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Learning, Evolution and Complexity in
Innovation Systems - A Veblenian Approach
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FOREWORD
Based on master thesis in economic geography, University of Oslo (1994): Selv-organisering: et
evolusjonært perspektiv på relasjoner mellom læring og kontekst. (Self-organisation: an evolutionary
approach to relations between learning and context).
1
CONTENTS
FOREWORD......................................................................................................................................................... 1
1
ITRODUCTIO ........................................................................................................................................ 3
2
ISTITUTIOAL AD EVOLUTIOARY POLITICAL ECOOMY ............................................... 3
3
THE IOVATIO SYSTEMS APPROACH ......................................................................................... 7
3.1
3.2
4
The Emergence of the Innovation Systems Approach ........................................................... 7
Theoretical Origins of the Innovation Systems Approaches.................................................. 7
IOVATIO SYSTEMS AD COMPLEX SYSTEMS........................................................................ 9
4.1
Complexity Science ............................................................................................................... 9
4.1.1 An Emerging Worldview................................................................................................... 9
4.1.2 The Science of Complexity and Complex Systems Theory ............................................ 11
4.1.3 Different Kinds of Complex Systems .............................................................................. 12
4.2
Relations between Innovation Systems and Complex Adaptive Systems ........................... 14
4.3
Illustration of Some Ideas from the Complexity Field......................................................... 16
5
SUMMIG UP ........................................................................................................................................... 19
REFERECES .................................................................................................................................................... 20
2
1
INTRODUCTION
In part one of this paper Thorstein Veblen’s approach is taken as the point of departure for discussing
theories of learning and evolution as the basis for a systemic approach to innovation. In part two,
innovation systems as an evolutionary and political-economic approach to studying innovation,
learning and technological change is introduced. In part three, the idea that complex systems theory
can be a way to theorise the workings of innovation systems is introduced.
2
INSTITUTIONAL AND EVOLUTIONARY POLITICAL ECONOMY
In the last part of the 19th century Charles Sanders Peirce and the later pragmatists were attracted to
Darwinism because it seemed to recognise the randomness, creativity and spontaneity in the universe.
This orientation towards an evolutionary, in contrast to a mechanistic, worldview in American
philosophy is mirrored in Thorstein Veblen’s famous article from 1898, «Why is economics not an
evolutionary science? ». Institutionalism in its origins blended a sensitivity to culture with a desire to
be scientific. This is a constant feature of the economic and methodological writings of Veblen. He
was clearly awake to the sociology of knowledge and the interpretative qualities of social science,
features that where largely neglected in mainstream economics. Simultaneously he stressed the
scientific character of economics and hence, implicitly, was adopting a sophisticated, culturally
informed version of naturalism and realism.
According to Rick Tilman (1993), when Veblen demanded an «evolutionary» economic science, what
he wanted was one that would be a science of collective welfare: that is, a means of advancing the
instrumental adaptive powers in the entire community to ceaseless change. Rather than emphasising
the capitalisation of intangible assets for personal gain he valued high the industrial arts as a
community possession. In a properly developing society workmanship, parenthood and idle curiosity
would flourish, community qualities wherein Veblen found embedded some transcultural set of values.
This was the foundations for Veblen’s development of an alternative theory of value to both neoclassicism and classical Marxism which he was critical of. When, in his evolutionary, i.e. Darwinian,
mode of analysis, he wrote of the «generic ends» of life «impersonally considered» and of «fullness of
life», the existence of some such transcultural value set was implied. In his critique of neo-classical
economics he labelled the deficiencies «predarwinian», i.e. rooted in a pre-evolutionary mind-set
which had not yet come to terms with the scientific revolution for which Charles Darwin was the chief
catalyst (Tilman 1993).
The critique of Veblen for not developing a systematic theory is, according to Tilman, a
misunderstanding that could have been avoided if his critics had been more cognisant of the
significant influence on his theoretical system by Charles Darwin and the Darwinian revolution in
Western thought. Darwinism is the most important in grasping the essentials of Veblen’s naturalistic
theory. It is important to point out that it was not the conservative «social Darwinism» of a.o. Herbert
Spencer he appropriated. On the other hand
«Veblen took from Darwin the essential scientific method, which studies humanity in its process of
continous adaption to both its social and natural environment and which sees the conditions of
human existence as subject to ceaseless change. Although he was not always consistent in his use
of Darwinian principles, it is impossible to appreciate the systematic nature of his theory without
grasping its Darwinian origins» (Tilman 1993: xxvi. My emphasis).
When we look at the histories of economic and biological thought, we discover a remarkable crossfertilisation of ideas. As noted above, in Peirceian terminology this can be seen as abductions between
different contexts. For instance, as John Foster points out,
3
«(e)conomic imagery was attractive to biologists because, from Adam Smith onwards, it could be
viewed as ‘scientific’ in the sense that there was a metaphorical link with the physics of Newton»
(Foster 1994a: 23).
In light of these intellectual roots, and the set of values and processes Veblen emphasised, his
notorious question can, and should, be repeated anew on the basis of Lawson’s argument that
«recent developments in the philosophy of science systematized under the heading of realism or,
more accurately, critical realism and more consistent with the institutionalist writings of, say,
Veblen, provide an alternative conception of economics as a science» (Lawson 1994: 70).
In this alternative conception, the formulation of which is a still ongoing process, economics must be
seen as a realist evolutionary science based on a sophisticated form of naturalism. Such a
reconstruction of the work of Veblen can be based on the philosophies of a.o. Peirce (1931-58) and
Bhaskar (1978). Moreover, it should be supplemented with a parallel reconstruction of the work of
John Commons, which, as in the case of Veblen, contains much theoretical stuff that could be of
significant interest to contemporary heterodox economics.
Veblen regarded the economy, and its institutions and structures, as an inherently dynamic process;
social structures can not be taken as given and static. That is why there is always movement and flux
in Veblen’s perspective. Even if social structures are seemingly reproduced relatively intact in areas
confined in time and space, this is always done on the basis of immanent dynamic, and potentially
transformational, human practice. Relative change and relative continuity is fundamental in social life.
That is why institutions, structures and systems of human practice and social interaction have to be
regarded as processes in constant evolution and constant reproduction. These changes do not come as
a result of external or exogenous chocks, but as an integrated part of what the actual system or
phenomena is in and of itself. The great challenge for Veblen and for institutional economics after him
is therefore «the economic life process still in great measure awaiting theoretical formulation» (Veblen
1919: 70). Two of Veblen’s characteristic formulations give the essential aspects of reality that should
be taken as a basis for such theorising. There must be developed a
«theory of a process of cultural growth as determined by the economic interest, a theory of a
cumulative sequence of economic institutions stated in terms of the process itself» (Veblen 1919:
77).
According to another typical formulation of his, such a theory is
«substantially a theory of the process of consecutive change, which is taken as a sequence of
cumulative change, realized to be self-continuing or self-propagating and to have no final term»
(Veblen 1919: 37).
The flavour of these formulations is unmistakably Darwinian, and it is from such a conception of
evolution that Veblen derived at his important concept of cumulative causation. For Veblen
«the Darwinian scheme of thought ’is a scheme of blindly cumulative causation, in which there is
no trend, no consummation’. Evolution was thus seen, as it was for both Malthus and Darwin, as a
continuing and endless process without finality or goal» (Veblen 1919: 436, in Hodgson 1994:
128).
This combined naturalist and evolutionary perspective can constitute a basis for the working out of a
theoretical framework for analysis of social shaping of technology and the socioeconomic contexts
processes like these takes place within.
A relevant point of departure for this kind of work is the fact that Veblen, as early as the beginning of
this century, argued that the accumulated knowledge of a population is the most important capital in a
community. This knowledge has evolved from the practical needs connected to the maintenance of
and the continuity of the life process. His emphasis on the general underlying preconceptions of this
knowledge points towards the existence of objective truth which content is not influenced by the
4
practical needs of the knower. Such truth is only perceived because of its usefulness, because correct
perceptions are more useful than wrong ones in the life process. Veblen focused on the importance and
discourse forming role of preconceived meanings in economic and other thinking. He argued that
«(the) ultimate term or ground of knowledge is always of a metaphysical character. It is something
of a preconception, accepted uncritically, but applied in criticism and demonstration of all else...»
(Veblen 1919: 149).
According to Warren Samuels, Veblen was one of the earliest writers on the sociology of knowledge.
He was concerned with the social construction of meaning rather than absolute categories of truth and
with the formation of knowledge or beliefs, or both, as a product of group or community life in
particular institutional and cultural contexts (Samuels 1990).
Veblen’s coupling of concepts about objective truth and knowledge is closely connected to his concept
of cumulative causality and to human actors understanding and utilisation of the connection between
causes and effects, both in nature and society. He considered this collective knowledge to be far more
important than its embodiment or appearance in physical capital or technology. This can be illustrated
with regard to his view on the industrial arts or skills of nations;
«the industrial skill of a nation consists of a set of relevant habits, acquired over a long time, widely
dispersed through the employable workforce, reflective of its culture and deeply embedded in its
practices» (Veblen 1914, in Hodgson 1993: 133).
In this classical or «old» institutional perspective one can say that the essential core in evolutionary
economics hinges upon the concepts of process and change. Accordingly technology has been
considered as a, if not the, fundamental dynamic power in economic, social and cultural
transformation. Technology has thus played a significant role in evolutionary thought since the days of
Veblen. The transformational power and practice of the industrial community was the main point of
departure in Veblen’s perspective;
«The active material in which the economic process goes on is the human material of the industrial
community. For the purpose of economic science the process of cumulative change that is to be
accounted for is the sequence of change in the methods of doing things - the method of dealing
with the material means of life» (Veblen 1898: 384).
In this perspective technology is, first and foremost, and in a wide sense, human ideas and their
embodiments in artefacts connected to problem definitions and problem solutions developed in a
wider framework that can be termed culture. Within such cultural and institutional frameworks there
takes place a continuous consideration and evaluation of values according to a community’s social and
political goals. Included in this process is the social shaping of technology (Jennings and Waller
1994).
Based on this Veblenian view, the economy and the economic life process of a community are to be
interpreted as important and very complex kinds of learning processes in societies where creativity,
innovative capacity, capacity to break path dependency and change technological trajectories become
important characteristics of actors. Thus Veblen anticipate by many years the now so popular concept
of «the learning economy», which, one the one hand, can be seen to belong to a wider, general class of
learning systems or knowledge-based networks and is thus one of several learning sub-systems in a
complex society. On the other hand, «the learning economy» is itself constituted by a wide variety of
learning sub-systems at micro, meso and macro levels, including firms, inter-firm networks and
innovation systems.
Following this emphasis on learning, creativity and knowledge, a challenge for evolutionary economic
theories consists in explaining the endogenous transformations of the knowledge used in economic
systems. Among other things this amounts to explaining the continuous changes, adaptations and
5
diversities in the decision rules, products, production methods and organisational forms that we find in
economic practices. A basic evolutionary process that is interesting in this connection is one involving
«agents who can change their behaviour in an irreversible manner through a self-generated
process» (Andersen 1994: 15).
An endogenous explanation of such phenomena must necessarily focus on the mechanisms and
tendencies in the evolutionary process. Given the result of a supposedly evolutionary process one must
try to identify the mechanisms and tendencies that have generated this result or outcome. An approach
to explanation based on underlying mechanisms and tendencies is due to the fact that innovation and
its sources play a significant role in evolutionary processes. When one studies an object that changes,
something about that object has to be unchanged, an essence that makes it possible for us to identify
that which changes. Through abduction one has to go from the empirical result of a supposedly
evolutionary process and try to identify and analyse the specific systems of social practice that realise
the potentials that are the underlying source of the results. We must try to discover a tendency or a
fundamental principle that makes it possible to develop explanations. The synthetic character of
evolutionary economic studies is, to a large degree, due to the fact that
«the mechanism underlying economic evolution is very complex; this mechanism should be
thought of as a synthesis between different (sub)mechanisms rather than as a single mechanism
whose parts can be considered as black-boxes. A basic task is to show how an evolutionary process
can be synthesised from these individual mechanisms» (Andersen 1994: 14).
This means that contributions from different theoretical fields, that can (potentially) deepen and
elucidate different mechanisms or different aspects of mechanisms, have to be synthesised in a
common methodological frame to develop insight in evolutionary processes. This could be based on a
modern, critical naturalism. This is in line with the point stated earlier that critical realism as such do
not legitimate any specific substantial theories and perspectives. Rather than focusing one-sidedly and
detailed on the individual mechanisms, it is the syntheses between the different theories that are the
core of an evolutionary perspective. This synthetic view can be further pursued through the
combination of interactive learning theory, evolutionary theory and complex systems theory.
The methodological position argued for here is thus critical realism and the mode of inference termed
abduction or retroduction. Concerning development of knowledge the American philosopher and
founder of pragmatism Charles Sanders Peirce argued that
“there occurs in science and in everyday life a distinct pattern of reasoning wherein explanatory
hypotheses are formed and accepted. He called this kind of reasoning ‘abduction’, a form of
inference that goes from data describing something to a hypothesis that best explains or accounts
for the data. Thus abduction is a kind of theory-forming or interpretive inference” (Josephson and
Tanner 1994: 5)
In a critical realist interpretation (Bhaskar, 1978, Lawson 1989, 1994), the goal of abductive or
retroductive processes is to utilise existing knowledge and frames of reference to find theoretical
patterns and deep structures (at the real level), which, if they where correct, would provide the means
to conceptualise observed empirical patterns and surface structures (at the actual level). Critical
realism as such does not legitimate any specific substantial theories and perspectives. So, rather than
focusing one-sidedly and detailed on the individual mechanisms, it is the syntheses between the
different theories that are the core of an evolutionary perspective.
6
3
THE INNOVATION SYSTEMS APPROACH
The ongoing research program called «innovation systems» is a broad, synthetic approach focusing on
the importance of innovation, creativity, learning and knowledge in the economy. The basic idea of the
«innovation systems» approach is to take into account a wide range of factors and conditions that may
influence on processes of innovation, processes of creativity, processes of learning and processes of
knowledge development.
3.1
The Emergence of the Innovation Systems Approach
In the growing literature on innovation systems several basic types have been identified and discussed
(Edquist 1997). According to Chris Freeman, the concept of «national system of innovation»
originated in the preparations to part 5 of Dosi et al. 1988, when Bengt-Åke Lundvall proposed this as
the title of that part. Moreover, the term was used by several authors in the book. But Freeman himself
was the one who first used the term in printed form in his book on economic performance and
innovation and technology policy in Japan (Freeman 1987). Two books on national systems of
innovation published in the early 1990s have had an enduring significance on the concept. The books
where edited by Bengt-Åke Lundvall (Lundvall 1992) and Richard Nelson (Nelson 1993).
Bo Carlsson and his colleagues have developed the concept of «technological systems» within a
research program on factory automation in Sweden. An industry specific concept of innovation
systems have been developed by Stefano Breschi and Franco Malerba under the term «sectoral
innovation system» (Breschi and Malerba 1997). The concept of «regional innovation systems» has
been developed as a combination of evolutionary innovation theory and studies of regional economic
development within economic geography and regional science. Among the many variants of this
concept the contributions of Asheim and Isaksen (1997) and Brazyk et al. (1997) could be mentioned.
The various perspectives emphasise different aspects of innovation processes but the also have
significant similarities which «allow them to be clustered together as variants of a more general and
broadly encompassing systems of innovation approach» (Edquist 1997: 3).
3.2
Theoretical Origins of the Innovation Systems Approaches
Three of the main originators of innovation systems theory - Carlsson and Stankiewicz (1991), Nelson
and Rosenberg (1993) and Lundvall (1992) - are all committed to the idea that technological change is
an evolutionary process. Not only is the systems of innovation approach compatible with evolutionary
theories of innovation but there is a close affinity between the two. Moreover, interactive learning
theories, as developed by Lundvall and his colleagues (Lundvall 1992), are often considered an
important theoretical input to the approach. According to Edquist (1997),
«(t)he systems of innovation approach is compatible with the notion that processes of innovation
are, to a large extent, characterised by interactive learning. It could be argued that some kind of
systems of innovation approach is inherent to any perspective that sees the process of innovation as
interactive: interactivity paves the way for a systemic approach» (Edquist 1997: 5)
Thus evolutionary theories of technical change together with theories of interactive learning constitute
origins of the systems of innovation approach. The origins of the systems of innovation approach may
be set in a wider theoretical context, because according to Edquist,
«(t)he systems of innovation approach...allows for the inclusion not only of economic factors
influencing innovation but also of institutional, organisational, social, and political factors. In this
sense it is an interdisciplinary approach. Perhaps it might best be labelled a ‘political-economic’
approach» (Edquist 1997: 17. My emphasis).
7
Thus, the systems of innovation approach may be further developed through the integration of
theoretical insights from political economy. Such work could be based on the evolutionary approach to
political economy developed by de la Mothe and Paquet (1996).
8
4
INNOVATION SYSTEMS AND COMPLEX SYSTEMS
As pointed out by Andersen (1997) researchers within evolutionary economics have started to extend
their area of interest to the innovative evolution of complex systems. This extension appears to suggest
many interesting theoretical tasks, driven by the situation that, in the application of evolutionary
analysis to innovation systems, there is a need for inventing and applying a fairly large set of new
concepts that can cope with the analysis of evolution within and of complex systems. This raises
several questions that are pursued in this part. First, the emerging theoretical field of complex systems
is introduced (section 4.1.). Second, complex systems as a theoretical approach to innovation systems
are introduced (section 4.2.). Third, the synthetic character of the emerging approach is illustrated with
John Foster’s theorising on self-organised learning in social systems (section 4.3.).
4.1
Complexity Science
4.1.1 An Emerging Worldview
As a result of the demise of the logical positivist model of science, social scientists has not paid much
attention to the significant changes taking place in many scientific fields, changes which permit for the
first time a real rapprochement of physical, biological and social sciences on equal footing. In this
sense, the study of scientific and philosophical conceptions of «epistemic strategies», «emergence»
and «structure» is extremely important for social theory. The message one gets from the recent
developments in these domains is that the physicalist, Newtonian-Laplacian (dogmatic, empiricist,
mechanistic, equilibrium-based, atomistic) model of the world is superseded by a more robust,
emergentist one, hospitable to the habitus of social scientists.
The Newtonian world view may be viewed as part of the macrostructure of information and
knowledge that emphasises and usually tolerates only the following; rationalism; reductionism; parts
isolated from wholes; detached objectivity of observation and measurement and separation of the
observer from the observed system; simple causality; logical, step-like but iterative analysis; deduction
of rules, procedures, and algorithms; maximum use of numbers; emphasis on average behaviour;
equilibrium; fixed, inviolable laws; reversibility; denial of variety and ambiguity; denial of
subjectivity; and convergent focus on the correct answer or solution. The Newtonian world view is
best fitted to a static or slowly changing world of stability and structural continuity, not one of
evolution, instability, and structural change.
The new «emergentist» research programs are a broad and loosely defined meta-theoretical position
now flourishing in many scientific domains (Kontopoulos 1993). These perspectives have emerged as
alternatives to the Newtonian world view, which has come under increasing scrutiny and criticism.
Specifically, the new worldview has emerged as a result of the tremendous upheaval and
transformation of the fields of evolutionary biology by a combination of many sets of interdisciplinary
theoretical, modelling, methodological and empirical work. But indeed, the revolution involves all the
domains of physical and biological sciences: quantum mechanics and cosmology, many-body physics,
non-equilibrium thermodynamics, constructionist physical chemistry and molecular biology, postdynamic ecological models, neuroscience, models of neural networks and distributed parallel
processing in artificial intelligence studies1. The message of this revolution for the social sciences is
1
Regarding phenomena in the natural sciences it can be argued that ideas of complexity and self-organisation are
not new. To illustrate this, an important aspect of the work of Charles Sanders Peirce can be referred to. There
has occurred in the natural sciences a profound change where the classical (Newtonian), static, mechanical and
deterministic image of the natural laws have been undermined, first by the theories of relativity and later more
dramatic by quantum mechanics, cosmology, chaos theory and complexity theory. What is amazing is that much
of this development was anticipated by Peirce as part of his hypothesis that the laws of nature themselves are
results of an evolutionary process. This can be illustrated with the following citation from Joseph Brent’s
9
that arguments on behalf of the tremendous complexity of social processes are warranted. For the first
time, models, tools and metrics from the physical and biological sciences appear to begin to capture
that complexity. All the more important for social scientists therefore, to be aware of these ongoing
transformations in the scientific practices of these fields.
The new «emergentist» research programs themselves encompasses several crucial elements; nonrationality; non-linearity; non-equilibrium; mutual causality; irreversibility; stochasticity/
determinism; uncertainty; opportunity and choice seen in fluctuations and apparent noise; the
dominance of exceptions near critical thresholds; the generation and maintenance of variety; structural
change; complexity; self-organisation; divergent thinking; the recognition that there can never be
eternal truth and reality but only different perceptions of such. Since these views are quite new and not
widely known, a survey of the major advances in the physical, biological and cognitive sciences,
bearing on the issue at hand, seems indispensable. It could then be shown that a new convergent model
has emerged - a non-reductive, non-equilibrium, multilevel conceptualisation of phenomena, which is
currently revolutionising these sciences and which could provide support for a different and more
successful recasting of the notion of complex human and social systems. There seem to be
convergence on the points i) that the world provides ample evidence of emergence; ii) that it forms a
level structure; and iii) that the proper approach to the world should be based on a robust nonreductive materialist or «integrated pluralist» philosophy of science. This new, emerging convergence
between philosophical, scientific and social scientific views can steer a way between the Scylla of
reductionism and the Charybdis of collectivist functionalism toward more robust, intermediate,
complex but self-consistent conceptualisations of the world and, in the present context, of complex
human and social systems2.
biography of Peirce. There is an important point here that almost everything that has been written about selforganisation, both within the natural and the social sciences, explicitly or implicitly refers to the work of Ilya
Prigogine;
“this extraordinary lecture (‘Design and Chance’ 1884) has great importance for understanding the uncanny
accuracy of Peirce’s thinking about physical reality in the light of modern discoveries. This prescience was
pointed out in 1984, one hundred years after Peirce’s lecture at Johns Hopkins titled ‘Design and Chance’, by
the Nobel Prize-winning chemist and one of the founders of the ‘new physics’ of chaos, Ilya Prigogine,
among the few modern theoreticians of science to have read Peirce. Prigogine showed how Peirce’s view of
time and the second law of thermodynamics anticipated the ‘new physics’ which derives order out of chaos
by means of the idea that very small, chance differences can quickly create ‘self-organized’ large-scale
uniform effects - that the physical world we perceive is characterised by extremely sensitive dependence on
initial conditions, a fact which Peirce himself had pointed out” (Brent 1993: 175).
2
As this new paradigm has been further developed it has become clear that complexity and self-organisation are
not new ideas in economic theory either. Well known phenomena that has been studied in a self-organisation
perspective encompasses e.g. labour markets, finance markets, diffusion of technology and localisation of
activities. Five examples can illustrate this;
i) Adam Smith’s «invisible hand» has been interpreted as a self-organising process;
ii) Alfred Marshall’s motto from «Industry and Trade»: «The many in the one, the one in the many» and other of
his methodological texts are has also been interpreted in the same way;
iii) Friedrich Hayek’s notion «spontaneous order» and
iv) Joseph Schumpeter’s concept «creative destruction» constitutes also some quite evident cases of selforganising processes.
v) Thorstein Veblens concept of «the economic life process», the core of his evolutionary economics, can be
interpreted as a self-organising process. The point of departure for such an interpretation can be based on his
view of what such a process is;
«The active material in which the economic process goes on is the human material of the industrial
community. For the purpose of economic science the process of cumulative change that is to be accounted for
10
4.1.2 The Science of Complexity and Complex Systems Theory
As part of this emergentist research program the science of complexity and complex systems theory is
yet rather unknown and little used in the social sciences. It comprises a possible meta-theoretical
approach to specific fields of research and is a potentially rich source of insights and new perspectives
for the social sciences. It can be broadly characterised as a transdisciplinary, non-empiricist
epistemology which require a non-atomistic ontology and which utilises both qualitative and
quantitative methodology to explore different aspects of the objects of study. The objects of study are
the wide class of complex, adaptive or learning systems and networks of diverse kinds (Mitchell
Waldrop 1992). This epistemology is, for instance, the foundation of some recent work within
organisational theory that is of interest when studying creativity, learning and innovation (Stacey
1996, Nonaka & Takeuchi 1995, von Krogh, Roos & Slocum 1994).
According to Maintzer (1996), the complexity approach is an interdisciplinary methodology to explain
the emergence of certain macroscopic phenomena via the non-linear interactions of microscopic
elements in complex systems. One of the crucial points of the complexity approach is that, from a
macroscopic point of view, the development of political, social or cultural order is not only the sum of
single intentions, but the collective result of non-linear interactions. According to Stacey (1996), the
complexity approach adds an important dimension to the conventional view on causal explanations
within science. It put forward
«a different theory of causality, one in which creative systems are subject to radical
unpredictability, to the loss of the connection between action and long-term outcome. The purpose
of the theory and the research is then to indicate how conditions might be established within which
spontaneous self-organization might occur to produce emergent outcomes» (Stacey 1996: 264).3
The concepts of self-organisation and complexity are postulated to have profound consequences for
different sciences;
«(t)he paradigm that is moving both the ‘natural’ and the ‘social’ sciences away from models built
upon assumptions about input and output, externalities, and optimal equilibrium states is that of
self-organization» (Lee 1994: 165).
“Self-organization refers to the study of the global behaviour of complex dynamic systems as it
results from the interactions of a large number of subsystems. As such this paradigm encompasses
many phenomena ranging from natural sciences to social ones” (Ngo Mai & Raybaut 1994: 513).
As argued above, the increasing recognition of uncertainty, nonlinearity and unpredictability in the
natural realm has piqued the interest of social scientists in these new discoveries. Complex systems
theory appears to provide a means for understanding and examining many of the uncertainties,
nonlinearities and unpredictable aspects of social systems behaviour. Complex systems theory is the
result of natural scientists’ discoveries in, among other realms, the field of non-linear dynamics. Nonlinear dynamics is the study of the temporal evolution of non-linear systems. Non-linear systems
reveal dynamic behaviour such that the relationships between variables are unstable. Furthermore,
changes in these relationships are subject to positive feedback in which changes are amplified,
breaking up existing structures and behaviour and creating unexpected outcomes in the generation of
new structure and behaviour. These changes may result in new forms of equilibrium; or even temporal
is the sequence of change in the methods of doing things - the method of dealing with the material means of
life» (Veblen 1898: 384).
A theory of such a process should be
«(the) theory of a process of cultural growth as determined by the economic interest, a theory of a cumulative
sequence of economic institutions stated in terms of the process itself...(or)... substantially a theory of the
process of consequtive change, which is taken as a sequence of cumulative change, realized to be selfcontinuing or self-propagating and to have no final term” (Veblen 1919: 77, 37).
3
The concept of self-organisation is discussed in Samuelsen (1994a, 1994, 1994c).
11
behaviour that appears random and devoid of order, the state of «chaos» in which uncertainty
dominates and predictability breaks down (Kiel and Elliott 1996).
In all non-linear systems the relationship between cause and effect does not appear proportional and
determinate but rather vague and, at best, difficult to discern. The social realm is clearly non-linear,
where instability and unpredictability are inherent, and where cause and effect are often a puzzling
maze. Non-linear systems are historical systems in that they are determined by the interactions
between the deterministic elements in a system’s history and «chance» factors that may alter its
evolution. In systems operating in a chaotic regime, this fact is referred to as sensitive dependence on
initial conditions. In short, the combination of factors that defines the initial conditions of the
phenomenon and the insertion of chance elements during its «life» may generate very divergent
outcomes from systems that initially appeared quite similar. This distinguishes chaotic behaviour from
truly random behaviour. In a genuinely random system, such a system is insensitive to its initial
conditions (Kiel and Elliott 1996).
Uncertainty is also an important element of non-linear systems since the outcomes of changing
variable interactions cannot be known. Thus, the complexities of both internal dynamics and
environmental «disturbances» generate considerable uncertainty during change processes in non-linear
systems. Furthermore, a wide and complex array of possible outcomes is available to non-linear
systems. This is particularly true during chaotic regimes. As a result, any effort at long-term prediction
in non-linear systems is highly suspect. The dynamics in the relationships between variables over time
in non-linear systems may generate complexities that defy generalisation. This difficulty in developing
such generalisations underscores the challenge of building theories that are relevant to complex social
phenomena.
4.1.3 Different Kinds of Complex Systems
The complexity approach is an interdisciplinary research program in which several natural and social
sciences are engaged. It explains what we can know and what we can not know about non-linear
dynamics in nature and society (Maintzer 1996). This means that the properties of complex systems
may be studied on different levels according to the structured ontology outlined in chapter two. One
basic dividing line is between non-living and living systems. Thus, at the bottom of this hierarchy of
complex systems, we find inanimate systems typically studied in physics and chemistry. They can be
termed deterministic feedback networks (Stacey 1996). Next, there is an equally important dividing
line within the realm of living systems between those typically studied by biologists and ecologists,
termed adaptive feedback networks (Stacey 1996) and those studied by social scientists, ranging from
small groups, via organisations to innovation systems and economies. Overlapping and blurring the
dividing line between different kinds of living systems are the studies of human beings as learning and
creative actors, typically performed in cognitive science and psychology. In addition to these, recent
advances in computer technology have facilitated the simulation of the complex nature of natural and
human systems.
i) Deterministic Feedback Networks
A deterministic non-linear feedback system is a network of agents whose behaviours are determined
by a common schema consisting of a few rules that are fixed over time, apply to all agents without
exception and do not have to do with achieving some purpose. It follows that agents do no adjust their
behaviour in light of their consequences for a particular purpose. In other words, there is no learning
of any kind. This kind of system is fare removed from human systems and concern basically inanimate
physical and chemical systems. Examples of deterministic feedback systems could be a simple
pendulum, the weather or a population of organisms at macro level.
12
ii) Adaptive Feedback Networks
At its simplest, an adaptive non-linear feedback system is a network consisting of a large number of
agents, each of whose behaviour is determined by a shared schema consisting of a few rules that are
fixed over time and that apply to all agents without exception. However, in contrast to deterministic
non-linear feedback systems, even the simplest adaptive systems has some purpose, namely to perform
some task. It follows that, unlike agents in deterministic systems, agents in all adaptive systems adjust
their behaviour in light of its consequences for their purpose. In other words, adaptive systems learn,
at the very least, in a simple single-loop manner, whereas deterministic systems do not. These systems
mainly encompass living biological systems. The complexity approach can, by computer simulations,
show that non-linear feedback, operating at critical points in system parameter values, causes
spontaneous self-organisation among agents, which in turn causes new patterns of behaviour. The
system parameters are: i) energy and information flow; ii) agent connectivity; and iii) schema
diversity. It seems that a self-organising capability is an inherent property of a complex adaptive
system operating in certain conditions.
iii) Complex Systems in the Social Sphere
Complex systems are dynamic rather than static, evolve or are driven into domains of instability, and
emerge into new structures. Following this, human systems have the same basic structure as all other
complex adaptive systems. But the difference between complex adaptive systems in general and
human and social systems consists of the specific internal structure and human nature of the agents in
human and social systems. Some of the basic aspects of human nature that interest us here are
creativity and innovation. In a complex systems perspective these are nested processes. Each level
(individual, group, organisation, etc.) is itself a complex adaptive system that is a component of an
even larger complex adaptive system, with behaviour at each level ultimately affecting and being
affected by behaviour at all other levels, to produce a coevolutionary trajectory through time (Stacey
1996). This in turn means that it is to be expected that at these levels, too, the properties of creativity
and innovation will display the same self-similar pattern as at the other levels. Focusing at the level of
an industry, an economy, or a society, they must have the same characteristics as those at the level of
an organisation, a group, or an individual. They too must be conditioned by a set of control parameters
that are set at critical points. The following control parameters are specified by Stacey (1996): i) the
speed of information flow; ii) the extent of differences expressed and worked with; iii) the richness of
interconnections between agents in the system; iv) the levels of contained anxiety; v) the degree of
power differences, as well as the way in which power is used. For each kind of complex adaptive
system in the social sphere, the working of these control parameters must be investigated in more
detail.
Much research into these matters are going on at present, focusing particularly at applications of
complexity science to the study of technological innovation, learning organisations and networks of
organisations (Allen 1994, Leydesdorff and van der Besselaar 1994). Among the issues investigated
are; the meaning of creativity in complex adaptive social systems; intra-firm co-operation and
collective action; labour-management relationships; the importance of involvement, worker
participation, loyalty, commitment; the importance of trust and flat and egalitarian organisations;
vertical vs. horizontal flows of communication and information; governance structures and power
relations; formal system vs. informal systems; networks of inter-firm co-operation; the importance of
spatial proximity of interacting firms; the generation of new knowledge by combining internal and
external learning; horizontal inter-firm networking promoting technological co-operation; the degree
of concentration of the network structure.
Another strand of research in this broad field sees the global economy as a complex adaptive process.
Some of the characteristics of this process has been described by John Holland (1988): i) it is
composed of networks of agents acting in parallel and its control tends to be highly dispersed; ii)
13
agents are acting on the basis of representations or mental models of the process; iii) it has many
building blocks and levels of organisations, with all sort of tangling interactions across levels; iv) the
building blocks are continually revised and recombined as the system accumulates experience; v) it is
characterised by perpetual novelty.
4.2
Relations between Innovation Systems and Complex Adaptive Systems
Some of the literature on innovation systems refers to the concepts of complexity and complex
systems. These terms are used to characterise and conceptualise aspects and properties of innovation
systems and processes taking place within such systems. According to Edquist (1997: 1), the
«processes through which technological innovations emerge are extremely complex». This is due to
the fact that these processes
«have to do with the emergence and diffusion of knowledge elements (i.e. with scientific and
technological possibilities), as well as the «translation» of these into new products and production
processes. This translation by no means follows a «linear» path from basic research to applied
research and further to the development and implementation of new processes and new products.
Instead, it is characterised by complicated feedback mechanisms and interactive relations involving
science, technology, learning, production, policy and demand» (Edquist 1997: 1).
This noted complexity is one of the reasons that firms never innovate in isolation and therefore also a
reason that the different innovation systems approaches have emerged as conceptual frameworks to
deal with the complex phenomenon of innovation. This is reflected in Edquist’s discussion of the
concept of «system» in the emerging approach, a concept that he finds problematic in all the original
contributions to the approaches (Carlsson and Stankiewicz 1995, Nelson and Rosenberg 1993 and
Lundvall 1992). To make some clarification, Edquist thinks it illuminating to relate the innovation
systems concept to a general systems concept. Here, the term «system» refers to
«complexes of elements or components, which mutually condition and constrain one another, so
that the whole complex works together, with some reasonably clearly defined overall function»
(Fleck 1992: 5, in Edquist 1997: 13).
Thus, the notion of complexity is invoked at a basic level of the innovation systems approach. This is
related to one of the common characteristics of the innovation systems approaches identified by
Edquist, viz. that these approaches
«can be characterised as «holistic» in the sense that they have the ambition to encompass a wide
array - or all - of the determinants of innovation that are important - whether it is in a national,
regional, or sectoral context» Edquist 1997: 17).
Thus, the innovation systems approaches stand in contrast to «reductionist» approaches where
potentially important determinants of innovation are excluded a priori.
Another implication of the systems view adopted is that interdependence and interaction between the
elements in the systems is focused upon. In the innovation systems approach, innovations are not only
determined by the elements of the system but also by the relations between these. To describe a system
of innovation it is therefore not sufficient only to enumerate its elements. The relations between the
elements must also be addressed. These relations are extremely complex and often characterised by
reciprocity, interactivity, and feedback mechanisms in several loops. They are clearly not
characterised by unilateral and linear causal relationships.
According to Andersen (1997: 174) evolutionary economic research «has started to extend its area to
the innovative evolution of complex systems, which appears to suggest many interesting theoretical
tasks». This strand of research is now also influencing the innovation systems approach, as the
evolutionary approach frequently appears to be the choice of theoretical framework of many
researchers engaged in the initially descriptive studies of innovation systems. But as Andersen (1997:
174) argue, this cross fertilisation between «two very different types of research is not at all arbitrary
14
but is founded in a deep affinity between innovation systems research and evolutionary theorising».
But in the application of evolutionary analysis to innovation systems «there is a need for inventing and
applying a fairly large set of new concepts that can cope with the analysis of evolution within and of
complex systems» (Andersen 1997: 175). Some of the interesting theoretical tasks of the extension of
evolutionary theorising pointed out by Andersen (1997) are related to properties of innovation
systems. One problem is the complex issues stemming from the fact that within innovation systems
there is microevolution (at organisation or firm level), mesoevolution (at industry, network or cluster
level) and macroevolution (at systems level). Another problem that becomes obvious when dealing
with innovation systems in evolutionary terms is that they may encompass many different selection
mechanisms, i.e. innovations have to go through many «filters» on their way from idea to commercial
product or process. A final problem for evolutionary analysis is pointed out by Andersen (1997: 175):
«innovation systems are so complex that it is obvious that their «optimality» and «efficiency» cannot
be determined in any strict sense». These problems are further pursued by Andersen and Lundvall
(1997) in their analyses of what is variously termed complex economic systems, complex industrial
systems, complex production systems or complex innovation systems.
The deep affinity between innovation systems research and evolutionary theorising has been further
investigated by Saviotti (1997). His point of departure is that the concept of national innovation
systems, and, by implication, innovation systems in general,
«owes its origin to the strong historical and institutional specificity displayed by different countries,
properties that in more abstract terms can be interpreted as path dependency, irreversibility and
multistability. Evolutionary theories predict the existence of all these properties and can be very
useful in explaining the nature of the NSI» (Saviotti 1997: 181).
Thus, he discusses evolutionary theories that are now emerging from the convergence of a number of
disciplines and research traditions. Saviotti discuss systems theory, non-equilibrium thermodynamics,
biology, organisation theories and economic antecedents of evolutionary theories. The main findings
of relevance for innovation systems analyses from his investigations of these strands of evolutionary
theorising are the following:
«1. Qualitative change, or change in the composition of the system, resulting from the balance of
variation, the creation of new «species», and selection, which is based on differential adaptation.
Inheritance too affects the rate and type of qualitative change.
2. Uncertainty, path dependency, and multistability, all features arising from the out-of-equilibrium
nature of systems and processes.
3. Heterogeneity of agents, requiring a population approach, emphasizing not only representative
agents and mean values of properties, but also their distribution within a population» (Saviotti
1997: 185-186).
This investigation of different conceptions of complexity within the innovation systems research
community indicate that innovation systems may be classified as belonging to a wider class of
systems, viz. complex adaptive systems. An interpretation of innovation systems in the terms of
complex adaptive systems have not been pursued yet. But among the tasks that need to be done, is an
elaboration of what the control parameters for complex adaptive systems would mean in the context of
innovation systems; i) how to theorise the rate of information flow through innovation systems; ii)
how to theorise the richness of connectivity between actors in the systems; iii) how to theorise the
levels of diversity in and between the schemas of the actors; iv) how to theorise power differentials
between actors; and v) how to theorise levels of anxiety containment within the systems.
Another point of departure could be the Aalborg school’s perspective on the «learning economy»
expressed through the idea of self-organisation (Dalum et al 1992). An important aspect of a learning
economy is that «the organisational modes of firms are increasingly chosen in order to enhance
learning capabilities: networking with other firms, horizontal communication patterns and frequent
movements of people between parts and departments, are becoming more and more important»
(Lundvall & Johnson 1994: 26). This means that «the firms of the learning economy are to a large
15
extent learning organisations» (ibid.) and that the formation of such organisations are important
organisational and institutional innovations (Asheim 1996a). A set of core propositions from this
approach to learning and organisational and institutional innovations, based on the metaphor of selforganisation, is explicated in Dalum et al. (1992);
i) According to modern didactic principles, students learn best when they search for solutions to
problems they regard as important. Thus a wide space for self-organised learning may be
preferred to detailed tutoring.
ii) Public intervention should primary be directed towards shaping the overall structure of
production and the institutional set-up so that this promotes self-organised learning. This reduces
the need for fine-tuning and detailed intervention in the economy.
iii) Economic structures and institutional set-ups can be regarded as systemic wholes. Such
structured wholes have a strong capacity for self-organised learning as long as i) the structure of
production includes development blocks (industrial complexes) with a strong potential; and ii) the
institutional set-up is well adapted to the prevailing technological opportunities.
This work remains to be done and so the story ends temporarily here. But some of this work may also
be based on the ideas expressed in the last section of the paper.
4.3
Illustration of Some Ideas from the Complexity Field
John Foster’s work on the self-organising aspects of economic activity may serve as a relevant
illustration of some of the ideas argued for in the foregoing sections. A main point to note here is the
synthetic character of the argument, in that several theoretical components are integrated. The point of
departure for Foster’s work is the conception that the theorising of historical processes are of interest
only when these processes are evolutionary in nature (Foster 1993a,b, 1994c,d Foster & Wild 1994).
Rather that giving up abstract analysis he argues for taking real, historical time as the starting point for
discovering analytic principles of economic evolution. Foster grounds this kind of theoretical work on
Tony Lawson’s (1989) realistic methodology, which is built on the ideas of Nicholas Kaldor. Both
Foster and Lawson acknowledge that these ideas have their roots in Charles Sanders Peirce’s and
Alfred North Whitehead’s methods and philosophies of science, and that this new version can be seen
as bringing back to life Peirce’s original and interchangeable concepts of abduction and retroduction.
They also acknowledge that this methodology can be related to Alfred Marshall and to a basic tension
inherent in his work. According to Nelson & Winter this tension in Marshall consist of
«having a theory that captured what he saw as the key structural aspects of the economic system
and of economic processes, and having an abstract theory that was analytically tractable and
logically complete. Given the mathematical tools at his disposal, he could not reconcile these
objectives» (Nelson & Winter 1982: 45).
Foster argues that Marshall, more than a hundred years ago, worked on similar ideas, but that he didn’t
come further than to an intuitive conception of the principles of economic self-organisation. The
planned volume 2 of his «Principles of Economics» about evolutionary economic theory was never
written. An important reason for this, according to Foster, was that Marshall was unable to find a
formal representation of an evolutionary process in evolutionary biology that could serve as an
analogy or as a metaphor. The result of this was that the potential for translating his central ideas on
the importance of knowledge and complexity in economic evolution never was to find any formalised
expression (Foster 1994d).
Foster sees self-organisation as an abstraction from a complex, concrete whole that can be used to
understand historical and geographical processes in socio-economic systems (Foster 1991). On the
basis if this abstraction unique aspects of the actual process can be modelled in an abductive process
of theory-forming or interpretative inference. Through abduction, i.e. going from data describing a
complex, concrete whole to a hypothesis that best explains or accounts for the data, self-organisation
can be established as an underlying tendency in concrete, historical and geographical processes. Even
16
if this work must be based on empirical studies this evolutionary process theory can, according to
Foster (1994d), be given a mathematical and formalised representation by using logistic equations or
functions, also known as S-curves, diffusion curves, Marshallian diffusion processes or Lotka-Volterra
equations (Andersen 1994, Witt 1991, Metcalfe 1988). A main point in this perspective is to
acknowledge how creative human knowledge as a general tendency generates socio-economic
structure. This tendency is realised under conditions which the actors don’t choose themselves, but
which they contribute to the reproduction of. This kind of formalising concerns the analysis of
consequences in the environment (i.e. the evolution of structure) of actor’s learning and acquiring of
new knowledge. According to Esben Sloth Andersen,
«this particular type of an S-shaped curve reflects the simplest mathematical model which should
be seen as a starting point for theoretical study as well as for sharper hypotheses about the facts»
(Andersen: 64).
Such an abstraction of self-organising processes represents a general, non-linear mathematical
formulation of processes of structuration. It is descriptive, i.e. based on empirical studies, and not
deductive, i.e. based on mathematical logic (Foster & Wild, in Foster 1994d).
Through the combination of theoretical and empirical work it is possible to develop models of the
concrete institutional factors that somehow influence on and conditions actual processes. The
understanding of the institutional arrangements of economic systems is essential for understanding
how the abstract dynamic principles manifest themselves in concrete situations. One important reason
for this is that the activities that generate techno- and socio-economic structures are parts of a society’s
broader socio-cultural structures. These activities are part of a society’s interpretative practices and of
specific infrastructures that constitute institutional sources of information. Such practice forms and
infrastructures, in which there is a cultural processing and interpretation of economic data, are in their
turn conditioned by concrete legal, institutional, political and social contexts. For this reason there is a
need for empirical studies of qualitative, institutional conditions to discover significant variables in the
processes. Such a combination of cultural hermeneutic and economic evolution constitutes the core of
an institutional perspective in Veblen’s spirit (Jennings & Waller 1994).
The factors that are included in a logistic equation consist of two variables. First, the boundary
conditions or the aspiration level which the structuration process tend towards. Second, the rate of
diffusion or rate of structuration which determines the details of the S-shaped curves. Through these
two, obviously complex, variables it should be possible to introduce and test concrete institutional
factors that can have importance in actual processes. At a given point in time and in a given economic
system the total result of structural processes can, in principle, be identified and measured with a
universal unit of measure called money. According to Foster this makes it possible to operationalise
the approach by using data from time series (Foster 1994b). The quantitative, economic factors can be
expressed in e.g. relative prices, productivity or profit, while the qualitative, non-economic factors are
more difficult to analyse. Firstly, it could be possible to set a monetary value on the concrete economic
path-dependent structures that arise in historical time and place in socio-economic contexts. Secondly,
it could be possible, at least approximately, to give a monetary value to those activities which are the
sources of the structuration processes in historical time and place. Those activities can be related to
those actors that anticipate possibilities for innovation and that learn and develop knowledge through
active and creative contextualisation of information from the environment.
This means that the factors included in a logistic equation are contingent upon concrete valuations,
exchange relations, power relations, contracts, negotiations and interpretations, and that a part of the
equation is influenced by factors that are not economic and therefore not subject to monetary valuing
and measurement. The logistic equation is thus a product of empirical observations and represents an
abstraction of endogenous, historical and geographical processes that in an abductive way can take
into consideration different factors with historical and geographical relevance. But I think it is
17
important in this connection to refer to Kurt Dopfer’s (1991) point that there is a limitation to this
approach. The logistic equation can not in itself explain the sources of economic evolution in human
creativity and innovation. This has to be done on the basis of concrete, qualitative analyses of the
action and learning of actors in self-organising processes. There is a need to connect the microdiversity of individual and collective actors to the macro-order of different kinds of systems. The
logistic equation is one approach to establishing such a connection.
18
5
SUMMING UP
In this paper, the innovation system approach have been introduced, although very briefly. The main
purpose of the paper was rather to identify some theoretical antecedents of innovation systems
thinking in some older and newer institutionalist writings and to introduce complex systems theory as
a possibly fruitful contribution to the further investigation into innovation systems. The discussion has
illustrated that the notion of self-organisation can shed some light on how macro-level structure of
innovation systems can be seen as emerging from the micro-level interaction of actors within the
systems. That the complex systems approach can also provide insight into the sources of economic
evolution in human creativity and innovation at the micro level is discussed in Samuelsen (1994).
19
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