The Competitiveness of Nations in a Global Knowledge-Based Economy
Brian J.
Loasby
The Evolution of Knowledge
DRUID Summer 2001 Conference
12-15 June, Alborg, Denmark
Cointent
Evolution,
rationality and knowledge
The
evolution of ideas and capabilities
Economic development, of which technological change is clearly an
important part, may be very appropriately represented as an evolutionary
process, which requires an approach substantially different from that of
mainstream economics. The growth of
knowledge, including the knowledge embodied in technological innovation, and
biological evolution have in common the generation of variety and selection
from this variety, and the cumulative effect of both processes typically
(though not invariably) is an increasing differentiation of function matched by
a closer integration between functions. However,
there are major differences between the two processes in the ways in which
variety is generated and selected and in the content of differentiation and
integration. In addition, ex-ante
as well as ex-post selection is an essential feature of technological
innovation, and the processes of variety generation and selection, far from
being sharply differentiated, are deeply entwined: the incubation of a new artefact or method of production involves frequent
rejection of candidate variants that leads directly to new design, and users
are shapers as well as selectors. Furthermore,
the effective selection criteria in technological innovation, and in the
knowledge (both theoretical and practical) that supports it, 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. What perhaps most
distinguishes technological from biological evolution is that it rests on the organisation of knowledge, which is itself supported by the
organisation of the process of generating, testing,
and modifying knowledge. Underpinning
all these activities, of course, are the biologically-evolved capabilities and
motivations of human beings, and it seems to me that 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.
Evolution,
rationality and knowledge
Let me begin by specifying what I understand by the term ‘evolution’:
it is a process, or cluster of processes, which combines the generation of
novelty and the selective retention of some of the novelties generated. This basic definition is sufficient to
distinguish evolutionary theories from theories that are clearly not
evolutionary, while allowing us to identify distinctive kinds of evolutionary
theory; in this paper 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.
Neo-Darwinians claim that the only alternative to their explanation of
life-forms is the now-discredited explanation by design. However, explanation by design, in the form of
equilibria of rational choosers, 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, internal as well as external
selection, and tribal behaviour (on the details of
which and its consequences for the content of their subject very few economists
have publicly reflected), has led by incremental adaptation to a style of modelling that relies on what outsiders may consider to be
an extreme - and even irrational - form of rationality: all agents base
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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 they are so successful in designing their plans that no revision is
necessary.
This analytical system assumes that 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. It is true that in both neo-Darwinian theory
and in standard economics selection is determined by consequences; but whereas
in neo-Darwinian theory mutations must be introduced into the environment in
order to discover what their consequences are, in standard economics the
consequences of any contemplated action can be correctly deduced in advance (if
necessary, as a probability distribution). Nothing can happen that was not provided for. 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 will ever induce
agents to envisage new possibilities. Time
is incorporated into models as an additional dimension of goods and information
sets, to the exclusion of any analysis of an economy which develops in time. Thus explanation by design, incorporating
efficient ex-ante selection, is at
the core of economics, which might therefore be considered in direct conflict
with biological principles.
However, matters are not so simple. Some economists have suggested that models of
rational choice equilibria may be regarded simply as
convenient instruents for prediction, and that we may
have considerable confidence in their predictive value because even a complete
absence of rationality, in a market setting, will result in convergence on
outcomes which are similar to those of well-informed optimisation.
(For an extensive
treatment, see Vromen 1995.) Ex-ante
and ex-post selection, it is claimed,
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. Evolutionary forces in economic systems are so
effective that evolutionary arguments are superfluous in economic reasoning,
including the economics of technological innovation, which becomes essentially
a race to attain a technology that is known to exist.
The most thoughtful version of this argument, by Alchian
(1950), made no claims for the optimality of ex-post market selection operating on non-rational behaviour, but simply for an average response in the
appropriate direction to any change in circumstances, which was as much as Alchian thought could reasonably be hoped for. 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. It is noteworthy that some neo-Darwinians are
similarly inclined to consider the outcomes of biological evolution to be structures
and behaviour which are very
close to optimality, and some may even argue that they have better grounds than
economists
for this claim because biological selection has had much
longer to produce such outcomes (Maynard Smith 1996, p. 291).
There is no doubt that these two conceptions, of natural selection from
random mutations, and of optimal choice from known opportunity sets, both
facilitate the 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. Unfortunately, however,
neither 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, as Frank
Knight (1921) pointed out 80 years ago; and the fundamental difficulty about
neo-Darwinian explanations of human activity, as Edith Penrose (1952) insisted,
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. This
has long been recognised. “Purposes mistook, fall’n on the inventors’ heads” is the stuff of tragedy -
and of comedy too; on the other hand many of the desirable features of society,
though the consequences of human action, were not consciously intended by
anyone. 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 selection processes within academic disciplines.
Evolutionary processes in human societies need not, and I suggest
should not, exclude rationality in the broad sense of acting for good reasons. What is essential is uncertainty: the absence
of any procedure for decision-making that is known to be correct (Knight 1921),
which often extends to the absence of any means of ensuring that all
possibilities have been identified (Shackle 1972). As experienced by humans, uncertainty results
from the interaction of two fundamental factors. The factor that is sometimes recognised by economists may be called Herbert Simon’s
problem, though this is better defined as ‘bounded cognition’ than as ‘bounded
rationality’, which unfortunately lends itself to interpretation as maximisation in the presence of information costs, whereas
bounded cognition includes both the limitations of our logical powers and the
need to impose, rather than deduce, simplifications in our representations and
short-cuts in 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.
The interaction of Simon’s and Hume’s problems is most acute in
situations of complexity. The need for
simplification is generally recognised, but what is
not generally recognised, but can be proved by
logical reasoning (Marengo 2000), is that a logically impeccable simplification
must begin with a full representation of the
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complex system - which Simon and Hume have both shown is
unobtainable. (Hayek (1952, p. 185) makes this argument in The
Sensory Order to which we shall refer later). Our only recourse is to start at the other
end, with a simple view, and to add complexity as we are impelled to do so and
to the extent that we are capable of doing so. 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, I suggest, a
fundamental principle of evolutionary economics (see Potts 2000). 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 highly 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 2000, p. 291). Of course, “the 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 there is no scientific proposition
which is in principle beyond challenge, and any investigation achieves
precision at the focus of attention only by “ignoring the lack of definition as
we approach the edges of the image” (Ziman 2000, p.
291).
The epistemology of science is based on uncertainty (Ziman 2000, p. 328), and scientific journals publish many
papers which are later dismissed as embodying false hypotheses or inadequate
tests; yet over time scientific methods 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, based on the Mertonian norms of communalism, universalism, disinterestedness,
originality, and scepticism - which, it should be
noted, 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 2000, pp. 303, 316). These norms are not reducible to any
conventional notion of rationality, though they do embody more than an echo of
Adam Smith’s ([1759] 1776a) Theory of Moral Sentiments. Yet 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 2000, p. 297). This permeable boundary and the continuing
resemblances across it offer some prospect for an investigation by social
scientists of 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. Thus 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 form the premises for decision-making; on
observing the outcomes they may select among them, according
to the theories by which they impute causality and their criteria for what is
desirable (which are not merely the criteria that drive biological processes,
though the relationship between the two kinds of criteria is a legitimate
subject for investigation). These ex-post selections may lead to the
generation of further hypotheses, sometimes (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 seems to be pervasive enough to
justify an evolutionary approach to the growth of academic, technological and
everyday knowledge, but an approach which is significantly different from the
biological model.
Neo-Darwinian evolution requires stability in both the selection environment
and in 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 not only incremental but
extremely slow. This doesn’t look like 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. That the only reliable baseline in general
equilibrium theory is the set of general equilibrium prices is the fundamental
reason why there can be no theory of rational choices in disequilibrium - a
proposition advanced by Richardson (1960) and Leijonhufvud
(1968); that this proposition has not been universally recognised
is indicated by the prospectus, which I recently received, for a three-day programme for ‘finance professionals’ which claims to show
how uncertainty, which is differentiated from risk, can be exploited by
calculating Arrow-Debreu prices. The absence of a reliable baseline also
explains why many game-theoretic models yield multiple solutions. The general principle that I would emphasise is that theories of innovation should explain
what does not change as well as what does, and the
effect of particular combinations of persistence and change on innovative
sequences.
Neo-Darwinians insist that in any evolutionary process there should be
only one unit of selection: genes and ‘memes’ are assigned to distinct
processes. I see no reason to accept
this principle for evolutionary economics: techniques, artefacts
and firms are all relevant, and so too are institutions, organisational
arrangements, and bodies of knowledge, including know-that, know-why, know-how
and know-who. The essential requirement
is to distinguish, at each stage of analysis, between the elements and the
connections that remain stable and the elements and connections 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. Stability in the direction of technological
change is likely to encourage variation within that trajectory and also
variation in the combination of techniques to 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.
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The
evolution of ideas and capabilities
For 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 [1795] 1980). Whether consciously or not, Smith’s theory combines two distinctive
themes from his friend David Hume. The first, which we have already encountered,
is the impossibility of proving empirical truths. The second is the rejection of the supremacy
of reason, which “alone can never produce any action” (Hume 1978, p. 414) 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). Thus rational choice is an inadequate
explanation for behaviour, because neither the
empirical premises nor the objectives of behaviour
can be logically derived. Standard
economics scorns to explain why economic agents should be so keen on maximisation: it is not obvious why people who are equally
content with every point on the highest attainable indifference curve should be
unwilling to tolerate any point which is fractionally below it. Maximisation
therefore fails, both as a method - because the premises of any calculation are
always problematic, and as an objective - because it lacks adequate motivation.
The practical significance of these twin deficiencies are examined
(without reference to Hume) in Chester Barnard’s (1938) lecture on “Mind in
Everyday Affairs”, which ought to be read by every microeconomic theorist and
every policy analyst. Let me just note
Barnard’s (1938, p. 308) observation that the insufficiency of reason when
action is required “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 is influenced by the
particular nature of the connections between them. Now if maximisation
is insufficient to deal with what are represented in economics as allocation
problems, it can hardly serve to explain either the decision to seek new
knowledge or the process of seeking it. Indeed,
the search for novelty presents another challenge to rationality, 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 maximisation: Kirzner’s entrepreneurs are alert, and Schumpeter’s have
powerful non-pecuniary incentives.
Evolutionary economics, being concerned with the development of
knowledge through purposeful behaviour, requires
attention to the motivation for generating new ideas; and this is the first
element that Smith provides. He begins
his exposition by identifying three passions of wide application, 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, before claiming 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 that
economists seek to model, 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: the generation of novelty and the ex-ante selection processes which guide
its adoption or rejection. People try to invent ‘connecting principles’
which will afford a basis for collecting phenomena into categories and link
each category with an explanation which is credible enough to ‘soothe the
imagination’. The ‘equalizing circle’ in
Ptolemnaic geometry and the rule that ‘when one body
revolved round another it described equal areas in equal times’ in Kepler’s system are examples that Smith ([1795] 1980, pp.
61, 90) uses of the resolution of difficulties by appealing to general
principles of motion that appear congenial to prevailing notions of good order;
rational choices based on rational expectations are widely accepted principles
of good explanations of economic phenomena; and we are now identifying the
connecting principles of Smith’s own explanation of the growth of knowledge.
Smith’s third element, which is already implicit in the reference to
notions of good order, is the link between emotion and aesthetics. Smith gives particular attention to the
importance of aesthetic criteria both in guiding conjectures, for example in
the ideas of Copernicus and Kepler, and in
encouraging their acceptance, notably in discussing the rhetorical appeal of
the Newtonian system, 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 causes it to sway
so disconcertingly in use that it has been closed, and for which no simple or
cheap remedy has been identified, is a current illustration 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 the human readiness 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 underpin them, are foundational
principles of Smith’s ([1759] 1976a) Theory of Moral Sentiments, which
is itself an essential element in 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, Smith ([1795] 1980) 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. This disjunction between evidence and
established means of explanation defines a pressing problem of ex-post selection, though it may be the
evidence rather than the explanatory system which is rejected; when the
evidence is recalcitrant, and satisfactory adaptation is despaired of, a new
search for connecting principles begins. This is the fifth element, which renews the
evolutionary sequence.
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The sixth element in Smith’s system is the evolution of the
evolutionary process itself. This basic
human activity generates first an increasingly distinct category of knowledge
which comes to be called ‘scientific’ and subsequently a progressive differentiation
between sciences that we might now label speciation. The consequent differences both of focus and
of criteria for acceptable categories and acceptable explanations generate a
greater variety of more precisely-defined problems and consequently accelerate
the growth of science. This, it is worth
noting, is the context in which the effects of the division of labour first appear in Smith’s ([1795] 1980) surviving work
(though publication did not occur until after his death); in this sequence it
seems almost natural, and therefore a source of pleasure, that the division of labour is invoked in the Wealth of Nations, not as
the best way to make the most of differentiated skills - which was a very old
idea - but as the chief instrument of the growth of productive knowledge (Smith
[1776] 1976b). Since this is easily the
most important idea in economics - the co-ordination problem which normally
receives priority among economists would be trivial without the continuous
generation of new knowledge and new artefacts - it
deserves recognition as a seventh element of Smith’s evolutionary theory.
Smith’s prime ‘connecting principle’ of the division of labour was applied to physiology in 1827 (Milne-Edwards
1827) and this application in turn contributed to Darwin’s vision 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. We may therefore suggest that Smith provides a
better basis for evolutionary economics than biological models; we may also
observe that different analytical systems, focussing
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, and of kinds of knowledge, is a
condition of progress in human society. However
it has its opportunity costs, of which I will mention two. One is that differences in the structure of
understanding, and in the criteria for good theory and good practice, may
create substantial obstacles to the integration of knowledge across disciplines
or between technological fields, as well as obstacles to the integration of
technological and non-technological perceptions of the value of any particular
innovation. A special, but not uncommon,
case of such differences in perceptual structure is that between sensory
perception and scientific categorisation: “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). Theoretical
developments may not map readily onto recursive practice, and know-how may
resist usable codification. The desire
to assuage the discomfort of this apparent contradiction led Hayek to construct
an evolutionary account of the development of The Sensory Order, to
which we shall refer later.
The other 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. 353-4). This is a key
issue in the management of innovation, as in many other fields. Unanticipated technological disasters are
frequently traceable to unjustified assumptions (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.
The double-edged character of uncertainty is the focus of Frank
Knight’s Risk Uncertainty and Profit (1921), and of much of 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 which cannot be
a source of sustainable profit. 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 also creates opportunities
for imagination - as in Smith’s psychological theory: indeed, Knight argues
that it is a necessary condition for entrepreneurship and profit - and also for
the firm, which provides shelter for those who are unwilling to cope with
uncertainty in person and prefer the conditional security offered by
entrepreneurs. The opportunities
perceived by Knight are to be found both within the economic system and in the
corpus of economic theory, where it is appropriate to cite the (very different)
ideas that economic interaction might be formally analysed
as a game between hyper-rational players or that a firm might be conceived, not
as a production function or a nexus of contracts but as a pool of resources, of
uncertain applicability, within an administrative framework (Penrose 1959).
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 choice or
optimal design and random activity, and in doing so warns us (as does Barnard)
not to identify intelligence with logical operations. Niels Bohr’s rebuke
was blunter: “You are not thinking; you are merely being logical” (Frisch 1979,
p. 95). This dissociation of
intelligence from 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”; moreover, individuals differ in
their capacity to change, and learning takes time (Knight 1921, p. 243). Knight is talking about the effect of the
division of labour on the development of
differentiated intelligence, though without reference to Adam Smith.
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”: in other
words, we rely on ‘connecting principles’ of association and causation -
together with “the sufferance of the things that [they have]
10
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 tend
to 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. If we succeed in making new connections which
constitute new knowledge, these connections will 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, indeed, should we assume that, within the
categories that we invent, the similarities dominate the differences, while
between these invented categories the reverse applies? However this ambiguity, though often a problem
and indeed a source of error, occasionally catastrophic, is also an opportunity
for the generation of ideas by making new combinations (a principle enunciated
by both Smith and Schumpeter). The
difficulty of putting boundaries around the capabilities of any individual or organisation, and the consequent ambiguity of their range
of application, is a prominent theme in Nelson and Winter’s (1982) theory, and
underlies Penrose’s (1959) emphasis on the need to perceive, by non-logical
means, how resources may be directed towards productive services. Ambiguities of both capabilities and
‘knowledge that’ also explain why diffusion, typically across different
contexts of similarity, as my former colleague Frank Bradbury frequently
reminded us, is often both unexpectedly difficult and also a major contributor
to the content of innovation. The use of
metaphor, which has played no small role both in technological innovation and
in the attempt to understand it, illustrates the point; abstract thought relies
on language which originated in metaphor - indeed the terms ‘abstract’ and
‘metaphor’ originate in Latin and Greek metaphors. If, in Shackle’s (1979, p. 26) beautiful
phrase, innovation begins with “the imagined, deemed possible”, both what is
imagined and the judgement of its possibility rest on
the exploitation of ambiguity. 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,
guided by the institutions of individual cognition, a research group, or an organisation, lead and direct the innovation process; but
because they are possibilities, not specific predictions, and because the judgements are themselves subject to error they cannot, as
some writers on corporate strategy assert, allow us to deduce a successful
course of action from the specification of a desired final state. Reverse engineering may allow us to
reconstruct the process of manufacturing an existing artefact;
but a successful artefact is a resolved ambiguity (or
cluster of ambiguities), and we have the evidence of its resolution to guide
our reconstruction. It may also be
possible to simulate the path to an achieved scientific discovery, for there is
always retrospectively a pathway to current knowledge; but such success does
not provide a procedure for deducing fresh knowledge, because 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. The development of science may be presented to
students as a logical progression; but the logic is typically available only
in retrospect. There
is no better example of this than the centuries-old search for a proof of
Fermat’s last theorem (Singh 1997).
Nightingale (2000, p. 352) observes that Bradshaw’s paradox, that we “need
to know the biological results before we can decide on the appropriate space to
represent our compounds” (Nightingale 2000, p. 337) applies to the whole
innovation process; indeed the optimal decomposition of any complex problem can
be discovered only by solving the problem. Knight’s principle of supposedly-relevant
similarities, exemplified by scientific and social scientific theories, design
trajectories, recognised good practice, and many
other institutional aids to cognition, 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 in order to achieve successful outcomes.
Thus, as Nightingale (2000, p. 337) points out, the significance of
recent advances in medical knowledge is that they have created new contexts of
similarity or institutional frameworks for innovation, which have enabled pharmaceutical
companies to refine their search for new compounds and to reduce the costs of
search. However they do not allow the
specification of a safe and effective drug to be deduced from a definition of
desired effects, and so reliance on these contexts has not reduced the number
of candidate compounds that it is thought necessary to screen, and “there is
little evidence that this is translating into improved performance”
(Nightingale 2000, p. 351). Moreover, we
should remember Knight’s warning that if there were to be a standard procedure
for attaining optimal outcomes, no-one should expect to make a sustainable
profit from its use. Detailed agreement
on the best way to organise research is more likely
to reduce than enhance the profits of pharmaceutical businesses, 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).
Diversity, especially when based on different ways of connecting
perceptions, phenomena and ideas, entails significant problems of co-ordination
between individuals and between groups whose knowledge is differently ordered. Alfred Marshall (1920, pp. 138-9) 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.” The organisation of
each firm privileges a particular set of connections between people who each
tend to rely on somewhat idiosyncratic contexts of similarity, thus producing,
at individual, sub-unit and firm level, somewhat distinctive responses to
information and generating somewhat different conjectures. (The “administrative
framework” is crucial to Penrose’s (1959) account of a firm’s development.)
Different forms of organisation
are required in order to accommodate diverse combinations of similar and
complementary capabilities (Richardson 1972) while permitting some variety
within each combination as a consequence of differences in temperament,
association and experience which define manageable problems (Marshall 1920, p. 355).
Moreover, the most effective forms, both
of internal structure and external relationships, change overtime, largely as a
consequence of their own effects; this theme was most forcefully expounded by Allyn Young (1928). As
Young maintained, increasing returns are not a property of equilibrium, but of
a process of developing new patterns
12
of connections within an economy; but we may extend Young’s
argument from changing the distribution of activities between firms to the
distribution within each firm and indeed to the changing patterns of knowledge
within each person.
Knowledge itself is organisation, produced by trial and error, and always subject to challenge, including changes in its form and 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. Since technological innovation is an expression of the development of human knowledge, 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 both warns of the likelihood of failure and 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.
Although the absence of human purpose in the variety-generation process
is sufficient reason for rejecting the neo-Darwinian biological model as a basis
for analysing evolutionary processes in social and
economic systems, nevertheless the model may reasonably be applied to the
biological processes which have shaped the human beings whose behaviour we wish to study. Indeed, models of biological evolution 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; it is therefore appropriate to
consider briefly the consistency of the argument that I have presented with
current biological understanding of evolved human capabilities. This consistency has become much clearer with
a 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 which appears entirely compatible 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 this intelligence, as Smith and Knight believed, relies more on
connecting principles than on 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 produced abundant
evidence of very poor performance when the test is presented in the most
abstract form, in which the
underlying logical structure should be most apparent, and
far better performance when the test is presented 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. As previously noted, this facility for
imposing similarities and a readiness to accept similarities which have been
imposed by others, provided that they can be assimilated to some kind of
association which is already familiar, are the psychological characteristics
which underpin Smith’s ([1795] 1980) account of both the creation and the
spread of new knowledge, which includes a recogniition
of the obstacles to that spread among those for whom no such assimilation is
possible - or in other words, who lack the relevant absorptive capacity.
By considering the environment in which the human brain has evolved, it
is possible to trace a plausible biological pathway to a brain with such
characteristics. The evolutionary
success of our predecessor species was promoted by rapid identification of
threats and opportunities, closely linked to effective and specific responses
to each; and identification and response rested on the close co-ordination of
sensory impressions and physical activities. In the early stages of animal evolution,
locally-appropriate networks were genetically programmned,
as is still true of many of the neural networks that regulate human activity;
from this apparently secure basis later mutations produced programmnes
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 generalised
pattern of behaviour - provided, of course, that
there is sufficient motivation to create new patterns when appropriate. The importance of pattern-making activity
suggests an obvious role for aesthetic sensibility as a motivator which has
tended to enhance evolutionary success, and which has 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, an improvement that is 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, and the process of making sense of the world which begins
at birth creates a particular network of connections that imposes order on
events. It is this imposed order, not
the events themselves, that constitutes experience. ”We conceive a person’s processes as operating
through a network of pathways rather than as fluttering about in a vast
emptiness. The network
is flexible and it is frequently modified, but it is structured and it both
facilitates and restricts a person’s range of actions” (Kelly 1963, p. 49).
The development of individual knowledge
and skills is subject to path-dependency: 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 is
necessary for each individual, even when acting in isolation, despite the
obvious dangers; for without firm anchors no
14
intelligent variation is possible. This is another manifestation of the
importance of a persistent baseline; some concept of stability, to which we
might give the label ‘equilibrium’, is necessary to understand change.
Furthermore, the development of new networks embodying new knowledge
typically relies on exaptation - the use, often with
some modification, of existing structures for new purposes. What novelties are possible for any person at
any point of time depends on pre-existing structures and the history of past
adaptations; but these constraints - the evolved institutions of the brain -
are rarely sufficient to be of much help in predicting novelty, except in a
negative sense. Innovation may require
the breaking of some established connections, but the new connections must be
adequate substitutes for the old in forming 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. The emphasis on discontinuity in Kuhn’s (1962,
1970) account of paradigm change and Schumpeter’s (1934) invocation of
entrepreneurial vision is, in my view, more misleading than helpful; on the
contrary “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 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 must always rest
ultimately on tacit knowledge. (That is
not to deny the value of codification.) Hayek’s (1952) account of the formation
of our sensory order, formulated at the outset of his career, 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.
This 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’; this predates his interest in economics but may well have had
some influence on his understanding of economic processes, as argued by Tiziano Raffaelli (2001), who is
primarily responsible for recognising the
significance of this work. 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. Of the early computer manufacturers, IBM alone
created the categories which enabled it to identify a market; its astonishing
record of success subsequently trapped it within this categorisation,
and the policies which followed logically from it, when a very different categorisation became necessary for effective reasoning.
The genetic capability of developing a 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 particular parts of this
potential, 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. It is this characteristic of human cognition
that underlies Smith’s recognition of the crucial importance of the division of
labour as an evolutionary process. 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. These applications may even include
manipulation of the genetic evolution that made it possible, thus reversing the
hierarchy of causation. 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, including the development of knowledge which is
new to that individual, 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 it is important to recognise that their origin,
as a class of phenomena, lies not in the management of interactions but in the
requirements of effective individual cognition. Indeed it is in this fundamental cognitive
requirement that we can discover the possibility, as well as the incentive, for
developing the institutions that guide interactions.
The fundamental principles on which human brains are organised gives some prospect of understanding at least
aspects of other people’s behaviour; and Heiner (1983) suggested that our dependence on rules which
could not be precisely adapted to specific situations was what made possible
the interpretation of other people’s behaviour
without knowledge of these specific situations. Choi (1993) has
argued, drawing on Smith’s Theory of Moral Sentiments, to which we have
already referred, that this possibility of interpretation may allow us to
conduct vicarious experiments by observing others and imitating apparently
successful performance. This would lead
to shared routines and some shared rules for choosing between routines, even
when actions were quite independent. Sometimes
these shared routines and rules would form a natural basis for concerted
action, and when they did not the experience of shared routines might encourage
the notion that co-ordination problems might be resolved by a new form of
sharing: thus exaptation seems to be a promising clue
to the explanation of institutions in the popular sense, and perhaps 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.
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