps IBM would have been
a better choice...?).
First, it suggests there may be some benefits in looking at the situation in
terms of the complexity of the theory patterns that are being used to
analyse the situation and whether they are complex enough for what you are
trying to do - to me it looks like they are not.
Second, it suggests a need to look with an epistemological eye to de-messify
the epistemological problems about the concept of innovation and the
surrogates it is measured by.
All of this points to the same conclusions as Don Norman proposed: design
researchers need way more maths education.
Best wishes,
Terry
==
Dr Terence Love
Love Services Pty Ltd
PO Box 226, Quinns Rocks
Western Australia 6030
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+61 (0)4 3497 5848
==
-----Original Message-----
From: PhD-Design - This list is for discussion of PhD studies and related
research in Design [mailto:[log in to unmask]] On Behalf Of Rosan
Chow
Sent: Thursday, 1 March 2012 4:52 PM
To: Dr Terence Love
Subject: Re: more than the place where Claude Shannon and William Shockley
worked
Hi Gunnar,
As you know I have been reading, let me try to see what I retain from the
readings: (this is an exercise for me)
This story of the Bell Labs might be said to be based on the so-called
linear model of innovation (basic research, applied research, development &
innovation). This model, however, has been challenged, someone please helps
here.
As everybody knows, invention is not the same as innovation. Innovation,
understood today as commercialization of new/novel technology was made by
Rupert Maclaurin (and not J.Schumpeter, Surprise!) (Bodin 2008
http://www.csiic.ca/PDF/IntellectualNo2.pdf ).
On the one hand, I think the author has a point - to challenge how
innovation is defined and understood. (For a fascinating read for a history
of innovation as a category, again Bodin 2008
http://www.csiic.ca/PDF/IntellectualNo1.pdf ).
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