The Competitiveness of Nations in a Global Knowledge-Based Economy
Brian J.
Loasby
The evolution of
knowledge: beyond the biological model
Research
Policy
Vol. 31, 2002
1227-1239
Content
1.1. Evolution, rationality and knowledge
1.2. The evolution of ideas and capabilities
1.3. Implications of uncertainty
3. Biology, institutions and knowledge
The analysis of the evolution of knowledge is
distinguished from standard economics and neo-Darwinian biology; it combines
purpose with the impossibility of empirical proof. Adam Smith’s psychological theory includes
motivation, aesthetics, invention, diffusion, renewed search, the speciation of
knowledge and the division of labour. Knightian
uncertainty gives rise to both routines and imagination; variation and
selection require a baseline. Organisation and institutions, both of which entail
selective connections, aid knowledge, and knowledge consists of conjectured
connections, open to refutation. Modern
biological ideas about cognition provide an appropriate basis for this
analysis, but do not encompass it.
In this
paper, ‘evolution’ is broadly defined as a process, or cluster of processes,
which combines the generation of novelty and the selective retention of some of
the novelties generated. This definition
is sufficient to distinguish evolutionary theories from theories that are
clearly not evolutionary, while allowing us to identify distinctive kinds of
evolutionary theory; I intend to do both, first differentiating evolutionary
from non-evolutionary economics and then arguing that evolutionary economics
has important differences from the biological model, which may however provide
a useful complement to it.
The principal focus of discussion is technological innovation and the growth of knowledge, both theoretical and practical, which it embodies. This is an evolutionary process, to be sharply distinguished from mainstream economic analysis; but it is not neo-Darwinian. Ex-ante as well as ex-post selection is an essential feature, and the processes of variety-generation and selection, instead of being separated, are often deeply entwined: the incubation of a new artefact or method of production involves frequent rejection of candidate variants, which may lead directly to new design, and users are shapers as well as selectors. The selection criteria correspond to neither biological concepts of ‘fitness’ nor standard economic notions of optimality; they include emotional and aesthetic as well as ‘rational’ elements, and even the rationality is often of a kind that fails to meet the emotional and aesthetic criteria of orthodox modern economists. Technological evolution rests on new ways of organisating knowledge, and is supported by the organisation of the process of generating, testing, and modifying knowledge. Underpinning these activities are the biologically-evolved capabilities and motivations of human beings, and an understanding of these capabilities and motivations, rather than transferable models, is the prime contribution that evolutionary biology can make to the
study of technological innovation. This contribution is summarised
in the final section of the paper.
1.1. Evolution,
rationality and knowledge
Neo-Darwinians claim that the only alternative to their explanation of
life-forms by natural selection from random mutations is the now-discredited
explanation by design. However,
explanation by design, in the form of equilibria
based on universal optimisation, is the foundational
principle of standard economics (though not, as relatively few economists recognise, of Adam Smith’s economics), and it is deemed sufficient
for standard economic analyses of technical change, although these analyses trivialise change. An
evolutionary process within the economics profession, including directed variation
and internal as well as external selection, has led to the institutionalisation
of a style of modelling which relies on what
outsiders may consider to be an extreme - and even irrational - form of
rationality: all agents base their choices on the correct model of the economy,
which includes (usually by implication) the correct model of every other
agent’s behaviour. That is why agents are so successful in
designing their plans that no revision is necessary.
In this analytical system, selection is highly efficient, and takes
place before the event; there is thus no room for any kind of process that
might reasonably be called ‘evolutionary’ - or indeed anything that might be
called a process in the usual sense of the word. In both neo-Darwinian theory and standard
economics selection is determined by consequences; but whereas in neo-Darwinian
theory mutations must be introduced into the environment in order to discover
these consequences, in standard economics the consequences of any contemplated
action are correctly deduced in advance (if necessary, as a probability
distribution). The criteria of rigorous theorising in economics require the set of possible actions
and the set of possible states of the world to be complete, and known to be so;
information is problematic only when access to this information is costly, and
even then it is optimally selected, essentially by choosing the basis and fineness
of its partitioning. Agents can ‘learn’
only in the form of obtaining increasingly accurate estimates of the likelihood
of each possibility by a procedure that is fully specified in advance. Nothing can happen that was not provided for;
and nothing will ever induce agents to envisage new possibilities. Novelty is handled by specifying a new model,
with no account of the transition between models; and the possibility of
novelty is incompatible with optimisation. Time is an additional dimension of goods and
information sets, to the exclusion of any analysis of an economy which develops
in time. This reliance on explanation by
design, incorporating efficient ex-ante
selection, appears to bring economic analysis into direct conflict with
biological principles.
However, matters are not so simple. Some economists have suggested that models of rational
choice equilibria are simply convenient instruments
for prediction, which usually work well because the operation of markets leads
to outcomes that resemble those of well-informed optimisation.
(For an extensive
treatment, see Vromen, 1995). Evolutionary processes in economic
systems are so effective that no study of them is necessary; ex-ante and ex-post selection are close to being observationally equivalent,
and ex-ante selection is easier (and
more elegant - one should not overlook the aesthetics of rationality) to model.
In the most thoughtful version of this argument, Alchian
(1950) simply claimed
that market selection, operating on non-rational behaviour,
produced an average response in the appropriate direction to any change in
circumstances. This, he thought, was as
much as could reasonably be hoped for; the optimality of these outcomes was not
a critical issue. Other economists have
been less cautious in asserting that market selection can duplicate the results
of rationality - in striking parallel to the claims, once widely thought to be
both irrefutable and significant, that central planning and perfect competition
(under appropriate conditions) deliver identical outcomes. Some neo-Darwinians are so impressed by the
effectiveness of natural selection (and implicitly by the ability of mutation
eventually to generate the necessary near-ideal variants) that they are
inclined to consider the structures and behaviour of
biological life-forms to be very close to optimality, and may believe (with
some plausibility) that they have better grounds than economists for this claim
because biological evolution works on a much longer time-scale (Smith, 1996, p.
291).
These opposite conceptions, of highly efficient, if very slow, natural selection from random mutations, and optimal choice, which is virtually instantaneous, from known opportunity sets, both facilitate the
1228
construction of closed and (apparently) completely specified
models which meet fashionable criteria of ‘rigour’;
their popularity may therefore be explained by a combination of ex-ante and ex-post selection by and of practitioners. Neither, however, is a good match to the
problems of human activity, of which technological innovation is a prominent
example. The fundamental difficulty with
rational choice theory is its untenable assumption about human knowledge: when
making important decisions, people rarely know either what options are
available or their possible consequences (Knight, 1921); and the fundamental
difficulty with neo-Darwinian explanations of human activity, as Penrose (1952)
insisted in response to Alchian’s claim, is that it
ignores human purpose. Human action is
often the result of human design; but human design is inherently fallible,
however secure its logic, since it is based on knowledge that is usually
incomplete or erroneous, and so things rarely turn out
precisely as intended: outcomes may be better, or worse, or just different. Technical change, like most human activities,
lies in the interval between optimal choice and chance variation, and by opting
for either, or both, of these models (which we might think of as corner
solutions in the space of theoretical principles), we exclude at the outset the
possibility of understanding what is happening, and not least - though this
topic will not be directly addressed in this paper - of understanding the
development of academic disciplines.
The analysis of evolutionary processes in human societies should not
exclude rationality in the broad sense of acting for good reasons; but neither
should it exclude the essential incompleteness of knowledge. As experienced by humans, this incompleteness
results from the interaction of two fundamental factors. Economists sometimes recognise
the first of these as Herbert Simon’s problem of ‘bounded rationality’;
however, this term lends itself to misrepresentation as maximisation
in the presence of information costs, and ‘bounded cognition’ signifies more
clearly the limitations of our logical powers and the need to impose, rather than
deduce, simplifications in our representations and short-cuts in our
decision-making. ‘Making full use of all
relevant information’, far from being a definition of rational behaviour, is rarely an option. What is even more fundamental, but totally ignored
in both general equilibrium and game-theoretic versions of rational choice
theory, is David Hume’s problem: there is no way of demonstrating the truth of
any general empirical proposition, either by deduction, for there is no way of
ensuring the truth of the premises, or by induction, for there is no way of
proving that instances not observed would correspond to those that have been
observed. Reason is not enough.
The interaction of Simon’s and Hume’s problems is most acute in
situations of complexity. The need for
simplification is generally recognised, but a logically
impeccable simplification must begin with a full representation of the complex
system - which Simon and Hume have both shown is unobtainable. Our only recourse is to impose a simple view,
and to add complexity to our representation as we are impelled to do so and to
the extent that we are capable of doing so, trying to remember all the time
that what we have is indeed only a representation, which may be deficient in
some crucial respect - as the course of technological innovation has often
revealed. That our systems of thought
rely on connections that we have invented, or adapted from their inventors, and
not those which have been optimally chosen from a complete set, is a fundamental
principle of evolutionary economics (see Potts, 2000; Loasby,
2001). Human knowledge is a self-organising system.
Since ‘information’, far from being self-contained truth, acquires meaning
from its context, the implications of complexity are pervasive. Recourse to ‘science’ does not resolve these
difficulties, for science also depends on “the linkage of known empirical
phenomena into a wider network of accepted - or at least potentially acceptable
- ‘facts’ and concepts” (Ziman, 2000a, p. 291). “[T]he production of such linkages is the main
business of research”, and scientists have developed elaborate procedures for
testing them, using logical reasoning to identify testable implications of
candidate hypotheses; but no scientific proposition is in principle beyond
challenge, and precision at the focus of attention is achieved only by “ignoring
the lack of definition as we approach the edges of the image” (Ziman, 2000a, p. 291).
The epistemology of science is based on uncertainty (Ziman, 2000a, p. 328), and many scientific papers are later dismissed as embodying false hypotheses or inadequate tests; yet the evolutionary processes of science have produced a remarkable growth of knowledge which may reasonably be treated as reliable (Ziman,
1978). Reliability is a
consequence of appropriate and distinctive operating practices for generating
and testing ideas, based on the Mertonian norms of communalisin, universalism, disinteredness,
originality, and scepticism, which are responses, not
always explicit, to the impossibility of final validation, and to the corresponding
dependence on intersubjective appraisals to produce
structures of knowledge that are cognitively objective (Ziman,
2000a, pp. 303, 316). These norms are
not reducible to any conventional notion of rationality, though they embody
more than an echo of Adam Smith’s (1976a [1759]) Theory of Moral Sentiments,
and indications that adherence to them is less secure than in the past is a
potential threat to scientific credibility. Although it is possible to make meaningful
distinctions between science and what Ziman calls
‘life-world knowledge’, science has evolved from earlier attempts to make sense
of the human situation, and continues to rely on some knowledge which has not
been formally brought within the corpus of scientific analysis (Ziman, 2000a, p. 297). This permeable boundary and the continuing
resemblances across it offer some prospect of investigating the behaviour of people who are not scientists, but who are not
rational maximisers either.
Responses to uncertainty include various kinds of coping strategies,
including strict observance of routines, contingent application of rules (or of
Simon’s rather wider category of ‘decision premises’), and taking precautions
such as the building of reserves, including reserves of goodwill from earthly
or heavenly powers. But they also
include responses of a very different kind; for the obverse of uncompletable knowledge is the scope for imagination. Uncertainty can be exploited as well as
endured. This has been the most
distinctive as well as the most persistent theme in George Shackle’s writing
(see especially Shackle, 1972, 1979). People
may generate novel options and imagine new contingencies, make novel selections
among alternative hypotheses and embody some of them in artefacts
and many in institutions which guide action; on observing the outcomes they may
select among them, according to the theories by which they impute causality and
their criteria for the acceptability of explanations (Loasby,
1995). Selection may lead to the generation of
further hypotheses, sometimes (and especially in technological innovation) in a
closely-coupled way. Such evolutionary
processes are likely to be effective means of progress, though not always of
improvement in terms of human welfare. Uncertainty thus justifies an evolutionary
approach to the growth of academic, technological and everyday knowledge, but
an approach which goes beyond the biological model.
Neo-Darwinian evolution requires stability in both the selection
environment and the genotypes which are subject to selection; it also requires
genetic mutation to provide new variants from which to select. This dual genetic requirement can be satisfied
only if the chances of a defective copy are extremely small but not zero, and
that in turn requires neo-Darwinian evolution to be both incremental and
extremely slow. This is not a good model
for technological change or the development of human knowledge, though it does
encourage us to postulate stable genetic characteristics in the human
population over periods which are by comparison extremely brief. What it does have in common with technological
innovation is the importance of a reliable baseline; without this neither ex-ante nor ex-post selection can be significant. The coping strategies just mentioned are often
very important in providing such a baseline.
Neo-Darwinians insist that in any evolutionary process, there should be only one unit of selection; but techniques, artefacts and firms evolve interactively, as do institutions, organisational arrangements, and bodies of knowledge, including know-that, know-why, know-how and know-who. This interdependence of both variety-generation and selection at various levels is an important feature of human history, and presents obvious analytical difficulties. The essential requirement is to distinguish, at each stage of analysis, between the elements and the connections that remain stable and those that change. This combination varies according to time and circumstance; and there is no simple hierarchy. Sometimes established elements are assembled into a novel architecture; sometimes a modular architecture facilitates quasi-independent developments; in both cases, the innovation incorporates a great deal that is familiar, as dramatically illustrated in Constant’s analysis of the Lockheed ‘Starfighter’, which also illustrates the interaction between technological and organisational evolution. Stability in the direction of technological change is likely to encourage variation within that trajectory and also variation in the combination of techniques to
1230
produce artefacts. Decomposition and recombination are important
principles both in technological innovation and in the study of technological
innovation - as in other kinds of knowledge.
1.2. The evolution of ideas and capabilities
As our foundation model of the growth of knowledge
as an evolutionary process, we cannot do better than Adam Smith’s psychological
theory of the emergence and development of science, which he illustrated by the
history of astronomy (Smith, 1980 [1795]). Smith’s theory combines two distinctive themes
from his friend David Hume: the impossibility of proving empirical truths and
the rejection of the supremacy of reason, which “alone can never produce any
action” (Hume, 1978, p. 41 4) and therefore “is, and ought only to be, the
slave of the passions, and can never pretend to any other office than to serve
and obey them” (Hume, 1978, p. 415). Rational choice is an inadequate
explanation for behaviour, because neither the
empirical premises nor the objectives of behaviour
can be logically derived.
The practical significance of these twin deficiencies is examined
(without reference to Hume) in Chester Barnard’s (1938) lecture on ‘Mind in
Everyday Affairs’. Barnard (1938, p.
308) suggests that the insufficiency of reason to determine action “is probably
why it is difficult to make correct decisions without responsibility”. In this, as in many other ways, organisation changes behaviour -which
is the underlying theme of Barnard’s book; more generally, as Potts (2000)
argues, the performance of any system cannot be reduced to the performance of
its elements but depends on the particular nature of the connections between
them. As we shall see, this principle is
Adam Smith’s key to the growth of knowledge. The search for novelty cannot be ‘rational’,
for “no kind of reasoning can give rise to a new idea” (Hume, 1978, p. 164). It is therefore no accident that explanations
of entrepreneurship go beyond optimisation: Kirzner ‘s
(1973) entrepreneurs are alert, and Schumpeter’s (1934) have powerful
non-pecuniary incentives.
The motivation for generating new ideas is the first element in
Smith’s theory. He draws attention to
three general human passions, arguing that people are disturbed by the unexpected,
dismayed by the inexplicable, and delighted by schemes of thought that resolve the
inexplicable into plausible generalisations, and
claims that, in the absence of any assured procedure for attaining correct
knowledge, these are the motives which “lead and direct philosophical
enquiries”. They are a long way from the
incentives in economists’ models, but perhaps not so far from some of the incentives
that shape the behaviour of technologists, and of
economists also.
The second element in Smith’s theory is the sequence that is inspired
by this complex motivation: a combination of imagination and ex-ante selection guides the invention
of ‘connecting principles’ which sort phenomena into categories and link these
categories by an explanation which is sufficient to “soothe the imagination”. Smith (1980 [1795], pp. 61, 90) shows how the ‘equalising
circle’ in Ptolemaic geometry and Kepler’s rule that “when
one body revolved round another, it described equal areas in equal times”
appealed to principles of motion that conformed to prevailing conceptions of
good order; most economists accept the notion of ‘rational expectations’
because it fits their idea of a good theory; and both technology and business strategy
are shaped by what people feel comfortable with. Ideas must satisfy the selection criteria of
the imagination.
Smith’s third element, implicit in the reference to notions of good order, is the link between emotion and aesthetics. He explains the importance of aesthetic criteria both in guiding conjectures, for example those of Copernicus and Kepler, and in encouraging their acceptance, notably in discussing the rhetorical appeal of Newton’s theory, which in his Lectures on Rhetoric exemplifies Smith’s ideal method of “giving an account of some system” (Smith, 1983, p. 146). Aesthetic influences in the natural sciences and in economics (signalled earlier by the reference to the elegance of rational choice equilibria) are occasionally recognised but rarely explored (see Schlicht, 2000); aesthetic influences on the design of artefacts are often of major significance. Sometimes aesthetic appeal is a major objective; but of particular interest in an exploration of evolutionary processes is the extent to which aesthetic criteria are also surrogates for effective performance; bridges and aircraft are obvious examples, and the flawed design of the Millennium footbridge in London, which caused it to sway so disconcertingly in use that it had to be closed, is a recent reminder that surrogacy should not be assumed.
The fourth element in Smith’s proto-evolutionary theory is his
proposition that connecting principles which seem to work well are widely
diffused because of our readiness, when in any difficulty or discomfort, to
look for guidance from others who seem to know better, and because of our
desire to act, and indeed think, in ways that merit the approval of others. These powerful motivations, together with the
underlying similarity in human mental, emotional and aesthetic processes which
underpins them, are foundational principles of Smith’s (1 976a [1759]) Theory of Moral Sentiments, which
is itself an essential component of Smith’s complex account of social organisation, and applicable both to technological
evolution and any adequate understanding of organisational
behaviour.
However, because by Hume’s argument invented principles, however
widely accepted, are not proven truth - even, as Smith (1980 [1795], p. 105)
explicitly notes, when these principles have been invented by Newton - they are
liable eventually to be confronted with unexpected phenomena which they cannot
be adapted to explain. At this point,
the product of human imagination and design is rejected, and a new search for
connecting principles begins. This is
the fifth element, which renews the evolutionary sequence.
The sixth element in Smith’s system is the evolution of the
evolutionary process itself. The basic
human activity of seeking psychological comfort by inventing and imposing
connecting principles generates an increasingly distinct category of knowledge
which comes to be called ‘scientific’, with its own group of practitioners; and
as this category expands, we begin to observe a progressive differentiation between
sciences that we might now label speciation. The consequent differences of focus and of
criteria for acceptable categories and explanations lead to an increasing
variety of problems that are more precisely defined, accelerating the growth of
science.
It is in this scientific context that the effects of the division of labour first appear in Smith’s (1980 [1795]) surviving work; it
therefore seems natural that in the Wealth of Nations he invokes the
division of labour, not as the best way to exploit
differentiated skills - which was a very old idea - but as the chief instrument
for improving productive knowledge (Smith, I 976b [1776]). This is the seventh element of Smith’s evolutionary
theory; and it is easily the most important idea in economics, since the
co-ordination problem which normally receives priority among economists would
be trivial without the continuous generation of new knowledge and new artefacts.
Smith’s prime ‘connecting principle’ of the division of labour was applied to physiological diversity in 1827 (Milne-Edwards,
1827) and this application in turn contributed to Darwin’s vision - a novel
connection - that a Malthusian struggle to survive would result in the
differentiation of species (Raffaelli, 2001). The other basic elements in Smith’s account of
the development of knowledge by motivated trial, error, amendment and diffusion
understandably did not, for they go beyond biology. We may therefore suggest that Smith provides a
better basis for evolutionary economics than biological models; we may also
observe that different analytical systems, focusing on different patterns of
connections, may be most effective in developing different kinds of knowledge,
thus explaining the value of speciation among academic disciplines.
The differentiation of knowledge is a condition of progress in human society. However, it has its opportunity costs, of which I will mention two. Differences in the structure of understanding, and in the criteria for good theory and good practice, though providing necessary frameworks for the construction of knowledge of distinct kinds, may create substantial obstacles to the integration of these distinct kinds of knowledge and impede the combination of technological and non-technological perceptions of any particular innovation - an issue of particular relevance to public debate on research policy, and the theme of a particularly instructive study (Pool, 1997). A particular case of such differences in perceptual structure was identified by Hayek very early in his career: “events which to our senses may appear to be of the same kind may have to be treated as different in the physical order, while events which physically may be of the same or at least of a similar kind may appear as altogether different to our senses” (Hayek, 1952, p. 4). The desire to assuage the discomfort of this apparent contradiction led Hayek to construct an evolutionary account of the development of The Sensory Order, which preceded the emergence of scientific explanation. Ryle (1949), who is cited by Hayek, similarly pointed out that the knowledge required for effective performance is of a different kind from knowledge of facts and theories; theoretical developments may
1232
therefore not map readily onto recursive practice, and
know-how may resist usable codification.
A second opportunity cost of the differentiation of knowledge is the neglect of potentially crucial interdependencies. “When the compass of potential knowledge as a whole has been split up into superficially convenient sectors, there is no knowing whether each sector has a natural self-sufficiency... Whatever theory is then devised will exist by sufferance of the things that it has excluded” (Shackle, 1972, pp. 353-354). This is a key issue in the management of innovation, as in many other fields. Unanticipated technological disasters are frequently traceable to unustified assumptions (which are usually unconscious, but not always so) about the sufferance of something excluded from the processes of design, testing, or operator training. The Millennium footbridge already mentioned is an exemplary demonstration.
1.3.
Implications of uncertainty
The double-edged character of uncertainty is the focus of Frank
Knight’s classic Risk. Uncertainty and Profit (1 921), and pervades
Shackle’s work. Knight restricted the
concept of risk to situations in which both the set of possibilities and the
probability distribution over this set are known, either by argument a priori, as in calculating the expected
results of throwing dice, or by statistical analysis of appropriate evidence. Choices under risk may therefore be made by a
standard procedure which can be demonstrated to be optimal, but such
demonstrably optimal procedures cannot be a source of sustainable profit for
any firm or individual. For conditions
of uncertainty, however, no demonstrably optimal procedure can be devised; we
must act in the space between optimality and randomness.
But if uncertainty creates difficulties, it is also a necessary
condition for imagination, as in Smith’s psychological theory: indeed, Knight
argues that this must be the basis of any economic explanation of
entrepreneurship and profit - and also the firm, which provides shelter for
those who are unwilling to cope with uncertainty in person and prefer the
conditional security offered by entrepreneurs. In the absence of uncertainty, all economic
activity can be arranged by contracts between people who can use standard
procedures to agree the terms of these contracts; this is still the basis of
most economic theory, in which uncertainty is assimilated to risk - even though
the emergence of new ideas within economics depends on uncertainty about the
adequacy or applicability of existing theory.
Knight observes that in a world without uncertainty “it is doubtful
whether intelligence itself would exist” (Knight, 1921, p. 268): this locates
the role of intelligence squarely in the space between optimal design and
random activity, and warns us not to identify intelligence with formal logic. This message is repeated in Barnard’s analysis
of the problems of running a business, and in Niels
Bohr’s warning: “You are not thinking; you are merely being logical” (Frisch,
1979, p. 95). The insufficiency of logic
underlies Knight’s (p. 241) observation that “[m]en differ
in their capacity by perception and inference to form correct judgements as to the future course of events in the
environment. This capacity, moreover, is
far from homogenous”; individuals also differ in their capacity to change, and
learning takes time (Knight, 1921, p. 243). Though he makes no reference to
Adam Smith, Knight is here observing the effect of the division of labour on the development of differentiated intelligence.
Knight (1921, p. 206) is also unconsciously close to Smith in arguing
that “in order to live intelligently in our world... we must use the principle
that things similar in some respects will behave similarly in certain other
respects even when they are very different in still other respects”: we rely on
‘connecting principles’ of association and causation - together with “the
sufferance of the things that [they have] excluded” - in developing our own
ideas and in adapting other people’s. (The
biological basis for this reliance is explained in the final section of this paper.)
For differently-formulated problems, we
rely on different contexts of similarity, and for new problems we experiment
with new connections that define new contexts: that is how the division of labour leads to differentiated knowledge. New connections provide us with new rules and
routines, releasing cognitive capacity for new applications, as in Penrose’s (1959) conception and use of “the
receding managerial limit”.
By insisting on contexts of incomplete similarity, Knight preserves uncertainty at the core of his analysis. Why should we assume that, within the categories that we invent, the similarities dominate the differences, while between these invented categories
the reverse applies? As
Popper (1963, p. 44) pointed out, any such judgement
reflects a particular point of view, and Smith showed how points of view may
change, leading to a rearrangement of categories. This inherent ambiguity of interpretation is
both a source of error, occasionally catastrophic, and an opportunity for
imagination. The difficulty of
delimiting the capabilities of any individual or organisation
is a prominent theme in Nelson and Winter’s (1982) theory, and underlies
Penrose’s (1959) emphasis
on the need to connect resources that have been developed in the course of business
to new productive opportunities. Ambiguities
also explain why diffusion, which typically encounters different contexts of
similarity (as my former colleague Frank Bradbury frequently reminded us), may
be unexpectedly difficult and also a major contributor to the content of
innovation. What is deemed possible, and
- even more - what is deemed capable of being made possible, depends upon
perceptions of the applicability of both theoretical and practical knowledge to
novel contexts.
Category-based judgements of possibility,
which often differ between individuals in a firm and firms in an industry, lead
and direct the innovation process; but because they are fallible conjectures
they cannot allow us to deduce a successful course of action from the
specification of a desired final state. Reverse engineering may reconstruct the
process of manufacturing an existing artefact; but a successful
artefact is a corroborated conjecture among many
conjectures that have failed, and its success does not provide a guaranteed
procedure for developing a new artefact. The development of science may also be
reconstructed as a logical progression; but the logic is only retrospective. There are many divergent pathways from
established ideas and many ways of linking ideas, and which path seems worth
following depends on conjectured contexts of similarity. Connections have to be invented. There is no better example of this than the
centuries-old search for a proof of Fermat’s last theorem (Singh, 1997).
Knight’s principle of supposedly-relevant similarities is exemplified
by scientific and social scientific theories, design trajectories, recognised good practice, and many other institutional aids
to cognition, which enable us to do far better (most of the time) than random
speculation; but all these forms of ex-ante
selection are themselves conjectures which need to be reinforced, modified, and
sometimes superseded by ex-post
selection. Recent advances in medical
knowledge have created new contexts of similarity, or institutional frameworks
for innovation, which have enabled pharmaceutical companies to refine their
search for new compounds; but they do not permit the specification of a safe
and effective drug to be deduced from the desired effects (Nightingale, 2000,
p. 337). They have not therefore reduced
the number of candidate compounds that are screened, and “there is little
evidence that this is translating into improved performance” (Nightingale,
2000, p. 351). Moreover, we should remember
Knight’s warning that a standard procedure for attaining optimal outcomes
cannot be a source of distinctive advantage; agreement between pharmaceutical
businesses on the best way to organise research is
more likely to reduce than enhance their profits, unless they can also erect
barriers against rivals. Diversity
remains a general condition both for profit and the growth of knowledge; and
the effect of system diversity on development is a basic evolutionary theme (Pavitt, 1998, p. 439).
We noted earlier that a major opportunity cost of diverse ways of
connecting perceptions, phenomena and ideas, is the problem of co-ordination
between individuals and between groups whose knowledge is differently ordered. Standard economic analyses of co-ordination
focus on differences in objectives and access to information, but ignore
differences in ways of constructing knowledge. However, an earlier economist who recognised the value of these differences, Alfred Marshall,
offers some helpful advice. “Organisation aids
knowledge; it has many forms, e.g. that of a single business, that of various
businesses in the same trade, that of various trades relatively to each other,
and that of the State providing security for all and help for many’ (Marshall,
1920, pp. 138-139).
The organisation of each firm privileges a particular network of connections between groups who rely on distinctive contexts of similarity, thus producing, at individual, sub-unit and firm level, differentiated responses to information and somewhat different conjectures. (The influence of its ‘administrative framework’ is crucial to Penrose’s (1959) account of a firm’s development). The knowledge structures in these networks vary between industries, and within the value chain of each industry, in order to facilitate appropriate combinations of similar and complementary capabilities (Richardson, 1972); and within each
1234
structure differences in temperament, associations and
experience lead to the variation in detail that generates progress through
trial and error (Marshall, 1920, p. 355).
These organisational
forms are the equivalent of differentiated species and variety within species,
but (in contrast to the biological model) they embody purpose and imagination, internalise and accelerate a considerable part of the
selection process and use the outcomes of selection as a guide to further
experimentation.
This evolutionary process extends to the organisations
themselves; the most effective forms of internal structure and external
relationships change over time, largely as a consequence of their own effects. This theme was most forcefully expounded by
Young (1928), who insisted that the increasing returns which have been
fundamental to economic progress are not an equilibrium phenomenon, but define
a process of developing new patterns of connections within an economy. We may extend Young’s argument from changes in
the distribution of activities between firms to similar changes within each
firm and indeed to the changing patterns of knowledge within each person, and
their connections to other knowledge. All this may be derived from Adam Smith’s
original account of the growth of knowledge; that the derivation has been far
from obvious is a particularly striking example of the difficulties of
improving our own knowledge.
Knowledge itself is organisation, produced
by trial and error, and always subject to challenge, including changes in its
form and its relationships to other bodies of knowledge; it is a product as
well as a precondition of decisions. Knowledge
lies in the particular connections between elements, rather than the elements
themselves; this is a concept foreign to microeconomics, in which connections
are assumed to be complete except when the absence of a particular connection
is identified as a source of market or organisational
failure. It is a unifying principle of
evolutionary economics that the performance of any system depends not only on
the elements of which it is composed but on the particular pattern of
connections, both direct and indirect, between them (Potts, 2000; Loasby, 2001).
Since technological innovation is an expression of the development of
human knowledge, and especially of knowledge how, an understanding of human
knowledge provides a basis for understanding technological innovation - not
least because the power and fallibility of human imagination and human
calculation seem to correspond to the remarkable successes and myriad failures
of technology. It is the combination of
uncertainty - the unlistability of possibilities and
the absence of any procedure, known to be correct, for assessing and evaluating
those possibilities which are listed - and the evolved characteristics of human
cognition that warns of the likelihood of failure but also creates the alluring
prospect of extraordinary success, as well as explaining our reliance on
institutions. Progress in both knowledge
and technology depends on the diversity of individual initiative, but also on
the relationships, formal and informal, between individuals; for every one of
us, as well as for the communities to which we belong, knowledge depends on the
organisation of categories and the relationships
between them; and the organisation of people into
categories and relationships, if appropriately managed, aids the development
and use of knowledge in society.
As Adam Smith knew, the appeal of connecting principles is enhanced by
their scope; so the attraction of reducing evolutionary processes to neo-Darwinism
is easy to understand. However, as Ziman (2000a, pp. 324-326) explains, each level of complexity
must be studied on its own terms, and evolutionary processes in social and
economic systems, as we have seen, have distinctive features. Nevertheless compatability
between levels is a useful criterion, and biological models may be very helpful
in understanding the cognitive characteristics of biological creatures who are capable of producing true novelty, and yet are so
dependent on rules and routines. The
recent shift of attention from the simple artificial intelligence model of the
human brain as a serial logical processor in favour
of a conception of multiple neural networks has produced an understanding of
evolved human capabilities which is entirely consistent with the Smith/Knight
theory that the growth of intelligence is driven by the imperative of coping
with situations that are not amenable to logical solutions.
That human intelligence relies on connecting principles rather than formal logic is suggested by the results of a wide range of experimentation by psychologists with versions of the Wason test, in which
subjects are asked to identify evidence which is relevant to
the refutation of a simple proposition. These
experiments have repeatedly demonstrated very poor performance when the test is
presented in an abstract form which most clearly displays the underlying logical
structure, and far better performance in contexts which are more complex but
with which the subjects are familiar. The human brain appears to recognise
similarities much more readily than logical implications. This propensity to impose similarities and to
accept similarities which can be assimilated to familiar categories is the
psychological foundation for Smith’s (1980 [1795]) account of the creation and absorption of new
knowledge, including his recognition of the obstacles to absorption among those
for whom no such assimilation is possible - or in other words, who lack the
relevant absorptive capacity.
A plausible biological pathway to a human brain with such characteristics
may be traced by considering the environment in which it has evolved. The evolutionary success of our predecessor
species was promoted by rapid identification of threats and opportunities and
effective and specific responses to each; and both identification and response
required the close co-ordination of sensory impressions and physical
activities. In the early stages of
animal evolution, locally-appropriate networks were genetically programmed, as
is still true of many of the neural networks that regulate human activity; and
later mutations produced programmes for the
development of new networks in response to new threats and opportunities.
A generalised capacity for making patterns
provides the potential for much more flexibility than a general capacity for
logical reasoning. There must, however,
be sufficient motivation to create new patterns when appropriate; and the importance
of pattern-making activity suggests an obvious role for aesthetic sensibility
as a motivator which has enhanced evolutionary success and both stimulated and
shaped scientific as well as other forms of knowledge. Smith’s discussion of motivation is thus not
only an essential feature of his theory, which cannot be adequately represented
by the standard conception of incentives in modern economics, but an essential
feature of satisfactory models of biological evolution. The differentiation of function between
networks in a single brain is a straightforward application, recognised by Milne-Edwards (1827), of Adam Smith’s
principle of improved performance as a consequence of the division of labour, more easily achieved by this means than by
incremental adaptations towards a general-purpose logical processor.
The neural structures of each person’s brain are the product of two
selection processes: genetic selection provides the basic architecture and some
of the connections, but much of the brain’s development occurs after birth in
the process of making sense of the world by imposing order on events. It is this imposed order, not the events themselves,
that constitutes experience (Kelly, 1963, p. 73); though genetically enabled,
it goes beyond genetics. The development
of individual knowledge and skills is path-dependent, but not path-determined:
the network supports routines of behaviour and rules
for conceptualising and resolving problems, and it
strives to preserve itself, sometimes by denying the validity of information. This inertia is necessary despite the obvious
dangers; for without firm anchors no intelligent variation is possible. A stable baseline is a condition of change.
Furthermore, change embodies stability; the development of new
networks embodying new knowledge typically relies on exaptation
- the use, often with some modification, of existing structures for new
purposes. As Potts (2000) explains, change
is predominantly to adjacent states; the novelties attainable by any person at
any point of time are conditioned by pre-existing structures and the history of
past adaptations - the evolved institutions of the brain. Innovation may require the breaking of some
established connections, but they must be replaced by new connections which
form complementary relationships with some preserved patterns. The new ideas and the old may be
incommensurable in the straightforward sense of not being partitions of a single
structure of knowledge, but there is no absolute break. This is a notable feature of Smith’s account
of scientific progress; the contrary emphasis on discontinuity in Kuhn’s (1970,
1962) account of paradigm change and Schumpeter’s (1934) invocation of
entrepreneurial vision is misleading. “Good
continuity” (Schlicht, 2000) is important, and Schlicht has drawn attention to the importance of aesthetic
factors in determining what is good continuity - which over a long period may
resemble Wittgenstein’s rope.
The development of an architecture of the brain which facilitated the creation of neural networks
1236
necessarily preceded the emergence of conscious thought, which did not displace these networks of unarticulated ‘knowledge how’. It is therefore necessarily true that we know more than we can tell, and that codification rests ultimately on tacit knowledge, which is coded in our neural networks. Hayek’s (1952) account of the formation of our sensory order, noted earlier, is a remarkable anticipation of this model of evolutionary psychology. By the normal rules of biological evolution by exaptation, conscious thought was similarly built upon connections rather than logical processes; Hayek explains why the connections of science may not correspond to the evidence of our senses. An evolutionary sequence from connections between impressions and actions to connections between ideas of impressions and actions, including the imagination of possible connections, was conjectured in Alfred Marshall’s (1994) early paper ‘Ye machine’, which predates his interest in economics but may have had a substantial influence on his understanding of economic processes (Raffaelli, 2001).
The ability to construct logical sequences is a relatively
recent and relatively weak development, almost an ‘artificial’ form of intelligence,
and its effectiveness depends on the prior creation of appropriate categories,
as has been repeatedly - and sometimes spectacularly - demonstrated.
3. Biology,
institutions and knowledge
The genetic capability of developing a particular set of behaviours, out of a very large potential, by selecting
connections in response to perceptions of phenomena, together with the
emotional impulse to develop such connections, is the biological precondition
of modern economic systems; for if differences of interest and situation lead
members of a population to develop different parts of this potential, then the
capabilities of that population may far exceed what even the most gifted
individual can attain. The fostering of
differences in interest and situation by both formal and informal organisation encourages and shapes the development of these
capabilities. The division of labour is an evolutionary process; and it is Smith’s
conception of the character of humnan cognition that
led to his recognition of its importance. It has its own pathology, not least in
technological innovation; yet this combination of capabilities and motivation
has made possible a non-biological evolutionary process that has operated much
faster and encompassed unprecedented categories of applications. One may claim, with Cosmides
and Tooby (1994), that the mental capabilities that
have resulted from our biological evolution are “better than rational” for
coping with the range of problems that lie between randomness and the economists’
concept of rationality. These certainly
include the problems of uncertainty and imagination; indeed we may agree with Rizzello (1999, p. xv) that “[t]he economics of the mind is
the economics of creativity, uncertainty and complexity”.
Individual cognition is governed, though not determined, by a dense network of rules and familiar relationships, many of them partly or wholly tacit. When these rules and relationships are shared within a community, we call them ‘institutions’, and many of the rules and relationships on which each of us relies are indeed institutions in this sense; but their origin as a class of phenomena lies not in the management of interactions but in the requirements of effective individual cognition. Indeed this fundamental cognitive requirement produces the possibility, as well as the incentive, for developing the institutions that guide interactions. The principles on which human brains are organised gives some prospect of understanding at least aspects of other people’s behaviour; and Heiner (1983) has suggested that it was our dependence on rules which could not be precisely adapted to specific situations that made possible the interpretation of other people’s behaviour without detailed knowledge of their circumstances. Choi (1993) subsequently drew on Smith’s Theory of Moral Sentiments in arguing that this possibility of interpretation allows us to conduct vicarious experiments by observing others and imitating apparently successful performance. This behaviour leads to shared routines and shared rules, even when actions are quite independent. These shared routines and rules may provide a natural basis for concerted action, and encourage the development of new routines to resolve co-ordination problems: thus exaptation seems to be a promising clue to the explanation of institutions in the popular sense, and in particular to the general acceptance, usually tacit, of the idea that organisations are incubators of institutions. Among the significant
organisations which depend on both
individual and shared routines and rules are research communities, firms and
markets. Without such institutions, economic
evolution as we have experienced it would not be possible.
This paper has been developed from my response to a group study of
technological innovation (Ziman, 2000b) which
endorsed both the value and the variety of evolutionary reasoning, and provided
ample evidence for both endorsements. I
strongly recommend it.
Alchian, A,A., 1950. Uncertainty, evolution and economic theory. Journal of Political Economy 58, 211-221.
Barnard, CA., 1938. The Functions of the
Executive. Harvard University Pres; Cambridge, MA.
Choi, YB., 1993. Paradigms and, Conventions: Uncertainty,
Decision-making and Entrepreneurship.
University of
Michigan Pres Ann Arbor, MI.
Cosmnide; L., Tooby, .J., 1994. Better than rational: evolutionary psychology and
the invisible hand, American Economic
Review 84, 327-332.
Frisch, O., 1979. What
Little I Remember. Cambridge University Press Cambridge.
Hayek, F.A., 1952. The Sensory Order. University of
Chicago Presss Chicago.
Heiner, F.A., 1983. The origin of
predictable behaviour. American Economic Review 73, 560-595.
Hume, D., 1978. In: Selby-Bigge,
L.A. (Ed.), A Treatise on Human Nature, 2nd Edition.
Clarendon Pres Oxford (revised by Nidditch, P.H.).
Kelly, GA., 1963. A
Theory of Personality: The Psychology of Personal Constructs. W.W. Norton, New York.
Kirzner, I.M., 1973. Competition and Entrepreneurship. University of Chicago Press
Chicago.
Knight, F.H., 1921. Risk,
Uncertainty and Profit. Houghton Mifflin, Boston.
Kuhn, T.S., 1962, 1970. The Structure of Scientific Revolution.
1st and 2nd Editions. University of Chicago Press,
Chicago.
Loasby, B.J., 1995. Acceptable
explanations. In: Dow, S.C., Eillard, J. (Eds). Keynes, Knowledge and
Uncertainty. Edward Elgar, Aldershot, pp. 6-24.
Loasby, B.J., 2001. Time, knowledge and evolutionary
dynamics: why connections matter. Journal
of Evolutionary Economics 11, 393-412.
Marshall, A., 1920. Principles of Economics, 8th Edition.
Macmillan London.
Marshall, A.,
1994. Ye machine. In: Research in the
History of Economic Thought and Methodology, Archival Supplement 4. JAI
Pres Greenwich, CT, pp. 116-132.
Milne-Edward. H., 1827. Nerf In: Bory de Saint-Vincent,
M. (Ed.), Dictionnaire Classique de l’Histoire Naturelle. Rey et Gravier,
Paris.
Nelson, R.R., Winter, S.G., 1982. An Evolutionary Theory of Economic Change. Belknap Press, Cambridge, MA.
Nightingale,
P., 2000. Economies of scale in experimentation: knowledge and technology in
pharmaceutical R&D. Industrial and
Corporate Change 9, 315-359.
Pavitt, K., 1998. Technology;
products and organization in the innovating firm: what Adam Smith tells us and
Joseph Schumpeter doesn’t. Industrial and
Corporate Change 7, 433-452.
Penrose, E.T.,
1952. Biological analogies in the theory of the firm. American Economic Review 42, 804-819.
Penrose, E.T.,
1959. The Theory of the
Growth of the Firm, 3rd Edition.
Oxford University
Press Oxford, 1995.
Pool, R., 1997. Beyond Engineering: How Society Shapes
Technology. Oxford University Press; New York.
Popper, K.R.,
1963. Conjectures and
Refutations. Routledge and Kegan Paul, London.
Potts, J.,
2000. The New Evolutionary Microeconomics:
Complexity, Competence and Adaptive Behavior.
Edward Elgar, Cheltenham and Northampton, MA.
Raffaelli, T., 2001.
Marshall on mind and society: neurophysiological models
applied to industrial and business organization. Journal of the History of Economic Thought 8, 208-229.
Richardson,
G.B., 1972. The organisation of
industry. Economic Journal 82,
883-896.
Rizzello, S., 1999.
The Economics of the
Mind. Edward Elgar,
Cheltenham and Northampton, MA.
Ryle, G., 1949. The Concept of Mind.
Hutchinson, London.
Schlicht, E., 2000. Aestheticism in the theory of custom. Journal des Economistes et
des Etudes Humalnes 10, 33-51.
Schumpeter,
J.A., 1934. The Theory
of Economic Development. Harvard University Pres;
Cambridge, MA.
Shackle, G.L.S., 1972. Epistemics and Economics.
Cambridge University Pres Cambridge.
Shackle, G.L.S., 1979. Imagination and the Nature of
Choice. Edinburgh University Press, Edinburgh.
Singh, S., 1997. Fermat’s Last
Theorem. Fourth Estate, London.
Smith, A., [1759] l976a The Theory of Moral Sentiments. In: Raphael, D.D., Macfie,
AL. (Eds.). Oxford University Pres Oxford.
Smith, A., [1776] 1976b. An Inquiry into the Nature
and Causes of the Wealth of Nations. In: Campbell, R.H., Skinner, A.S.,
Todd, W.B. (Eds.). Oxford University Press Oxford.
Smith, A., [1795] 1980. The
principles which lead and direct philosophical enquiries: illustrated by the
history of astronomy. In: Wightman,
WP.D. (Ed.), Essays on Philosophical
Subjects. Oxford University Pres Oxford, pp. 33-109.
Smith, A.,
1983. Lectures on Rhetoric
and Belles Lettres. In: Bryce. J.C. (Ed.).
Oxford University Pres Oxford.
1238
Smith, J.M., 1996.
Conclusion. In: Runcin1an, W.G., Smith, J.M., Dunbar,
R.I.M. (Eds.), The Evolution of Social Behaviour
Patterns in Primates and Man. Oxford University Press~ Oxford, pp. 201-207.
Vromen, J.J., 1995. Economic Evolution.
Routledge, London.
Young, A.A.,
1928. Increasing returns and economic progress. Economic Journal 38, 527-542.
Ziman, J.M., 1978. Reliable Knowledge. Cambridge University Press, Cambridge.
Ziman, J.M., 2000a. Real Science. Cambridge University Press, Cambridge.
Ziman, J.M. (Ed.) 2000b. Technological Innovation
as an Evolutionary Process. Cambridge University Press, Cambridge.
1239