EU-US SEMINAR: NEW TECHNOLOGY FORESIGHT, FORECASTING & ASSESSMENT METHODS-Seville 13-14 May 2004
Paper 2 : Evolutionary theory of technological change: State-of-the-art
and new approaches
Tessaleno C. Devezas
Technological Forecasting and
Innovation Theory Working Group
University of Beira Interior
Covilhã- PORTUGAL
I. Introductory thoughts
The main objective of this seminar concerns the exploitation of the powerful new
capabilities provided by the Information Technology Era to advance Future-oriented
Technology Analysis (TFA), both product and process. Among these new capabilities
the TFA Methods Working Group has recently1 identified three main converging areas
of development: complex networks, simulation modeling of CAS and the search of vast
databases. Such convergence has conducted to a rejuvenation and growth in FTA
methods and practice, much in accordance with the perspective envisioned by Harold
Linstone in 19992, following his optimistic view of a strong, confident technologydriven scenario, which would bring a renewed thrust toward new methods in
technological forecasting (Figure 1). The picture suggests that the chaotic phase
transition might be behind us and that the new trust is all around, as alias we can infer
from the agenda of this seminar and the integration of the field discussed in the above
referred paper1 (not to mention the umbrella concept of TFA recently adopted!).
Figure 1 – Technological Forecasting in perspective presented by Linstone in the 30year anniversary issue of TF&SC (1999).
Narrowing the focus on new methods related to the new capabilities we have the themes
of the parallel sessions 5 and 6 of this seminar: tales from the frontier (new methods for
TFA) and importing ideas (methods and tools adopted from other fields) respectively.
Both cases borrow necessarily the discussion of methods and tools that have explosively
grown in recent years related to the biosciences, bioinformatics and evolutionary
approaches. Among the needs for TFA envisioned by the TFA Methods Working Group
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we find the questioning about the validity of the analogy between technological evolution
and biological evolution (ref. 1, pp. 299): “Can artificial technological worlds be
created by simulation modeling analogous to biological ones?”. This question is hardly
a new one, and we can even trace an at least three-decade long debate on this issue.
What make the difference now are exactly the powerful new capabilities provided by
the Information Technology Era and the manifold convergence of information and
molecular technologies that are contributing enormously to new insights in simulation
methods and evolutionary programming. In the previously cited 30-year anniversary
issue of TF&SC (1999) Bowonder et al.3 have briefly reviewed this topic, mainly
focusing some of the lessons learned from evolutionary theory involved in anticipating
changes in evolutionary trajectories, and proposed a research agenda for future research.
But these authors have not considered in detail the new capabilities and have not
identified the possible sources of troubles and obstacles to be overcome to transform
evolutionary approaches in useful forecasting tools.
The present paper intends to present the state-of-the-art on this debate and to
address some important considerations necessary to answer the question above. The
sense one gets from the published literature on this theme is that the to-date effort has
been in great part centered on the striking similarities between biological evolution and
technological evolution and mostly based on verbal theorizing. It seems that a synthesis
of biology and technology remains beyond reach, with some people even doubting
whether it can ever be achieved. In the following lines we intend to point out and briefly
discuss some quite important aspects that have been overlooked and misinterpreted in
this exciting debate.
II - Some missing pieces…
It is usually said that some biological evolution-related concepts like mutation,
selection, adaptation, life cycle, survival of the fittest, etc, are useful metaphors in the
realm of economics, business and technology assessment. But few people realize that
the inverse is also a common usage: as systems increase in complexity, it becomes
necessary to draw upon social experiences to provide the necessary analogies4. This is
the case in cellular and molecular biology, where we find, for instance, a cell seen as a
factory, with complex relationships and functions such as signaling, energy budget,
transport, and quality control. Peter Corning5 has pointed out that complexity – in nature
and human societies alike – has been shaped by the “payoffs” arising from various
forms of synergy. Cooperation, “payoffs”, networks, agents, and some other conceptual
figures first originated in the reasoning about the social realm, became in last years the
most common figures permeating social and natural sciences as well.
The metaphorical language provides the means for understanding and talking about
abstract ideas and entities that are not directly observable, in terms of concepts
grounded in very basic physical and social perceptions. The more complex and
intangible the system, the more useful is the resort to metaphors. That is evidently the
case of the theory of evolution itself: we evoke metaphors in both ways, from the
biological to the social and also in the other way around, from the social to the
biological. And that is so since Darwin himself, and it is what makes Darwinism a so
controversial and long-lived scientific discipline, still open to further developments and
applications.
Theorizing about the evolutionary (Darwinian) aspects of technological change is
then not merely a question of using metaphors and making analogies, as we find in the
literature on the theme. There are some further and subtle aspects to consider that, in my
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point of view, are still not well taken in account in the variety of discussions found in
the literature. These aspects may consist in some of the missing pieces to complete the
puzzle of a firmly based evolutionary theory of technological change (ETTC for short),
and are listed as points in the paragraphs below in a quasi-logical sequence. Needless to
say that these points are strongly inter-related and then difficult to be commented on
without some overlapping of ideas:
n - Biology, or perhaps more generally, biosciences, is not merely a good source of
metaphors, but historically it was, still is and I strongly believe that it will endure as the
most powerful mean to capture and to describe the ecosystem (what includes the
aggregate human behavior) and will be the seed and/or the substract for the further
development of useful forecasting tools in the technological realm. This is an historical
fact to which follows the necessity of acknowledging the law-like aspect underlying all
growth phenomena in the living (social as well) realm, mainly related to the mechanism
of information transmission and increase in system’s complexity.
o - The development of a working ETTC bears the correct understanding of three
difficult-to-define concepts, usually taken as granted by the popular common sense:
technique, technology and (technological) innovation. An evolutionary approach within
the framework of ‘anthropology of technique’ is a necessary step to grasp adequately
these concepts.
p - Universal Darwinism is still not well understood, what has delayed the entrenching
of evolutionary economics as a powerful alternative to other current economic models.
Mainstream economics still has strong objections to the application of pure Darwinian
principles in the working of agents in the socioeconomic realm. This barrier must be
overcome to constructing a working ETTC.
q - It does not make sense to develop an ETTC starting from the analogies and/or
disanalogies found between biological and techno-cultural evolution, or in other words,
between the evolution of organisms and artifacts. It urges to accept the general principle
of ‘Evolutionary Epistemology’, which interprets the whole history of human social,
intellectual and material development as the continuation of biological evolution by
other means. It is still missing to recognize that there are some other fundamental laws
(or driving forces) underlying evolution as a whole and that must be added to the
already acknowledged general rules of blind variation plus selective retention.
r - Finally it should be added that, in comparison with the relatively vast literature
found in verbal theories of techno-cultural evolution, the amount of practical work using
simulation methods is still a dwarf one. Although recognizing that the fields of
evolutionary computation and artificial life are still emerging sciences, some important
modeling attempts were undertaken along with the last decades and I think that some of
the above mentioned points are hindering the development of working computational
algorithms to simulate technological evolution.
It is impossible to accomplish the full discussion of all these points in a short paper
planned to a seminar talk. I hope that the following text commenting on briefly each of
these points should serve as a basis for establishing an effort toward an international
research agenda on the subject.
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III. …to complete the puzzle
To point 1: more than an useful metaphor
One of the most powerful technological forecasting tools, the logistic equation, has
its origin in the biological realm and has won the status of a ‘natural law’ of technology
diffusion due to its considerable success as an empirically descriptive and heuristic
device capturing the essential changing nature of technologies, products, markets and
industries.
Viewed on the most general level, living systems, from cells to societies, exhibit
common properties, with some attending intrinsic fundamental invariants. Recognition
of this fact in last decades is leading firmly to a new scientific paradigm, a complex biosocio-economics, with the convergence of different fields of science toward what may
be the clue to understand the modus operandi of ‘evolution’ per se – the development of
evolutionary algorithms for many different problem-solving and/or theoretical
applications. The fields of evolutionary computation and artificial life have reached a
stage of some maturity and we are witnessing to-day an intense debate on ‘universal
Darwinism’ as a broad theoretical framework for the analysis of the evolution of all
open, complex systems, including socio-economic systems (see point 3 ahead).
Evolutionary arguments in economics, as in biology, originally took purely verbal
forms, and it was only with considerable delay that more mathematical (algorithmically
based) arguments and models were advanced. The mathematical tools that began to be
employed in economics (as well as in technological forecasting) starting in the 1970s
had been developed by mathematical biologists in the 1920s and 1930s and were widely
known. The widespread availability of computers (and of computer literacy) has
undoubtedly contributed for the rapid diffusion of the usage of such mathematical tools,
but the matter-of-fact is that we still observe the same obstacle that has caused this
delay: the slow recognition of the appropriateness of evolutionary arguments at all.
Formalization of evolutionary thinking in biology in algorithmic terms began in
1930 when R.A. Fisher6 published his opus “The Genetical Theory of Natural
Selection”, introducing what are now called replicator equations to capture Darwin’s
notion of the survival of the fittest. A very important aspect of Fisher’s approach when
introducing for the first time the ‘fitness function’ was that of natural selection acting on
the population level, following then trustworthy Darwin’s original idea. By the same
epoch, and not necessarily motivated by evolutionary concepts, the bio-mathematicians
Vito Volterra and Alfred Lotka popularized a set of differential equations to describe
the growth of population levels, most commonly know as predator-prey (or multicompetition) equations. Important to note that more recently Hofbauer and Sigmund7
demonstrated that Volterra-Lotka and replicator equations are equivalent.
Yet in 1925 the American biologist and demographer Raymond Pearl8 in his
seminal book “The Biology of Population Growth” call the attention to the fact that the
growth of populations is essentially a phenomenon of biological nature, that is, a
phenomenon involving natural processes of reproduction and diffusion. Comparing
different growth processes, like the growth of organisms (measured by the body mass),
the growth of a population of yeast cells (in an appropriate nutritive solution), the
growth of a population of Drosophila melanogaster, or even the growth of human
populations, Pearl observed that all growth processes could be adequately described by
the logistic or Verhulst equation (which as we well know is a particular case of the
Volterra-Lotka equations, when a single population is competing for limited resources
in a confined niche). The question at that time was why a single universal algorithm
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(the logistic equation) can describe so different growth processes? See that in the first
case (growth of a body mass) one is measuring indirectly the cumulative growth of a
population of cells, not competing for resources and whose limit is dictated by genetic
inheritance. Yeast cells are unicellular organisms competing for resources and
multiplying themselves by cellular division, while Drosophila are complex organisms
doted with devices for digestion and sexual reproduction. In both cases there is no kind
of genetic inheritance controlling the ceiling of the growth process. Culminating the
differences are the human beings, not only much more complex organisms, but with
also a complex interconnection of motivations for living and reproduction.
It was not until the 1970s that the Volterra-Lotka equations have found numerous
applications in the world of business and technology assessment, describing the
competition among firms or innovations, or simply among products struggling for a
bigger market share. It is well known the case of the pioneering work of Fisher and Pry9
demonstrating the validity of the normalized logistic equation in accounting for
technological substitution processes or for the diffusion of basic technological
innovations. Cesare Marchetti10 and Theodore Modis11 contributed further to this
development calling the attention for the closed relationship between the growth and
diffusion of innovations and pure learning processes (as for instance the growth curve of
a child’s vocabulary achievement, that also follows a pure logistic trajectory). Moreover
Modis11 has demonstrated the complete equivalence between the learning curves
(exponential decaying) used in economy of scale (‘learning by doing’) and the logistic
(S-shaped) curves. More recently Devezas and Corredine12 proposed a generalized
diffusion-learning model to explain the succession of long waves in the technoeconomic world, whose basic mechanism of recurrence is controlled by two kinds of
biological determinants (constraints – generational and cognitive) that impose the
rhythm of collective human behavior.
All this is to say that the use of biological approaches in analyzing the evolution of
technology or the unfolding of economic phenomena (in small or worldwide scale) is
not a matter of simple metaphorical comparisons. We have witnessed a natural
evolutionary process of the human understanding of the socioeconomic realm that was
forced to follow (we may say in a fractal fashion) the same path, which will inevitably
lead to the recognition that cultural evolution is the continuation of biological evolution
by other means. It is absolutely clear that learning has a definitive role in the
technological or cultural evolution (we will turn to this aspect when discussing points 4
and 5), but is not sufficient to explain the ubiquity of the logistic curve in the living
world.
The question remains: what is the common denominator underlying the growth
phenomena of populations of multiplying cells, Drosophila, humans, and innovations?
From my point of view the common denominator lies in the basic mechanism of
information transmission (and also of information growth, which also begets
complexity growth) a point that has not been suitably accounted for in the efforts for
finding a universal evolutionary algorithm.
To point 2: a necessary anthropology of technique
In 1904 Hugo De Vries, the eminent Dutch botanist that rediscovered Mendel’s
laws and developed the mutation theory of evolution wrote13: “Natural selection may
explain the survival of the fittest, but it can not explain the arrival of the fittest”. This
statement, written a century ago, epitomizes one of the greatest mysteries of evolution
still challenging scientists – the emergence of novelty. All of the extraordinary
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organizational forms and behavioral strategies that we witness in nature or society have
arisen through the process of inheritance with diversification and selection. The formal
treatment of evolutionary dynamics is presently cast in terms of the changing
frequencies of some fixed entities: genes, linkage groups, individuals, social groups, and
even memes (in the cultural realm). Yet it is the arising of these robust and resilient
structures, in other words, the emergence of innovations, that is of profound interest,
both theoretically and for applications this understanding will facilitate.
Here we are dealing also with one of the most controversial points in all previous
attempts of comparisons between biological and technological evolution, that can be
subsumed under the following questions – are innovations (or novelties) in the
biological, cultural, and technological realm of the same nature? And if the answer is
positive what is the underlying set of rules driving their emergence and continuous
unfolding. There are many reasons to think that the answer is indeed positive, some of
them will be considered shortly in this paper, as well as some of the candidate rules
(point 4 ahead) driving the phenomenon and still absent of much of this discussion.
To begin with it should be stand out that the notion of innovation belongs itself to
that collection of fuzzy concepts, that along with some other (not necessarily related)
hard-to-define fancies like for instance globalization and complexity, are the currency of
contemporary economic and scientific debates. Everyone knows intuitively what they
are, but nobody can satisfactorily offer a short (and at the same time broad) definition of
each of them. But differently than globalization and complexity, that are more or less
restricted to economics, business or politics (as in the case of globalization) or restricted
to more scientific discussion rounds (the case of complexity), innovation is by far the
more transversal of them, bearing probably all possible human spheres of action. On the
Internet, a Google search yields the following results (April 2004) – 2.800.000 hits for
globalization, 6.600.000 for complexity, and 11.500.000 for innovation! But attention
please: the true winner in this modern competition is evolution with more than
17.000.000 hits, what evidences how evolutionary thinking permeates modern human
thought!
Most authors agree that it is impossible to define ‘innovation’ in a context-free
manner, and this difficulty is not necessarily make easier if we restrict our analysis to
‘technological innovation’ (our present context). I want to advance the following
arguments favoring an evolutionary approach to define innovation and then answering
in the positive the question above about the same nature of novelties in the biological,
cultural and technological realm:
c Novelty in any sphere of the living world (what includes social systems) seems to
arise ‘out of nowhere’, in spite of strong constraints that stabilize extant structures;
d In biological systems an innovation can be achieved without necessarily changing the
genetic underpinnings of a feature, but by shifting the context and timing of their
expression within the developmental sequence of an organism. This suggests that a
feature’s integrity depends on a systemwide network of interactions involving other
features. The same statement is true in a technological context if we substitute the words
‘genetic underpinnings’ by building blocks (following John Holland’s14 original
proposition of model building and emergence) and ‘organism’ by artifact.
e Evolution of organisms is the conjunction of two facts: the selective amplification of
genotypes based on the differential reproductive success conveyed by their phenotypes
through chance events at the level of genotypes. Again the same statement can be used
in the technological realm by substituting words: ‘genotypes’ by any sequence of
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building blocks, ‘differential reproductive success’ by differential adoption in a market
and ‘phenotype’ by technical expression.
f My final argument favoring an evolutionary definition of innovation regards the
aspect mentioned above of how strongly evolutionary thinking permeates modern
human worldview (accepting or not accepting the idea of an intelligent designer).
My proposal of definition is then simply:
Innovation is the emergence of a new adaptive design.
This definition has sufficiently broad meaning and can be easily applied in the
domain of cultural traits or technical artifacts. But, as commented on above, when we
focus the evolutionary analysis on technological innovations we are not necessarily
simplifying the field of discussion, but instead we are adding some difficulties about
which disagreement abounds in the published literature. In despite of the fact that nearly
everyone agrees that to explain technological advances we must look beyond the
artifacts themselves, we have some crucial troubles when talking about fundamental
ideas behind them. Some of these fundamentals are:
1 – what should be the suitable unity of analysis in technological evolution? Or in other
words, what then actually evolves? Artifacts themselves, the technical knowledge to
make them or some combination of these? Or the interface of artifacts and ideas in
technological practices?
2 – how does heritability occur in technological systems? That is, how do technological
units (whatever they may be) carry their information forward through time?
3 – are technological innovations indeed teleological or Lamarckian in nature or not?
Looking at the history of inventions and basic innovations we can find some evident
cases of intended and/or planed novelties as well as it appears to be common to find a
wide range of dramatic early random experimentation with radically different designs,
which branch further and then settle down to a few dominant lineages.
In a very recent book edited by John Ziman15 (Technological Innovation as an
Evolutionary Process) we have different authors theorizing about these questions, but
unfortunately we can not see much progress when we compare these contributions with
texts published in the 1980’s, as for instance the very often cited books of Nelson and
Winter16 and Basalla17. There is still little in the way of formal theorizing and model
building, and we can say that a lot of work remains to be done to make evolution a
viable strategy and school of thought in the study of technology. In my view what is
missing is a bridge linking evolutionary concepts in biology to technological progress,
but a bridge leading to a level higher up than the plain mapping of every element of
technological evolution onto a precise correspondence in the biological counterpart.
Such a bridge could be offered by a better-developed ‘anthropology of technique’,
in the way paved by the German philosopher of technology Hans Sachsse18 almost three
decades ago. Sachsse (whose work was most published in German and has remained in
a kind of limbo, probably obfuscated by the ‘evolutionary epistemology’ developed by
Karl Popper, with which it shares many common points) considered that humans
through their technical handle continued nature work, or in other words, we have
helped nature in its evolution. In the points below I try to resume some important
aspects that were never consistently considered in the attempts of model building of an
ETTC:
1 – Technique precedes technology, not only in human history, but also under a pure
evolutionary point of view. Technique (or routine, what is often the same thing)) did not
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need a brain or mind to come in existence in the course of biological evolution: very
primitive life forms have developed skilled techniques of gathering food, of attracting
partners for mating, of disguise to avoid predators, and of capturing preys. Some
primitive underwater beings are very successful killing machines. In a single coral reef
we can witness all a wonderful ebullience of rough life forms performing a huge of
trickeries to survive and reproduce.
2 – In the course of biological evolution the technique came to life as a form of
searching for a bypass (or shortcut) to reach a goal, because it is easier to pursue this
goal through the bypass. In my view this is a clear manifestation of the principle of the
least action in practice, which has worked as the underlying driving force for better and
better search procedures, amplified by the development of learning capabilities (we will
turn to this point ahead).
3 – Following this reasoning we can state then that humans, when dealing with
technique, do in a conscious way what nature ever did unconsciously. In other words
we can say that human technical skills are the continuation of this natural search for
bypasses by intelligent means.
4 – Another important conclusion is that the existence of learning capabilities and the
further development of brain and mind came into life because nature owns the basic
structure (then a fundamental law) of over shortcuts to reach easily the goals
immediately ahead.
5 – Technology is a recent human achievement that flourished conceptually in the 18th
century, when technique was not more seen as skilled handwork, but has turned as the
object of systematic human knowledge and a new ‘Weltanschaung’ (at that time purely
mechanistic). This terminus was proposed first in 1777 by the German economist
Johannes Beckman (in his opus “Einleitung zur Technologie oder zur Kenntnis der
Handwerke, Fabriken und Manufakturen”) as science from the technique, or the ‘Lehre’
as men perform something (technical) at their best.
With this short collection of ideas I wish to suggest that a firmly conceptually based
‘anthropology of technique’ is still lacking in the current attempts of model building and
formal theorizing of an ETTC. At this point it is worth to point out that I agree with Joel
Mokyr19 that the unity of analysis that makes sense for the study of technological
evolution is the ‘technique’.
To point 3: demotion and rise of evolutionary concepts in economics
It is well known the fact that the social sciences after experiencing an initial thrust
from evolutionary concepts at the turn of 19th to 20th centuries has historically insisted
in ignoring Darwinian ideas. Economics, in some ways the most ambitious of the social
sciences, progressively abandoned biology and adopted physics as its model natural
science. Social scientists, and particularly economists, have never correctly realized that
Darwin in his second and long-ranging intellectual torpedo (1871 –The Descent of Men
and Selection in Relation to Sex) has devised a theory that was more applicable to
cultural traits than to genetics (foreign to his thinking). Darwin himself was confused
about the mechanism of inheritance, and, by always imagining that organic inheritance
included the feature of inheritance of acquired variation and by liberally using the
concept of inherited habits gave birth to the most controversial scientific debacle that
lasted for over a century.
But during the last two decades we have seen a growing interest in evolutionary
ideas among economists. New professional associations focusing on these ideas have
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been founded and for more than fifteen years there has been the ‘Journal for
Evolutionary Economics’ (Springer), devoted particularly to this topic. This upswing in
evolutionary economics was in great part due to the renewed interest in the discussion
on long waves in economics during the last two decades, which otherwise open the way
to the revival of Joseph Schumpeter’s ideas of a evolutionary global economy driven by
the clustering of basic innovations and ‘creative destruction’ of older ones during
economic depressions (for a review on this topic see Devezas-Corredine, ref. 12).
However the basic ideas underlying evolutionary economics are still a matter of
considerable controversy. Among the main objections we can find for instance:
- some modern approaches from complex systems theory, like self-organization, is an
alternative to ‘biological analogies’ or Darwinism;
- ‘artificial selection’ is an alternative to ‘natural selection’ in the socio-economic
sphere;
- Darwinism excludes human intentionality.
We have no space in this paper to discuss in depth these objections, but as
demonstrated in a recent article published by Geoffrey Hodgson20 in the J. of Evol.
Economics, it is relatively easy to shown that all these objections are ungrounded. In
fact Darwinism includes a broad theoretical framework for the evolution of all open,
complex systems, including socio-economic systems, also involving a basic
philosophical commitment to detailed, cumulative, and causal explanations, as
envisioned by Richard Dawkins21 in his Universal Darwinism in 1983. Hodgson20
stated that Darwinism provides a compelling ontology and it is a universal theory in
which specific theories must be nested. However, Darwinism does not provide a
complete explanation of socio-economic phenomena, something more is required. As I
already pointed out before, the social cannot be reduced to the biological, a point of
view also stressed by Hodgson20: Darwinism may be universal, but economics should
not be abandoned to biology. There are the missing pieces I have mentioned in this
paper (see further discussion in the next section) and the necessary bridge to the
‘Anthropology of Technique’ discussed previously.
To point 4: technological evolution as the continuation of biological evolution by
other means (or more than blind variation plus selective retention)
Karl Popper’s22 view of scientific progress as a cumulative selection process
resembling Darwin’s natural selection threw new light on the evolutionary concept of
human cultural development. He proposed the natural selection of hypotheses, asserting
that our knowledge consists, at every moment, of those hypotheses that have shown
their fitness by surviving so far in their struggle for existence, a competitive struggle
that eliminates those hypotheses that are unfit. This hypothesis has paved the main road
followed by modern thinkers in cultural evolution, beginning with Donald Campbell23
in the 1960’s (who coined the term Evolutionary Epistemology to characterize Popper’s
epistemology) and conducting to some conceptual breakthroughs like Richard
Dawkin’s24 memes in the 1970’s and more recently Daniel Dennets25 Darwin’s
Dangerous Idea (the idea that all the fruits of evolution, not only organisms, can be
explained as the product of a mindless and mechanical algorithmic process).
Campbell defended a universal evolutionary or selection theory, claiming
ultimately that all innovative design is produced by one or another variation-plusselection-plus-transmission process, and proposed the acronym BV (blind variation) +
SR (selective retention) to designate the process. The most important arguments
introduced by Campbell in this discussion can be resumed:
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-
unlike biological evolution, characterized by direct trial and error adaptation
processes, knowledge processes evolve through vicarious forces, that is, inheritedacquired (by learning) psychological forces that act as surrogates for natural
selection because they arose themselves by natural selection;
- in the case of genetic evolution, the most important evolutionary forces, processes
that are capable of changing gene frequencies and causing evolution, are mutation,
genetic drift, gene flow, and natural selection, making unvarnished organic
evolution a purely random variation and selective retention process. Technological
evolution (and cultural evolution as a whole) must be subject to more or less
analogs of these four forces, but is also subject to several kinds of vicarious forces.
People are not only selected willy-nilly by natural selection, they also make
conscious and unconscious choices as they learn from themselves and from others.
In essence, Campbell forcefully reintroduced Darwinian ideas to social sciences
(economics as well), after a lapse of almost a half century after the initial thrust
commented on in point 1. Basically he suggested that Darwinism contained a general
theory of the evolution of all complex systems, and made the point that the appropriate
analogy for social evolution is not biotic evolution, but the more general process of
evolution of complex systems ‘for which organic evolution is but one instance’.
However, the above arguments, obvious as they may seem, are still a matter of intense
controversy – people insist in just looking for the analogs of the above mentioned four
forces or simply reject Darwinism because it can not account for the human
intentionality – a very wrong and biased attitude.
In my view Campbell’s concept of vicarious forces provide the suitable mechanism
to ensure that cultural evolution does favor the fitness of our genes, or in other words,
the basic process of Gene-Culture Coevolution, which is the most appropriate approach
to develop a firmly based ETTC.
When discussing on the previous points I have already pointed out some features
that have not been yet accounted for in the body of existing work on technological
evolution. To finalize the present discussion on point 4 I would like to add some other
further aspects equally not yet considered as well:
- if ‘technique’ had not favored organism’s pool of genes or genes transmission it had
not evolved to technology;
- if technology had not favored human pool of genes or human genes transmission it
had not continually evolved toward more and more complex technological systems;
- the human massive capacity for culture (and technology) may be seen as a very
strong capacity of adaptation to respond to very quick spatial and temporal
variations, observed in the Earth homeland since the Pleistocene;
- the coevolutionary complexity of managing two inheritance systems (the vertical,
genetic, and the horizontal+vertical, cultural) does not imply necessarily the highest
degree of perfection, for we must consider the many cultural pathologies observed
in human society. It serves almost exclusively to the human (genetically inherited)
quick capacity of response to rapidly changing environments or as Richerson and
Boyd24 so brilliantly stated ‘Humans are built for speed not for comfort’.
- technological evolution can not be think as an independent evolutionary process,
but it is part (the most energetic one) of a broad co-evolutionary set of processes,
manifest as a cascade of multilevel, nested, and self-similar Darwinian-like
processes, which on the whole constitutes the world system, as recently empirically
and mathematically demonstrated by Devezas and Modelski25;
- this set of processes is fundamentally innovation driven (each in its own scale),
exhibits power-law behavior and it is poised in the critical boundary between order
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and chaos (poised in the sub-critical-supracritical phase transition boundary), what
allows for the necessary flexibility required to take part in the selection process at
the several levels of the evolutionary game25.
To point 5: some promising approaches
As already mentioned there is a relatively vast literature in verbal theories of
technological and cultural evolution, but there is relatively few work proposing formal
models and using simulation methods in this field. Among the reasons for this lack of
practical-oriented works we have referred to:
- the persistent opposition of mainstream economics to Darwinian concepts as applied
to socio-economic systems, mainly caused by misinformation and non-acquaintance
with the basic assumptions of Universal Darwinism;
- the insistence of trying to map every element of technological evolution onto a
precise correspondence in the biological counterpart;
- the absence of a suitable basis of reasoning that could be offered by the
‘Anthropology of Technique’;
- the still missing pieces (some of them are principles of very general nature,
commented on in the previous sections, and that I will try to resume in the
conclusions) to complete the puzzle;
- the fact that the fields of evolutionary computation and artificial life, in despite of
some maturity as source of efficient heuristic tools to solve complex problems, are
still emerging sciences.
There are two possible approaches to simulating technological and/or socioeconomic systems. The systems dynamics approach, widely used in technological
forecasting since the 1950’s, is “top-down” in character (so called because it views the
system from above, as a whole). It is usually applied to human feedback systems and
their dynamics (behavior over time) is defined via the change of their organization (or
‘state’) as described by the system’s differential equations. Such top-down analyses are
very suitable for describing system’s regularities and to identifying dominant feedback
loops, or in other words, to forecasting agents’ aggregate behavior.
The other approach forms the new sub-field of “Artificial Life” (AL, for short) that
uses so-called ‘soft computing’ models of complex adaptive systems (CAS) that
encompasses several methods of simulation and it is best characterized as a “bottomup” approach. Its origin remounts to the 1970’s with the emergence of gaming
simulation. Theoretically and methodologically this approach makes possible the
construction of models from the level of processes that are immediately and empirically
observable, namely the local interactions of single units (agents) governed by local
rules.
Although a consistent ETTC still not exists and a formal (algorithmically based)
model allowing the simulation of technological evolution was not yet developed, there
are some attempts following this approach that deserve to be mentioned here. It is worth
to point out, however, that although this methodology is being used by a few research
groups worldwide, it is impossible to make justice to all efforts of all groups found in
the literature, as well as to discuss in this paper the details and the results attained by
these groups.
The formal mathematical models developed in the past two decades and most often
used are (mentioning only some important publications for each approach):
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-
-
-
-
NK technology landscapes, initially proposed by Stuart Kauffman26 and further
pursued by other researchers of the Santa Fe Institute, like José Lobo27 and Walter
Fontana28;
Complex network analysis. This is a new and emergent scientific branch that is
finding increasing application in a wide range of fields, from the physical sciences,
to life sciences and to social sciences. The most important system’s property
unraveled by this method is the existence of scale-free networks, which seem to be
ubiquitous in nature and subjacent to all CAS. Scale-free networks pervade
technology: the Internet, power-grids and transportation systems are but a few
examples. For a review on this field I suggest the reading of two recent review
articles29,30, and regarding its application to technological systems see the work of
Ricard Solé et al.31, also conducted in close collaboration with other researchers at
the Santa Fe Institute.
Cellular automata, initially developed for gaming simulation and widely publicized
by one of its most famous developers, Stephen Wolfram32, has been applied to the
evolutionary simulation of the innovation diffusion process by a group of the
Hebrew University leaded by Jacob Goldenberg and Sorin Salomon33,34;
Percolation models. It is a numerical simulation method for the search of complex
technology spaces based on percolation theory, using also some general principles
of cellular automata and NK landscapes. It has been used for instance by some
researchers of the Maastricht evolutionary school of economics (MERIT), as Gerald
Silverberg and Bart Verspagen35, for the study of the distribution of innovations;
Genetic Algorithms (GA’s). Also widely known as evolutionary algorithms, or
evolutionary computation, were invented by John Holland14 in the 1960’s and were
developed by Holland and his students at the University of Michigan in the 1970’s.
In technology and science GA’s have been used as adaptive algorithms for solving
practical problems and as computational models of natural evolutionary systems,
and are considered today a relatively mature computational tool for solving complex
engineering problems, for which the term Modern Heuristics36 was coined.
Regarding their use in the simulation of technological evolution it has been used by
one of Holland’s students, David Goldberg37, for instance, for studying the
connection between the two basic processes of innovation, continual improvement
and discontinuous change. Goldberg proposed the use of the algorithms selection +
mutation and selection + recombination as expressing the basic mechanisms of
continual improvement and innovation respectively.
In the present stage of our knowledge no one can be sure which method is best
suited for purposes of simulating technological evolution and/or for developing useful
tools for technological forecasting. Altogether the application of these methods within
the limits imposed by their own characteristics has helped researchers in unraveling
some until now hidden properties of technological systems. My personal opinion is that,
among the above-mentioned methods, cellular automata is the poorest for more
sophisticated simulations due to the simplicity of its basic assumptions and limitations
that must be imposed in the rules governing interactions between agents. Undoubtedly
the strongest potential belongs to genetic algorithms, or more generally speaking to
genetic programming (a refinement of GA’s developed in 1987 by John Koza38), as we
can infer from very recent results (‘Evolving Inventions’) announced by John Koza and
his co-workers39 (Stanford University). They claim to have reproduced ‘in silico’ 15
previously patented inventions in the field of electronics (6 of them patented after
January 2000) and have applied for a patent for a genetically evolved general-purpose
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controller that is superior to mathematically derived controllers commonly used in
industry.
IV. Conclusions
I see the new science of Digital Darwinism based on further improvements of
genetic algorithms and genetic programming as the most promising candidate for
establishing the knowledge basis of a working Evolutionary Theory of Technological
Change, as well as for developing useful tools for TFA. What remains to be done,
besides the improvements in the computational methods, is to incorporate in the
simulations some of the general evolutionary principles that were outlined in the present
paper, and that until now were not suitably considered in previous modeling attempts. It
is presented below a short resume of these missing fundamental considerations:
- the common denominator to all growth and diffusion phenomena in the living world
is the transmission of information, whose continuing evolutionary process
conduces to increasingly complex systems;
- cultural evolution (and technological evolution as well) is the continuation of
biological evolution by other means;
- technique is the most suitable basic unity of analysis and must be viewed as the
enduring search for bypasses (shortcuts) obeying the general physical principle of
the least action;
- technology must be viewed as the further improvement of this process by
intelligent means (then allowing too for intentionality), possessing both
mechanisms of variation – simply random (Darwinian) and intentional;
- human technology is a part of a biologically co-evolved massive capacity for
culture, managing two inheritance systems, vertical (twofold in scope, genetic and
Lamarckian) and horizontal (pure Lamarckian in scope), and that serves
fundamentally to the quick human capacity of adaptation;
- technological evolution is not an independent evolutionary process, but it is the
fastest and more energetic among a broad innovation-driven and co-evolutionary
set of processes, composing the whole of the world system.
As a first step toward a research agenda for future development of TFA I propose
the realization of an international seminar in this field (Evolutionary Theory of
Technological Change) bringing together specialists in evolutionary model building and
digital Darwinism to discuss the existing approaches and to present their most recent
results. As a follow-up of this event we could think on the edition of a proceedings
volume containing a collection of the most important contributions, which should be
more empirical than theoretical in scope. This scientific meeting could be planed
following the format of a recent proposal of this author with George Modelski for a
seminar on Globalization as Evolutionary Process40 to be held in the spring of 2005 in
Paris, sponsored by the Calouste Gulbenkian Foundation.
References
1. TFA Methods Working Group, Technology Futures Analysis: Toward Integration of the
Fields and New Methods, Technol. Forecast. Soc. Change 71 (2004) 287-303.
2. H.A. Linstone, TF&SC: 1969-1999, Technol. Forecast. Soc. Change 62 (1999) 1-8.
3. Bowonder et al, Predicting the Future : Lessons from Evolutionary Theory, Technol.
Forecast. Soc. Change 62 (1999)51-62.
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4. T.L. Brown, Making Truth: The Roles of Metaphors in Science, University of Illinois Press,
2003.
5. P. Corning, Nature’s Magic: Synergy in Evolution and the Fate of Humankind Cambridge
University Press, 2003.
6. R.A. Fisher, The Genetical Theory of Natural Selection, Clarendon Press, Oxford, 1930.
7. A. Sigmund and J. Hofbauer, Dynamical Systems and the Theory of Evolution, Cambridge
University Press, 1990.
8. R. Pearl, The Biology of Population Growth, Alfred A. Knopf, New York, 1925.
9. J. Fisher and R. Pry, A Simple Substitution Model of Technological Change, Technol.
Forecast. Soc. Change 3 (1971) 75-88.
10. C. Marchetti, Society as a Learning System: Discovery, Invention and Innovation Cycles
Revisited, Technol. Forecast. Soc. Change 18 (1980) 257-282.
11. T. Modis, Predictions: Society’s Telltale Signature Reveals the Past and Forecasts the
Future, Simon & Scuster, New York, 1992.
12. T. Devezas and J. Corredine, The Biological Determinants of Long-Wave Behavior in
Socioeconomic Growth and Development, Technol. Forecast. Soc. Change 68 (2001) 1-57.
13. H. De Vries, Species and Varieties, Their Varieties by Mutations, Kegan Paul, Trench,
Trubner & Co., London, 1904.
14. J. Holland, Emergence: From Chaos to Order, Perseus Books, Cambridge, Ma., 1998.
15. J. Ziman (Ed.), Technological Innovation as an Evolutionary Process, Cambridge
University Press, 2003.
16. R.R. Nelson and S.G. Winter, An Evolutionary Theory of Economic Change, Beknap of
Harvard Univerasity Press, Boston, 1982.
17. G. Baslalla, The Evolution of Technology, Cambridge University Press, 1988.
18. H. Sachsse, Anthropologie der Technik, Vieweg, Braunschweig, 1978.
19. J. Mokyr, Evolutionary Phenomena in Technological Change, in Ref. 15.
20. G.M. Hodgson, Darwinism in Economics: from Analogy to Ontology, J. of Evol. Econ.
12(2002)259-281.
21. R. Dawkins, Universal Darwinism, in D.S. Bendall (ed), Evolution from Molecules to Men,
pp. 403-425, Cambridge University Press, 1983.
22. K. Popper, Objective Knowledge: an Evolutionary Approach, Oxford University Press,
Oxford, 1972.
23. D. T. Campbell, Blind Variation and Selective Retention in Creative Thought as in other
Knowledge Processes, Psychological Review 67 (1960) 380-400.
24. P.J. Richerson and R. Boyd, Built for Speed, Not for Comfort, Hist. Phil. Life Sci.
23(2001)425-465.
25. T. Devezas and G. Modelski, Power Law Behavior and World System Evolution, Technol.
Forecast. Soc. Change 70 (2003) 819-859.
26. S. Kauffman, At Home in the Universe, Oxford Nniversity Press, New York, 1995.
27. D. Strumsky and L. Lobo, If it isn’t broken, don’t fix it, Extremal Search on a Technology
Landscape, SFI-Working Paper 03-02-003 (2003).
28. J. Lobo, J.H. Miller and W. Fontana, Neutrality in Technological Landscapes, SFI-Working
Paper (2004).
29. I. Frommer and G. Pundor, Small Worlds: a Review of Recent Books, Networks
4(13)(2003) 174-180.
30. A-L.Barabasi and E. Bonabeau, Scale-Free Networks, Sci. Am., May 2003, 50-59.
31. R.V. Solé et al., Selection, Tinkering, and Emergence in Complex Networks, SFI-Working
Paper 02-07-029 (2002).
32. S. Wolfram, A New Kind of Science, Wolfram Media, Inc., 2002.
33. J. Goldenberg and S. Efroni, Using Cellular Automata Modeling of The Emergence of
Innovations, Technol. Forecast. Soc. Change 68 (2001) 293-308.
34. J. Goldenberg et al., Inevitably Reborn: the Reawakening of Extinct Innovations, to be
published in Technol. Forecast. Soc. Change 71 (2004).
35. G. Silverberg and B. Verspagen, A Percolation Model of Innovation in Complex
Technology Spaces, to be published in J. Econ. Dynam. & Control (2004).
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36. Z. Michalewicz and D.B. Vogel, How to Solve it: Modern Heuristics, Springer, Berlin,
2002.
37. D. Goldberg, The Design of Innovations: Lessons from the Genetics, Lessons fro the Real
World, Technol. Forecast. Soc. Change 64 (2000) 7-12.
38. J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural
Selection, MIT Press, Boston, Ma., 1992.
39. J. Koza et al., Evolving Inventions, Sci. Am., February 2003, 52-59.
40. T. Devezas and G. Modelski, Globalization as Evolutionary Process: Modeling, Simulating
and Forecasting Social Change, A proposal of a Seminar to the Calouste Gulbenkian
Foundation, to be held in Paris, Spring 2005.
Presentation 2 :
I. Introductory thoughts
9
9
Session 5 (tales from the frontier)
Session 6 (importing ideas)
Î Both cases borrow necessarily the discussion of methods and
tools that have explosively grown in recent years rel ated to the
biosciences, bioinformatics and evolutionary approaches.
)Can artificial technological worlds be created by
si mulation modeling analogous to biological ones?
¾
My point in this presentation is to address some important
considerations stressing some quite rel evant aspects that have
been overlooked and misinterpreted in previ ous works.
14 May 2004
2
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II. Some missing pieces...
n - Biology, or perhaps more generally, biosciences, is not merely
a good source of metaphors, but historically it was, still is and I
strongly believe that it will endure as the most powerful mean to
capture and to describe the ec osystem (what includes the
aggregate human behavior) and will be the seed and/or the
substract for the further development of useful forecasting tools
in the technological realm. This is an historical fact to which
follows the necessity of acknowledging the law -like aspect
underlying all growth phenom ena in the living (social as well)
realm, mainly related to the mechanism of information
transmission and increase in system’s complexity.
14 May 2004
3
II. Some missing pieces...
(cont.)
o - The developm ent of a working E TTC bears the correct
understanding of three difficult-to-define concepts, usually taken
as grant ed by the popular common sense: technique,
tech nology and (technological ) innovation. An evolutionary
approach within the framework of ‘anthropology of technique’ is
a necessary step to grasp these concepts.
p - Universal Darwini sm is still not well understood, what has
delayed the ent renching of evolutionary economics as a
powerful alternative to current economic models. Mainstream
economics still has strong objections to the application of pure
Darwinian principles in the working of agents in the
socioeconomic realm. This barrier must be overcome to
constructing a working ETTC.
14 May 2004
4
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II. Some missing pieces...
(cont.)
q - It does not make sense to develop an E TTC starting from the
analogies and/or ‘disanalogies’ found between biological and
techno-c ultural evolution, or in other words, bet ween the
evolution of organisms and artifacts. It urges to accept the
general principle of ‘Evolutionary Epi stemolog y’, which
interprets the whole history of human social, intellectual and
material development as the continuation of biological evolution
by other means. It is still missing to recognize that there are
some other fund amental law s (or driving forces) underlying
evolution as a whole and that must be added to the already
acknowledged general rules of blind variation plus selective
retention.
14 May 2004
5
II. Some missing pieces...
(cont.)
r - Finally it should be added that, in comparison wit h the relatively
vast literature found in verbal theories of techno-cultural
evolution, the am ount of practical work using simulation
methods is still a dwarf one. Although recognizing that the fields
of evolutionary compu tation and artifici al life are still
emerging sciences, some important modeling attempts were
undertaken along with the last decades and I think that some of
the above m entioned points are hindering the development of
working computational algorithms to simulate technologic al
evolution.
14 May 2004
6
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III. …to complete the puzzle
To point 1: more than an useful metaphor
¾
One of the most powerful technological forecasting tools, the
logistic equation, has its origin in the biological realm and has
won the status of a ‘natural law’ of technology diffusion due to its
considerable success as an empirically descriptive and heuristic
device capturing the essential changing nature of technologies,
products, markets and industries.
¾
The mathematical tools that began to be employed in economics
(as well as in TF ) starting in the 1960s -1970s had been
developed by mathem atical biologists in the 1920s-1930s and
were widely known.
14 May 2004
7
III. …to complete the puzzle
(cont .)
¾
Form alization of evolutionary thinking in biology in algorithmic
terms - 1930 - R.A. Fisher - “T he G enetical Theory of Natural
Selection”, introducing replicator equation s to capture
Darwin’s notion of the su rvival of the fittest. Concept of
‘fitn ess function’- nat ural selection acting on the population
level (Darwin).
¾
1920s-1930s - Vito Volterra and Alfred Lotka popularized a set
of differential equations to describe the growth of population
levels - predator-prey (or multi-com petition) equations.
¾
1990s - Hofbauer and Sigmund demonstrated that VolterraLotka and replicator equation s are equivalent.
14 May 2004
8
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III. …to complete the puzzle
(cont .)
1925 - Raymond Pearl - “The Biology of Population Growth”
¾
Com paring di fferent growth processes - organisms (measured
by the body mass) - populations of yeast cells - popul ations of
Drosophil a melanogaster - growth of human popul ations, Pearl
observed that all growth processes could be adequately
described by the logistic or Verhulst equation (which as we well
know is a particular case of the Volterra-Lotka equations, when
a single popul ation is competing for limited resources in a
confi ned niche).
The question at that time was wh y a single universal
algorithm (th e logi stic equ ation ) can d escribe so different
growth processes?
¾
14 May 2004
9
III. …to complete the puzzle
¾
¾
¾
¾
(cont .)
After the 1970s - Volterra-Lotka equations have found num erous
applications in the world of business and technology assessm ent,
des cribing the competition am ong firms or innovations , or s im ply
among products s truggling for a bigger market share.
Fis her and Pry, Ces are Marchetti,Theodore Modis - dem ons trated the
validity of the norm alized logis tic equation in accounting for
technological s ubs titution processes or for the diffusion of basic
technological innovations , as well as the closed relationship between
the growth and diffus ion of innovations and pure learning process es (at
the individual or at the aggregate level).
All this is to say that the us e of biological approaches in analyzing the
evolution of technology or the unfolding of economic phenom ena (in
sm all or worldwide s cale) is not a m atter of s im ple metaphorical
comparisons .
Question: w hat is t he common denominator underlying the grow th
phenomena of populations of multiplying cells, Drosophila,
humans, and innovations?
³
14 May 2004
In formation tran smi ssion
10
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III. …to complete the puzzle
(cont .)
To point 2: a necessary anthrop ology of tech nique
¾
)
¾
¾
1904 - Hugo De Vries , Dutch botanis t who redis covered Mendel’s laws
and developed the m utation theory of evolution wrote: “Natural
selection may explain t he survival of the f ittest, but it can not
explain the arrival of the f ittest”. This s tatement, written a century
ago, epitomizes one of the greates t m ys teries of evolution s till
challenging s cientis ts – the emergence of novelty.
Are innovations (or novelties) in the biological, cultural, and
technological realm of the same nature? And if the answer is
pos itive what is the underlying s et of rules driving their em ergence and
continuous unfolding.
In m ay opinion the answer is indeed pos itive, and there are som e
candidate rules driving the phenomenon, s till abs ent of m uch of this
dis cuss ion.
Mos t authors agree that it is im poss ible to define ‘innovation’ in a
context-free m anner, and this difficulty is not necess arily m ake eas ier if
we res trict our analys is to ‘technological innovation’.
14 May 2004
11
III. …to complete the puzzle
¾
Î
¾
¾
¾
(cont .)
In the paper I pres ented four s trong argum ents favoring an evolutionary
approach to define innovation and to answer in the pos itive the
ques tion above. My propos al of definition is s im ply:
Innovation is t he emergence of a new adaptive design.
This definition has sufficiently broad m eaning and can be easily applied
in the dom ain of cultural traits or technical artifacts .
Difficulties and reasons of dis agreement:
1 – what s hould be the suitable unity of analysis in technological
evolution? Or in other words , what then actually e volves ? Artifacts
themselves , the technical knowledge to m ake them or ….
2 – how does heritability occur in technological s ys tems ?
3 – are technological innovations indeed teleological or Lamarckian
in nature or not?
Unfortunately we have not s een m uch progress on thes e ques tions in
the las t 20 years .
14 May 2004
12
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III. …to complete the puzzle
(cont .)
¾
In m y view what is m iss ing is a bridge linking evolutionary concepts
in biology to technological progress, but a bridge leading to a level
higher up than the plain m apping of every element of technological
evolution onto a precis e corres pondence in the biological counterpart.
¾
Such a bridge could be offered by a better-developed ‘anthropology of
technique’, in the way pa ved by the Germ an philosopher of technology
Hans Sachsse alm os t three decades ago.
¾
Sachss e cons idered that humans through their technical handle
continued nature w ork, or in other words , we have helped nature in
its evolution.
¾
The unity of analys is that m akes s ense for the s tudy of technological
evolution is the ‘technique’.
14 May 2004
13
III. …to complete the puzzle
(cont .)
In the points below I tr y to res um e som e im portant as pects that were
never cons is tently cons idered in the attem pts of m odel building of an
ETTC:
1 – Technique precedes technology, not only in hum an his tory, but als o
under a pure evolutionary point of view. Technique (or rout ine, what is
often the s ame thing)) did not need a brain or m ind to come in
exis tence in the cours e of biological evolution: very prim itive life forms
have developed s killed techniques of gathering food, of attracting
partners for mating, of dis guis e to avoid predators , and of capturing
preys .
2 – In the course of biological evolution the technique cam e to life as a
form of searching for a bypass (or shortcut) to reach a goal, becaus e
it is easier to purs ue this goal through the bypass . In m y view this is a
clear m anifes tation of the principle of t he least action in practice,
which has worked as the underlying driving force for better and better
s earch procedures , amplified by the developm ent of learning
capabilities .
¾
14 May 2004
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III. …to complete the puzzle
(cont .)
3 – Following this reas oning we can s tate then that humans, w hen
dealing w ith technique, do in a conscious w ay w hat nature ever
did unconsciously. In other words we can s ay that hum an technical
s kills are the continuation of this natural s earch for bypass es by
intelligent m eans .
4 – Another conclus ion is that the exis tence of learning capabilities and the
further development of brain and m ind came into life becaus e nature
ow ns the basic structure (then a fundamental law ) of over
shortcuts to reach easily the goals immediately ahead.
5 – Technology is a recent human achievement that flouris hed in the 18 th
century, when technique was not m ore seen as s killed handwork, but
has turned as the object of s ys tematic hum an knowledge and a new
‘Weltans chaung’. This terminus was firs t propos ed in 1777 by the
Germ an econom is t Johannes Beckman (“Einleitung z ur Technologie
oder z ur Kenntnis der Handwerk e, Fab riken und Manufak turen ”) as
science from the technique, or the ‘Lehre’ as men perform
something (technical) at t heir best.
14 May 2004
15
III. …to complete the puzzle
(cont .)
To point 3: demotion and rise of evolut ionary concepts in economics
¾
¾
¾
¾
¾
Social s cientis ts , and particularly econom is ts , have never correctly
realized that Darwin(1871 – The Descent of Men) has devised a theory
that was m ore applicable to cultural traits than to genetics (foreign to
his thinking).
Evolutionary economics is s till a burgeoning s cience (~20 years ), see
f.i. Journal of Evolutionary Econom ics (Springer 1988).
Basic ideas underlying evol. econ. are s till a m atter of controvers y:
- s om e m odern approaches from com plex s ys tems theory, like s elforganization, is an alternative to biological analogies or Darwinism ;
- ‘artificial s election’ is an alternative to ‘natural selection’;
- Darwinism excludes human intentionality.
It is relatively eas y to s how that all thes e objections are ungrounded!
Universal Darw inism (Richard Dawkins – 1983) - Darwinism includes
a broad theoretical framework for the evolution of all open, com plex
s ys tems , including s ocio-econom ic s ys tems .
14 May 2004
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III. …to complete the puzzle
(cont .)
To point 4: more than blind variation plus selective retention
¾ Donald Campbell (1960) – universal evolutionary or selection theory
– all innovative des ign is produced by BV + SR and introduced the
notion of vicarious forces (inherited-acquired by learning).
¾ Gene-cultural Coevolution – m os t appropriate approach to an ETTC.
¾ Adding more points never s uitably accounted for:
6 - if ‘technique’ had not favored organism ’s pool of genes or genes
transmiss ion it had not evolved to technology; and if technology had
not favored human pool of genes or hum an genes transm iss ion it had
not continually evolved toward m ore and m ore com plex technological
s ys tems ;
7 - the human massive capacity for culture (and technology) m ay be
s een as a very s trong capacity of adaptation to res pond to very quick
s patial and tem poral variations (obs erved since the Pleis tocene);
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III. …to complete the puzzle
(cont .)
8 - the coevolutionary com plexity of m anaging tw o inheritance systems
(the vertical, genetic, and the horizontal+vertical, cultural) s erves
alm os t exclusively to the human (genetically inherited) quick capacity of
response to rapidly changing environm ents or as Richerson and Boyd
s o brilliantly s tated ‘Hum ans are b uilt for speed not for com fort’.
9 - technological evolution can not b e think as an independent evolutionary
process , but it is part (the mos t energetic one) of a broad coevolutionary s et of process es , m anifes t as a cas cade of m ultilevel,
nes ted, and s elf-sim ilar Darwinian-like process es , which on the whole
cons titutes the world s ys tem , as recently em pirically and
m athem atically demons trated by Devezas and Models ki;
10 - this s et of processes is fundamentally innovation driven (each in its
own s cale), exhibits power-law behavior and it is pois ed in the critical
boundary between order and chaos (pois ed in the s ub-criticals upracritical phas e trans ition boundary), what allows for the necessary
flexibility required to take part in the s election process at the s everal
levels of the evolutionary game.
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EU-US SEMINAR: NEW TECHNOLOGY FORESIGHT, FORECASTING & ASSESSMENT METHODS-Seville 13-14 May 2004
III. …to complete the puzzle
(cont .)
To point 5: some promising approaches
¾ Two poss ible approaches to sim ulating technological and/or s ocioeconomic s ys tems .
i - the systems dynamics approach, widely us ed in technological
forecas ting s ince the 1950’s , is “top-down” in character. It is usually
applied to human feedback s ys tems and their dynamics (behavior over
tim e) is defined via the change of their organization (or ‘s tate’) as
des cribed by the s ys tem ’s differential equations . Such top-down
analyses are very s uitable for des cribing s ys tem ’s regularities and to
identifying dom inant feedback loops , or in other words , to forecas ting
agents ’ aggregate behavior.
ii - the other approach forms the new s ub-field of “Artificial Life” ( AL)
that us es s o-called ‘s oft com puting’ m odels of com plex adaptive
s ys tems (CAS) and it is bes t characterized as a “bottom -up” approach.
Its origin rem ounts to the 1970’s with the em ergence of gaming
s imulation.
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III. …to complete the puzzle
(cont .)
¾ Theoretically and methodologically this approach makes possible the
cons truction of m odels from the level of process es that are immediately
and em pirically observable, nam ely the local interactions of single units
(agents ) governed by local rules .
9
9
9
9
9
N-K technology landscapes (Stuart Kauff man – SF Institute)
Com plex Network Analysis (scale free networks)
Cellular Automata (Stephen Wolfra m)
Percolation Models (G erald Silverberg)
Genetic Algorithms (G A’s – John Holland, David Goldberg)
modern heuristics, genetic programm ing (Digital Darwinism)
) I s ee the new s cience of Digital Darw inism bas ed on further
improvements of GA’s as the m os t prom ising candidate for es tablis hing
an ETTC, if s uccess ful in incorporating in the s im ulations the pointed
out ‘general evolutionary principles’ res umed in the conclusions .
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EU-US SEMINAR: NEW TECHNOLOGY FORESIGHT, FORECASTING & ASSESSMENT METHODS-Seville 13-14 May 2004
IV - Conclusions
Ö the common denominator t o all grow th and diff usion phenomena
in the living w orld is the transmission of inf ormation, whose continuing
evolutionary process conduces to increas ingly complex s ys tems ;
Ö cultural evolution (and technological evolution as well) is the
continuation of biological evolution by other means;
Ö technique is the most suitable basic unity of analysis and m us t be
viewed as the enduring search for bypasses (s hortcuts ) obeying the
general physical principle of t he least action;
Ö technology m us t be viewed as the further improvement of this
process by intelligent means (intentionality), poss essing both
m echanisms of variation – sim ply random (Darwinian) and intentional;
Ö human technology is a part of a biologically co-evolved massive
capacity for culture, managing tw o inheritance systems, vertical
(twofold, genetic and Lam arckian) and horizontal (pure Lamarckian), and
that s erves fundam entally to the quick hum an capacity of adaptation;
Ö technological evolution is not an independent evolutionary process ,
but it is the fas tes t and m ore energetic am ong a broad innovationdriven, co-evolutionary set of processes, the w orld system.
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Research Agenda
21
(a proposal of Tessaleno Devezas)
¾ The exi sting li terature on the si mulati on modeli ng (agent-based, geneti cprogrammi ng, etc..) of technologi cal processes i s sti ll scarce and so far I
know a speci fic seminar or symposi um objectively ori ented to thi s topi c was
never reali zed, much less wi th the scope of contri buting to the
methodological i mprovement of FTA.
¾ I propose the reali zation of an i nternational semi nar in thi s fi eld
(Technological Change as Evolutionary Process) bri nging together
speci ali sts i n evoluti onary model bui lding and digi tal Darwini sm to discuss
the exi sti ng approaches and to present thei r most recent results. As a
follow-up of thi s event we could thi nk on the edi ti on of a proceedi ngs volume
contai ning a collecti on of the most i mportant contri buti ons, which should be
more empirical than theoretical in scope. Thi s sci entifi c meeting could be
planed following the format of a recent proposal of thi s author wi th George
Modelski for a semi nar on Globalization as Evolutionary Process to be held i n
September 2005 in Pari s, sponsored by the Calouste Gulbenkian Foundati on.
¾ This proposal should be i ncluded i n the di scussion of the ‘ scoping project’ to
create the Global M eta-Network for Strategic Foresight.
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