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DesignIssues: Volume 30, Number 3 Summer 2014
104
Correspondence:
Incremental Radical Innovation
John Z. Langrish
A Response to Donald A. Norman and Roberto
Verganti’s “Incremental and Radical Innovation:
Design Research vs. Technology and Meaning
Cha nge,” Design Issues 30, no. 1 (Winter 2014): 78–96.
I found the article, “Incremental and Radical Innova-
tion,” by Norman & Verganti (Winter 2014, 78-96) to be
very interesting but also puzzling. Why do the authors
make no reference to the concept of local maxima as it
occurs in evolutionary biology?
The basic idea is that in searching for better ways
of existing and propagating, entities can climb up a
hill of gradual improvement until they reach the top
where further small changes will only move down-
ward. The hilltop is called a local maximum because
there may be other peaks that are higher; but, how can
you reach these other peaks?
This idea has been around for more than 100
years and is not just limited to the academic literature.
Richard Dawkins’ book, Climbing Mount Improbable,
provides a popular account in which there is a mount
with a steep cliff face, almost impossible to climb.
1
Out
of sight, however, is a sloping path that reaches the top
in gradual steps.
The idea of local maxima in human design
cropped up in 1862 when Eilert Sundt (1817–1875), a
Norwegian sociologist, visited England and was
impressed by Charles Darwin. He wrote a paper giv-
ing a Darwinian model of gradual technical change
involving accidental changes, perceived improve-
ments, choice, etc. In his account of shipbuilding, he
concludes with the idea of experiments, “when the
idea of new and improved forms had first been
aroused, then a long series of prudent experiments,
each involving extremely small changes, could lead to
the happy result that from the boat constructor’s shed
there emerged a boat whose like all would desire.”
This gradual series of changes comes to a stop
when, “Each kind of improvement has progressed to
the point where further developments would entail
defects that would more than offset the advantage.2
The problem of getting stuck on a point had to
be tackled by evolutionary theorists, and maybe the
reason for the authors ignoring their ideas is that they
came to different conclusions.
Norman & Verganti claim that radical innovation
needs a different mechanism from the gradual climb-
ing of a slope but Darwinian theorists don’t agree.
In ‘Origin’, Darwin wrote, “If it could be demonstrated
that any complex organ existed which could not
possibly have been formed by numerous successive,
slight modifications my theory would absolutely
brea k dow n.”3
You can’t be much clearer than that. For “complex
organ,” substitute “complex system” or “radical inno-
vation” and it becomes obvious that Darwinians have
to find a way out of being stuck on a peak. In fact,
there are many ways out.
The ridge. Something that seems to be a peak
when viewed from one side may actually be
connected to another apparent peak by a
“ridge,” allowing movement from one peak
to another.
Symbiosis. Entities that have climbed
different peaks may find a ridge that enables
them to get together; this is what philosopher
Daniel Dennett calls the improvement of an
entity through joining with something
“designed elsewhere” (i.e., it has climbed up
a different slope).
Twin peaks. Entities climbing up a slope of
improvement may find that further advances
lead in different directions so that they end up
on different peaks. This is how evolutionary
biology proceeds with one species dividing
into two or many new species. Common
ancestry can also be found in technological
change. An entity stuck on a peak may be
overtaken by a “cousin” climbing a different
and higher peak.
Change in the landscape. Changes in the
“rules” of competition can be visualized as
a geographic upheaval. At one time the
major upward direction for aircraft was
speed. Concorde got stuck at the top of this
peak. It was the fastest but not the “best.”
The landscape had changed with cost per
doi:10.1162/DESI_c_00286
DesignIssues: Volume 30, Number 3 Summer 2014 105105105
passenger being the dominating peak.
Major upheavals can be compared with the
arrival of a new volcanic island emerging
out of the sea. Steam engines and the internal
combustion engine were new to the scene,
but they emerged slowly and developed
many ridges to existing peaks.
Any new form of living entity or artifact
arrives via a series of small steps. In the real
world, there are many more dimensions
than in the simple geological metaphor.
More dimensions increase the probability
of connections between peaks.4
I am aware that the size of technological
change is important, and in 1970 I devised
a scale for the size of a technological
innovation. This was a five-point scale
based on the change required in a standard
technical textbook. Size five represented
the need for a new book and size four
meant a new chapter in the textbook.
From a study of British innovations that
had gained the Queen’s Award for Industrial
Innovation, I obtained evidence that sizes
four and five seemed to happen in different
ways from those with smaller changes.5
However, even these large changes had
gradually climbed up their own historical
peaks and continued to climb once they had
combined with other things.
George Basalla’s classic study, The Evolution of
Technology, attempts to demonstrate the continuity
of all technology right back to the use of the first
stones and flints, “every new artefact is based to
some degree upon a related existing artefact.” Even
the transistor climbed out of earlier solid-state ampli-
fiers, used in the “crystal” radio sets.
6
As Norman &
Verganti rightly state, “a completely novel innovation
is impossible. All ideas have predecessors and are
always based on previous work - sometimes through
refinement, sometimes through a novel combination
of several pre-existing ideas.”
This being so, where then is radical innova-
tion and how is it different from hill climbing in a
rugged terrain with many peaks connected by ridges?
Perhaps it is a matter of semantics with radical—not
meaning very radical— or perhaps it is a reluctance to
follow the Darwinian path up the hill of explanations
of innovation.
By seeming to support the idea that some inno-
vations are not obtained by gradual hill climbing,
they are in danger of giving support to the so-called
intelligent design movement. This offshoot of the
creationists seeks to show the impossibility of some
biological innovations being the result of Darwinian
gradual change. “What is the use of half a wing?”
etc. (The fossil record shows that both “wings” and
feathers existed before they were used for flight; birds’
bodies are covered in feathers, not just their wings.)
Many apparently radical innovations in both biology
and in technology can be shown to make use of
previously existing systems that were used for a dif-
ferent purpose.
Given Norman & Verganti’s claim that “a com-
pletely novel innovation is impossible,” why don’t
they stick with Darwin? They claim, “All ideas have
predecessors” and ideas can form the basis of a
Darwinian approach to design. Ideas can be thought
of as memes—imperfect replicators—existing as
electro-chemical neuronal patterns in the brain. A pre-
vious paper in this journal shows that different types
of memes can be used in discussing Darwinian
design. Norman & Verganti’s two dimensions of tech-
nology and meaning can be thought of as two kinds
of memes: recipemes—ideas about how to do things,
and selectemes—ideas about what sort of things you
want to do. The desire to travel faster is a selecteme;
the idea of supersonic transport is a recipeme.7
I don’t believe that Norman & Verganti are
closet creationists, and I don’t believe that they are
ignorant of biological ideas of local maxima. So why
do they omit any mention of biological maxima, and
why do they want to insist that radical innovations
don’t arrive through a series of small changes?
1 Richard Dawkins, Climbing Mount Improbable (Harmondsworth,
Middlesex, England: Penguin, 1996).
2 Eilert Sundt, paper in Norwegian, 1862, translated in Jon Elster,
Explaining Technical Change (Cambridge University Press, 1983),
136–37.
3 Charles Darwin, The Origin of Species By Means of Natural
Selection 6th edition, (London: J Murray, 1859), 58, 137.
DesignIssues: Volume 30, Number 3 Summer 2014
106
4 Sergey Gavrilets, Fitness Landscapes and the Origin of Species
(Princeton University Press, 2004).
5 John Langrish et al., Wealth from Knowledge: Studies of Industrial
Innovation, Part Two (London: Macmillan, 1972).
6 George Basalla, The Evolution of Technology (Cambridge
University Press, 1988).
7 John Z. Langrish, “Darwinian Design: The Memetic Evolution of
Design Ideas,” Design Issues 20, no. 4 (Autumn 2004). See also
J. Z. Langrish, “Different Types of Memes: Recipemes, Selectemes
and Explanemes,” Journal of Memetics 3, (1999). http://cfpm.org/
jom-emit/1999/vol3/langrish_jz.html (accessed February 2, 2014).
Donald A. Norman and Roberto Verganti
Hill Climbing and Darwinian Evolution:
A Response to John Langrish
We find John Langrish’s argument to be puzzling. We
wrote a paper on product evolution and he chides us
for failure to cite the literature in evolutionary biology.
The issue is our discussion of optimization
through small, local iterations—each iteration mov-
ing in the direction that yields an improvement, stop-
ping when all further changes lead to a decrement.
This is a well-known technique, widely understood
and discussed in a wide variety of disciplines. The
mathematics are well studied. There are numerous
variants of this method, such as gradient descent and
ascent, hill climbing, and simulated annealing. For
the purpose of this response, let us call them all “hill
climbing.” All these methods lead to local optimiza-
tion but are incapable of finding a global optimiza-
tion. This is an indisputable fact of mathematics, very
widely-known, and in our opinion so well known
that they are not necessary to cite nor are they open
to discussion.
As Langrish properly points out, Darwin
considered evolution to be a kind of local optimiza-
tion process. Moreover, he was aware of the difficulty
of reaching a global optimum. So how does evolution
work? Langrish seems to think that Darwin assumed
that this method had to work, else his theory “would
absolutely break down.” Langrish cites this as
evidence that we are wrong in our assessment of the
imitation of hill-climbing optimization. Langrish’s
quotation of Darwin’s statement was a hope not
a proof.
The problem of reaching global optimization
has been well-studied, once again, in many disci-
plines. A simple solution is to use different starting
points, and if the space of possible starting points is
well-covered, then one is likely to lead to a global
optimization. This is precisely what we said in our
paper. We stated that by starting at a different point
in space, driven either through new technological
possibilities or meaning change, the new starting
point can lead to a superior solution. Indeed, this
is what Langrish himself partially suggests in his
list of possible candidates for solving the dilemma.
Multiple entities climbing at the same time but tak-
ing different routes (his suggestion 3) are examples
of different starting points. His suggestion of
changes in the landscape (his suggestion 4), is what
we mean by new technological innovations or
changes in meaning.
But many of his suggestions are rather bizarre,
perhaps because he is not aware of the underlying
mathematics. Suggestions 1 and 2, that a ridge might
connect local peaks, do not solve the problem of hav-
ing to descend (de-optimize) in order to traverse the
ridge, unless there is a path across the ridge that does
not require a descent. Hill climbing methods fail if
the ridge requires any decrease in value, but they
will succeed if the ridge never entails a decrease.
Note that it is possible to traverse ridges that require
some de-optimization through any one of a number
of stochastic optimization methods. These are also
well–studied in the literature on optimization, but
these are not the methods we discuss in our paper
because we did not believe they would apply to the
normal process of invention and improvement.
Langrish’s suggestion 5 is correct but irrelevant.
We assume the full space of possibilities, namely the
space existing in the world. That is, we do not assume
a simple-minded geological metaphor. Actually, add-
ing dimensions also increases the likelihood of mul-
tiple local maxima: more places to get stuck.
We stated that all radical innovations do
come from pre-existing ideas and innovations.
So how do they combine if not by local incremental
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DesignIssues: Volume 30, Number 3 Summer 2014 105107107
optimization? By novel combinations, that’s how.
We proposed that these novel combinations are done
through tinkering, through systematic trial and
error, through accidents, through a deliberate design
act, or through whatever events transpire. New tech-
nologies and new meanings provide new starting
points as well as novel combinations. The formation
of these combinations does not arise through hill
climbing nor optimization mechanisms. Once the
combination is assembled, then a hill-climbing
process begins to determine if the new combination
will survive or not, and then whether it can climb the
hill to an optimization point. (This is precisely how
genetic algorithms work: they randomly combine
features of winning organisms, creating new novel
transformation.) Some proposals for the mechanisms
of biological evolution are similar. In addition,
biological mechanisms probably are stochastic
because of the existence of “noise” and probably
follow optimization processes, whether through
noise (stochastic processes), the mixing of genes
(as in genetic algorithms), multiple starting points
(as Langrish points out), or any one of the multitude
of well-known ways of modifying simple hill climb-
ing techniques.
Unfortunately, Langrish does not clarify his
perspective for optimization: who is surviving, who
is succeeding? Optimization from the perspective of
the species may not lead to optimization for the
world. Optimization from the perspective of the
world probably leads to species that get stuck in local
maxima and therefore die, or in Langrish’s words,
are “overtaken by a ‘cousin’ climbing a different and
higher peak.” We look from the perspective of the
individual entity, for example, an organization or a
firm. Of course the socio-economic system evolves
and survives, but individual firms and organizations
that get stuck in an old pattern of local maxima
disappear (consider Olivetti, Polaroid, Digital Equip-
ment Corporation, etc.), overtaken by organizations
that abandon the path-climbing process of their
industry and find new combinations. Although, in
this Schumpeterian mechanism of creative destruc-
tion, new organizations may come from the ashes of
previous ones, the old entity is definitely not happy
to disappear.
In the published literature on the economics
and business of innovation and technological change,
the concepts of local maxima and path dependence
are well studied and the importance of disruption as
a strategy for success is a well-known mechanism.
We refer to this body of literature in our citations of
studies of radical innovation (see our notes 15 and
16—in particular, the work of Clayton Christensen),
and then when we acknowledge Giovanni Dosi, a
well-known evolutionary economist (note 26). We
connect these theories to our discussion of design re-
search in relation to drivers of change such as tech-
nology and meaning.
We thank Langrish for his interest in our
paper, but similar issues have been faced in many
disciplines. As we have demonstrated, his attempts
to map biological mechanisms to our approach are
either already accounted for (his suggestions 3 and
4) or are inappropriate (his suggestions 1, 2, and 5).
We see no reason why we should have cited every
field that has thought about problems of local versus
global optimization; and, we see no reason to modify
our suggestions based upon his analyses.
We are accused of being creationists. We plead
guilty. That’s what the field of design is all about:
all-seeing, overarching designers who look over their
creations and go in and change them. Designers have
that luxury. Release a product and call it back for
revision. Or completely change the next release,
keeping the stuff that worked and deleting the stuff
that didn’t. Or completely repurpose it for some other
usage that had not been considered at first. None of
this incremental creep that evolution must suffer
through: designers get rid of the appendix when it is
no long needed. Designers are creators.
Radical innovation in the field of design does
not come from hill climbing. It comes from putting
together things that never before were thought to
belong together. It comes from the heart and mind of
the designer. Yes, as designers we are creationists.
We teach it, practice it, and take delight in it.
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