The Competitiveness of Nations in a Global Knowledge-Based Economy
Brian J. Loasby
Time,
knowledge and evolutionary dynamics: why connections matter *
Journal of Evolutionary Economics
Vol. 11, 2001, 393-412
Content
Time
matters because knowledge changes.
Knightian uncertainty excludes correct
procedures and proven knowledge, but makes room for imagination and creativity,
which drive an evolutionary process. Human
cognition relies less on logic than on pattern-making; we impose connecting
principles to create patterns and causal linkages between them as
representations of phenomena, which are imperfect and often subject to multiple
interpretations. Stable patterns provide
the necessary baseline for selection. Our personal patterns are supplemented by
institutional regularities, and organisations of
various kinds help to shape the development of knowledge, which grows by making
connections at the various margins of existing knowledge.
Economic theory has concerned itself with the sources
and consequences of conduct, and has sought in this field what can be conceived
as rational, what can be expressed as proportion, what is publicly and
unanimously agreed, and what belongs within bounds defined by the notion of
exchange in an inclusive sense.
The attractions of such a programme
are evident and compelling. The cost
resides in what, by its nature, it is obliged to neglect or even implicitly [to declare unimportant... the most serious of
those exclusions… is the brushing aside of the question, a unity though
requiring three tells to express it, of time, knowledge, and novelty… theory
has chosen rationality, whole and unimpaired. Arid thus it has cut itself off from the most
ascendant arid superb of human faculties. Imagination, the source of novelty, the basis
of men’s claim, if they have one, to be makers and not mere executants of
history, is exempted by its nature from the governance of given and delimited
premises. Shackle 1972, pp. 443-444]
HHC: [bracketed]
displayed on p.394 of original.
* This is a substantially
amended version of a paper first presented at a meeting of the Network of
Industrial Economists at the University of Reading on 19 December 2000 and then
in a modified form at the DRUID Winter Conference at Klarskovgaard,
Korsør, 18-20 January 2001. I am grateful to John Cantwell for providing
the initial stimulus, to John Sutton for encouragement, and to participants at
both meetings for their comments; I have also benefitted
from interchanges with Uwe Cantner
and Jason Potts. All share the credit
for those parts of this paper that they agree with.
393
In Value
and Capital, Hicks (1948, p. 115) defined “[e]conomic
dynamics [as] those parts [of theory] where every quantity must be dated”. Subsequent theoretical development has shown
that this is not a sufficient criterion. In the Arrow-Debreu
system not only must every quantity be dated, it must also be indexed by
location and state of the world; yet in a model that conforms to these
specifications there is no room for dynamics, but a single equilibrium which
extends over all dates, locations and contingencies. There is no arrow of time: later dates
influence earlier allocations in precisely the same way as earlier dates
influence later allocations; there is no sense in which one thing can lead into
another. In terms of Hicks’ ([1976] 1982b)
distinction, time is incorporated into the model as an additional dimension,
but the model is not ‘in time’, which would imply the need for a sequence of
decisions by economic agents. Indeed,
within such a model there is no scope for even a single set of decisions: an
equilibrium allocation is deduced directly from the basic data, which includes
a complete set of preferences but requires no algorithms of choice. The individual is of measure zero (Hahn,
[1973] 1984, p. 64), not only because a single person’s preferences and endowments
have no perceptible effect but because no individual is allowed to take any
initiative. Everything that could possibly
happen must be incorporated in the specification of one or more states of the
world for each date and location; the occurrence of any novelty, either
endogenous or exogenous, violates this requirement and demonstrates that the
apparent equilibrium had been derived from false premises.
It is as well
that all differences between dates are incorporated in a single equilibrium,
because no resources are available to cope with change, having already been
optimally allocated within that equilibrium. There are not even any resources available to
cope with equilibration; for although the model purports to include all
resources it ignores the mental resources required for human action. Its internal consistency therefore requires
the exclusion of human agency, and so the process of achieving the declared equilibriumn cannot occur within a functioning Arrow-Debreu economy. As
good general equilibrium theorists do not hesitate to point out, the markets -
strictly a single market - in which this equilibrium allocation is represented
by a complete set of contracts open once only, and close
before
the economy starts operating. It is not
surprising that this looks less like a model of a market system than a model of
a command economy.
The
requirement that all transactions are arranged outside the economy in order to
exclude transaction costs from the model has significant implications. As Coase (1988, p.
15) pointed out, if there are no transaction costs there can be no problems
with externalities; impacts on third parties, whether beneficial or harmful,
should simply be added to the list of goods and to the preference systems of
those affected by these impacts. Thus
there can be no unexploited gains from trade - indeed there are no third
parties - and therefore no scope for beneficial policy interventions to remedy
system failures. If it is nevertheless
thought appropriate for particular theoretical purposes to incorporate costs of
transacting, then it is illegitimate to compute equilibria
separately from the analysis of the transacting process. Moreover, these costs depend on the ways in
which transactions are organised and the sequence in
which agents search for the best attainable set.
It is also
likely that some transactions, especially for dates at which there are many
possible states of the world, will be postponed, and it may seem attractive to
commit some resources to the development of systems for making and implementing
later decisions. This, of course, is the
basis of Coase’s explanation for the firm; it is also
the basis for an explanation of markets as institutional arrangements for
facilitating a series of transactions (Ménard, 1995,
p. 170), and for the creation of various kinds of reserves, as Menger ([1871] 1976) observed, in the form both of goods
and of capabilities, direct arid indirect. However, none of these phenomena can be
accommodated within the Arrow-Debreu system, for this
system cannot accommodate the concept of change within (relatively) stable structures
which make change possible - because of the particular conception of a system
that underlies it, as we shall see.
If we wish to incorporate the process and costs of
change within our models we need to modify our use of dates: they are now
required not simply to ensure that the set of variables is complete but in
order to identify the sequence in which things happen and in which knowledge
and possibilities become available. This
was precisely what Hicks intended to signal by his definition; in fact, as Leijonhufvd (2000, p. 111, n. 22) suggests, what matters
for dynamic analysis is that decisions must be dated - because knowledge must
be dated. But, as has been demonstrated
in many multi-stage models, this may not make much difference to the analytical
strategy, as long as it is assumed that agents correctly, if incompletely, anticipate
future knowledge and future possibilities and make correct deductions from
their anticipations. Hicks refused to
make this assumption: the dynamics of Value and Capital are
provided by a sequence of temporary equilibria, set
within an unanalysed intertemporal
disequilibrium - which incidentally provided Hicks with the basis for a sympathetic
interpretation of Keynes’s General Theory. It is an economy in which time matters,
because time changes the knowledge that is available to agents, and not only
through Bayesian updating, even though time matters less in Value and
Capital than in Hicks’s later writings (see
especially Hicks, [1976] 1982). Like the
discoverers of intertemporal equi-
395
librium,
Lindahl arid Hayek, Hicks rejected the assumption of
perfect foresight, even when extended to probability distributions, as an
obstacle to modelling the working of an actual
economy (Zappia, 2001), and Myrdal,
Hayek, Keynes and Schumpeter all based their theories of the business cycle and
unemployment on various kinds of imperfect knowledge (Loasby,
1998).
It was Frank
Knight (1921) who first emphasised the crucial
distinction between risky situations, in which there is an agreed procedure,
logical or empirical, for distributing probabilities over a closed set of
outcomes, and situations of uncertainty, in which there is no such procedure -
and often, as Shackle was to insist, no way to ensure that all possible
outcomes have been recognised. Knight pointed out that risk, as he defined
it, was a calculable cost and that, since the correct method of calculating
each risk was public knowledge, risk-bearing was a productive service not
sharply distinguishable from any other, and therefore not a distinctive source
of income. ‘Profit’ is then a misleading
name for a not-very-particular kind of wage, and risk-bearing belongs in the
production function, and therefore poses no threat to the homogeneity that is
required for perfect competition. But
since uncertainty, as defined by Knight, excludes the possibility of any method
which can be shown to be correct, uncertainty-bearing cannot be treated as an
input in a production function and the homogeneity of perfect competition
cannot be preserved. Profit is the
reward, not for risk-bearing, but for successful entrepreneurship which has
coped with uncertainty by idiosyncratic means: entrepreneurs might calculate,
but the correct basis for their calculations could not be deduced from the
basic data - and many of them fail.
However,
although the result of any attempt to cope with uncertainty must itself be
uncertain, Knight did not believe that success was purely a matter of chance;
nor was it simply the consequence of alertness to opportunities which, once
perceived, are clearly genuine - which is the basic case of Kirzner’s
(1973) theory of entrepreneurship. It
was a reflection of human capabilities, and a distinctive and valuable
resource. In his first published
article, George Richardson (1953) drew on Knight’s analysis of the significance
for economic efficiency of the deployment of distinctive entrepreneurial capabilities
to argue for the importance of selection among agents and, as can now be seen,
was only a thought away from the argument of his famous article of 1972.
But
uncertainty prevents the closure which is essential to achieve proofs of
equilibrium, and so in equilibrium theory it must be reduced to risk. Nor can modern expedients for deriving equilibria when agents have imperfect or asymmetric
information claim validity unless it can be assumed that every agent draws not
only from the same information set but also, though this is scarcely ever recognised, from a single system for interpreting this
information.
Knight’s
distinction is fundamental. It is a
distinction between conceptions of knowledge, which (as so often) was best
expressed by George Shackle (1972, Preface). “The economic analyst has opted for reason. He assumes that men pursue their interest by
applying reason to their circumstances. And
he does not ask how they know what these circumstances are”. In
particular, rational choice relies on a comparison of the consequences of all
available alternatives, without
deigning
to explain how these consequences can be known, or even how all the
alternatives can be known to be available. If we wish to do economics in the spirit of
Knight and Shackle, we must do it in another way: we must switch our emphasis
from closed to open systems, and from proofs to
process. However, if economists in
really substantial numbers are to be persuaded to change, it is important to
demonstrate both that this other way is accessible, and also that it is clearly
more appropriate for dealing with some issues that are widely recognised by economists to be important, such as economic
growth, technological change, the scope and role of the firm, the generation of
novelty, and even the co-ordination of economic activities.
Knowledge,
Institutions and Evolution in Economics (Loasby,
1999) was intended to show that such a way exists, that it is already applied
in other fields, that it has distinctive and significant applications within
economics, and, not least, that it is already a substantial part of our
heritage as economists. Economic
dynamics is founded on uncertainty, because it is economics in time: this Hicks (1982a, p. 34) had begun to recognise
by 1933, and this recognition grew in strength throughout his life. But though uncertainty gives rise to serious
problems, not only for economic theorists but also in the conduct of economic
activities at all levels from individual decision-making to the co-ordination
of economic systems, it also provides abundant opportunities. For as Shackle above all continually reminded
us, uncertainty is the precondition of imagination and creativity: it makes
space for the growth of both theoretical and practical knowledge.
However, it
also ensures that this growth must be evolutionary, because it is the result of
trial and error, and the rate and directions of growth are influenced by how
these processes of trial and error are organised. As Marshall (1920, p. 138) told us (but as
most economists have forgotten), organisation aids
knowledge, and it has many forms, each with its own particular virtues and
limitations. The past cannot be changed,
but it can, in part, be known; the future cannot be known, but it can be
imagined, and by acting on that imagination it can, in part, be changed. Imagination is shaped - though not determined -
by the interpretation of environment and experience. However, most of what is imagined turns out to
be impossible; and so progress depends on both the variety of imagination and
some process for selection among this variety - the essentials of evolution. It also depends on stability as a background
to change.
The method of
my book is to make connections, and its epistemic basis is the conception of human
cognition as a connective process, in which the connections forged by logical
argument are important but not primary, either in evolutionary terms or when ‘acting
for good reasons’ - for neither decision premises nor the framing of problems
result from logical processes. In
relatively modern times this limitation of logic was established by David Hume ([1739-40]
1978): “no kind of reasoning can give rise to a new idea” (p. 164), and “reason
alone can never produce any action” (p. 414). Hume also demonstrated that no amount of
evidence could provide definitive proof of any general empirical proposition - a
demonstration that inspired Popper’s evolutionary conception of science as a never-ending
sequence of conjectures which are exposed to possible falsification.
397
Hume’s friendship with Adam
Smith provides a foundational connection which is not explored in the book (but
see Loasby, 2002): however prominence is given to the
expositions by Smith, Marshall and Hayek of the theme that knowledge grows
through a fallible process of making connections. The starting point is not a conception of
perfect knowledge, from which one moves to risk and then to ‘uncertainty’ which
is regularly assimilated to risk, but the need to construct knowledge by
creating categories and imagining links between them. These two aspects of knowledge construction
are precisely identified by Adam Smith ([1795] 1980) as the ‘connecting
principles’ which constitute scientific explanation.
An unintended
but very welcome consequence of this method and content is that they provided
me with the specific absorptive capacity to appreciate both the force and the
value of Jason Potts’s (2000) book: we rely on similar ‘connecting principles’
in constructing our arguments. I believe
that our books are closely complementary; reading either is a good preparation
for reading the other. Which is the
better sequence for a particular reader depends on the intellectual position
and problem orientation from which that reader starts: this is in fact a
proposition derivable from either book.
In this
section I will attempt to exploit this close complementarity
by adopting (and adapting) Potts’s meta-theoretical perspective on the
relationship between equilibrium theorising, as
generally practised, and dynamic analysis. The Arrow-Debreu
system is very carefully located in integral space, where every element is
directly connected to every other element, just as the Newtonian model of the
solar system exists in a unified gravitational field. In the Newtonian system larger masses have
bigger effects, and in the Arrow-Debreu system stronger
preferences have bigger effects, but whatever the magnitude of the effect in
either system it impacts directly on every other element in that system. There are no gravitational shields or specialised intermediaries to constrain interactions; ‘markets’,
which provide connections that are indirect, have - and can have - no existence
except as a metaphor for direct and costless transfers of property rights, and
all ‘choices’ are transparent. There is
no structure. Menger
([1871] 1976), by contrast, set out to explain the structure of prices, and
Hicks ([1976] 1982b, p. 287) suggests that his condemnation of Böhmn-Bawerk’s theory as “one of the greatest errors ever
committed” was a response to the debasement of time in that theory from a
context for structure, as Menger had used it, to a
measure of capital intensity. Menger’s is a self-organising
system, which relies on the development of specific connections; it is
conceptually distinct from general equilibrium.
Although the
Arrow-Debreu model is no longer generally regarded as
the central model of economics, nevertheless the widely-adopted principle that
outcomes may be directly deduced from the data relies on the same integral
conception. George Richardson (1959, p.
24) long ago pointed out that in the world of practice there is no direct link
between data and outcomes, but only an indirect
link by way of beliefs and
intentions; whether consciously or not, economists have recognised
Richardson’s observation as a threat to the concept of a fully-connected
economic system and the theoretical technique that relies on it, and have
either ignored the issue or produced some notably unrigorous
stories to support their practice. The
rhetorical purpose of invoking rational expectations is to justify this
procedure by rendering illegitimate any inquiry into the ways in which data are
interpreted - thereby impoverishing business cycle theory, as well as the theory
of economic growth.
Since a
fully-connected equilibrium is a completed project, it is closed to further
enquiry (though one may compare the equilibria that
correspond to different data). However,
by assuming that ‘in the beginning there was a fully-connected system’ it is
possible to generate apparently well-defined analytical problems by postulating
that some carefully-chosen connection is missing from a set that is otherwise
complete; it is then possible to derive a local equilibrium incorporating
agents’ reactions to this solitary deficiency, relying on the results of the fully-connected
model to absorb that local equilibrium, and ignoring or finessing the
once-powerful argument that only a complete general equilibrium analysis
ensures validity. This reliance is often
implicit, and sometimes appears to be unconscious.
The identification
of a strictly-limited deficiency in an otherwise fully-connected system is the
standard method of generating soluble problems in economic theory.
There are two variants of this method:
in what might loosely be called the ‘Harvard’ version the deficiency results in
some welfare-reducing failure which creates a space for government intervention,
while the ‘Chicago’ version demonstrates that the result is a new equilibrium
in which economic agents reduce the damage to negligible proportions, whereas
governments, as ‘Chicago’ economists know, can be relied on to make things much
worse. ‘Harvard-style’ analysis is
illustrated by ‘New Keynesians’ who produce a caricature of Keynes’s results
which accepts the internal validity of new classical reasoning but makes a case
for government action. An appropriate
example of ‘Chicago-style’ reasoning in the context of this paper is Oliver
Hart’s (1996) explanation of the firm as an optimal allocation of property
rights, which may be briefly examined.
Here the
problem-generating deficiency is a narrowly-specified constraint on the
feasible contracting space, which is sufficient to frustrate the contractual
alignment of incentives but has no other implications. The consequences, and the narrow scope, of
this deficiency are so clearly defined that farsighted contracting is possible
about the right to make decisions, which Hart identifies with ownership; thus
the missing connection can be restored by an appropriate allocation of property
rights. Since, by virtue of the
background general equilibrium model, such an allocation is Pareto superior -
the reduction of variety in decision-making is clearly beneficial when the
correct decision is readily defined - there must be a set of contracts which
make it universally acceptable; there are, of course, no obstacles to efficient
contracts for property rights. The
result is an analysis that combines theoretical novelty with the apparent
validation of a general equilibrium theory in which
allocations are derived directly from the data,
399
without
postulating any interaction between agents. By virtue of its construction, there are no
dynamics in this model; the appropriate allocation restores the connection
between data and outcomes, allowing all dates and contingencies to be provided
for. The standard analysis of production
remains untouched. Thus the ‘firm’ which
the model purports to explain is just an extension of the ‘market’; neither has
any organisational or institutional existence as a
particular set of connections.
Oliver
Williamson’s approach looks more promising. Although he differs from Coase
in insisting that the fear of opportunistic behaviour
is a necessary condition for the existence of a firm, he follows Coase, and differs from Hart, in modelling
the firm as a system of resource allocation by direction. This immediately suggests the possibility of a
theory of organisational development, which may lead
to changes over time both in the way that the firm is organised
and managed and in the scope of its activities. Unlike Hart’s model, the Coase-Williamson
conception implicitly defines the firm as a network of privileged connections,
leading naturally to Herbert Simon’s vision of an economy in which firms, not
markets, are the primary forces. Williamson,
however, appears never to have appreciated the fundamental significance of this
conception, for he has denied the validity of Simon’s vision (Williamson, 1996,
p. 145) and has never shown much interest in what firms actually do - which is
to develop and use connections, of many kinds as we shall see. In Williamson’s explanation of the firm, no
less than in Hart’s, structure matters only as a means of validating the
underlying theory in which structure has no role. It is therefore no accident that he has never analysed the development of firms overtime, despite Nooteboom’s (1992, p. 285) observation that his theory
seems to demand a time-dimension. In terms
of Hicks’ ([1976] 1982b) distinction, time is incorporated into the model in
order to differentiate the choice of governance structures from the choices
that are made within the chosen structure, but the model is not itself in time:
that is precisely why there are no “surprise[s], victims and the like”
(Williamson, 1996, p. 46).
We have seen
that the basic model of standard economics ignores connections because they do
not affect outcomes, but allows for a variety of special models to explore the
implications of particular deficiencies; the term ‘market failure’ is a clear,
if rarely recognised, indication that this is what is
going on. The idea that connections are
problematic in general, and should be treated as problematic, is not seriously
entertained. But that is precisely what
is required in evolutionary economics and in industrial dynamics - and in other
less orthodox branches of economics, as Potts explains.
The obvious
objection to treating connections as problematic is that whereas there is
necessarily only one way in which a system can be fully connected, there are
very many ways in which it may be partly connected; how then are we to know
what connections to include in our model? Before we can attempt to answer that question,
we should explore the fact that underlies it: the recognition that the system
and our model are necessarily different. If all theoretical discussion is in terms of
fully connected systems, or integral space, then it is rather easy to assume
isomorphism
between the system and the theoretical model; but when theories are recognised to be simplifications then they must embody
partial connections - even when the model is of a general equilibrium. Our theories, our classifications, and our
ideas are not simply derivatives of reality; they exist in the space of
representations, and (if we conceive them to have physical form) in the neural
networks of our brain. We cannot start
with a complex reality, and choose how to simplify it by removing some
connections: that is a cognitive impossibility. Instead, knowledge has to be constructed by
building up connections. Knightian uncertainty, though habitually treated as a rare
and unimportant phenomenon, is actually the base case - and it is the basis of
human cognition and human society, not only as problem but also as opportunity.
Evolutionary
biology and psychology explain why human cognition should have developed, not
as a general-purpose instrument for solving problems by identifying their
logical structure, but as a loosely-connected cluster of context-limited
categories and linkages - Smith’s connecting principles. However, the use of
these cognitive skills, which are the basis of imagination, take us beyond the
random mutations arid natural selection of the biological model of evolution to
purposeful (but fallible) behaviour (Penrose, 1952). Wherever we start there are, in principle,
very many directions in which we may look for connections (Simon, 1992, p. 21),
and each move opens up a new set of possibilities; but each individual is likely
to notice only a few. It therefore seems
reasonable to suggest that the best way to improve knowledge is to encourage
many people to imagine connections, and to try to arrange that different people
will imagine different connections. The
latter, of course, is the function of the division of labour,
and it was Adam Smith who realised its fundamental
importance - and incidentally generated the co-ordination problem which has now
supposedly been solved by denying its origins. It is also necessary to have some means of
deciding which products of the imagination should be preserved and developed and
which discarded or amended. This is a
major role of markets, but it is not the only role of markets, as I have argued
previously, nor is it only markets that can do the job.
All our
theoretical systems are constructed in the space of representations; but many
of them are presumed to have real-world relevance. But this relevance is not ensured by applying
criteria deemed appropriate to the space of representations, although this is
often done - and not only in economics. There
is necessarily a gap between representation and reality; and preoccupation with
criteria which are internal to the representation may widen that gap. For example, refining the internal coherence
of the concept of rational choice has driven choice theory away from the
practice of decision making (Loasby, 1976); and the
isomorphism between planning and perfect competition as represented in models
of general equilibrium was a sign that these models were inappropriate for
understanding either planned or market economies, because the performance of
either rests on their specific structures, or patterns of connections
(Richardson, 1960). Charles Suckling
conceived the task of managing innovation as a careful exploration of the gap
between the initial representation (typically an
interesting
401
effect
in the laboratory but sometimes a theoretical result) and the much more complex
environment in which commercial success would be determined. (This is a representative example of the
reasons why I dedicated my book to him.)
The problem
of representation arises in many forms. How are we to know what connections to emphasise in the design of any particular organisation - for every organisation
chart defines a partly connected system? How are we to know what
connections should be made with other organisations?
What connections are most effective in
gaining customers? Who are the potential
competitors? What are the factors we
should take into account when designing a regulatory system for a privatised industry - and is the answer different for each
industry? As economists, in what aspects
of what other disciplines should we take an interest? What are the significant connections that are
missing from our models of purportedly fully-connected systems? We may also ask when connections are best
avoided, for example by assigning activities to distinct organisations
or problems to distinct theoretical systems.
A non-economist would assume, reasonably but wrongly, that the
principles governing the separation of activities would be a central theme in
explaining the organisation of industry; and the
desirability of differentiating theoretical systems is a key question for the analysis
of change, as is illustrated by the insistence of both Schumpeter (1934) and
Penrose (1959) on disconnecting the theory of growth from allocative
theory.
Fundamentally,
every organisation, every theory, every set of
expectations, every plan, and every policy privileges a very small subset of
possible relationships; its applicability is therefore always problematic, and
can be established only over time - and never for all time. Like the economic agents who are our nominal
subjects of study, we have to work in time; why not therefore try to develop
theories which are embedded in time - and therefore in uncertainty - and take
seriously the selective development of connections own time as a result of
fallible human action? In the remainder
of this paper I will discuss three themes which seem to me central to such
dynamic analysis: knowledge, institutions, and organisation.
I shall argue that these themes are
closely related.
Economists
nowadays quite often write about information; it is a convenient way of
implementing the strategy already discussed of removing a particular connection
from the basic fully-connected model and thus generating a
potentially-publishable paper. Information
may be coarsely rather than finely partitioned, so that agents are unable to
discriminate between states in which different actions are optimal; particular
items of information may be missing – not only information about future actions
by others; or information may be unevenly distributed. But the content of information is not itself
treated as problematic; often indeed it is explicitly information about the
probabilities of a closed set of possible states of the world. Underlying knowledge is complete, even if
information is not. Thus even when
information is dispersed and incomplete, the information sets of all
agents
within a model are drawn from a single and complete set. This avoidance of Knightian
uncertainty is crucial for the analytical strategies that are used, as has
previously been observed.
The denial of
Knightian uncertainty motivates the standard
treatment of complexity. The assumption
of an underlying single and complete information set ensures that all
simplifications are derived from a single correct source, which provides a
common basis for the analysis of transactions between agents. It is then natural to misinterpret Simon by
treating bounded rationality as equivalent to a cost of information and satisficing as an optimal response, and to avoid asking how
boundedly rational agents can know enough about the
correct model to be certain that their simplifications, though not the whole
truth, are nothing but the truth. The
answer to that unasked question may be found in what I propose to call Hayek’s
Impossibility Theorem: “any apparatus of classification must possess a structure
of a higher degree of complexity than is possessed by the objects that it
classifies; and.. therefore, the capacity of any explaining agent must be
limited to objects with a structure possessing a degree of complexity lower
than its own” (Hayek, 1952, p. 185).
The question
may also be applied to those who analyse complexity
in this way: how do they know that their models of complex systems are adequate
representations of the systems to which they are applied? To this question also, Hayek’s Impossibility
Theorem supplies the answer: they cannot know. Just as our analysis of systems should not take
as its reference point a fully-connected system, which directs us to questions
about specific failures arid their remedies, but start from the problem of
creating and maintaining connections that are appropriate for particular
purposes, so the problem of complexity is not one of simplifying a supposedly
complete model - which is a fantasy - but of constructing some representation
by selecting and linking elements. Both
are exercises in Knightian uncertainty, for which
there are no correct procedures, but the possibility of rewards for skill. Information needs to be interpreted, and the
interpretation depends upon the classification systems and the connections
between categories by which people attempt to make sense - for sense has to be
made - of phenomena.
This is how
we develop knowledge, by varying our construction systems as we “construe the
replication of events” Kelly, 1963, p. 72). Knowledge is structure, in the form of
categories into which phenomena or concepts may be grouped, or in the form of
relationships between such categories; and structure implies a non-integral
space. It is an imperfectly connected
system of imperfect connections, and any of these connections may change over
time, as Paul Nightingale (2000) shows in a recent analysis of pharmaceutical
research strategies. The world system of
knowledge is far from complete, and the knowledge possessed by - or even
accessible to - any individual is a very small proportion of that world system.
Nobody knows how a Boeing 737 works; and
nobody knows how the Boeing Company works.
Rather than
bounded rationality, which (as already noted) is usually interpreted as a
particular limitation in processing knowledge, it is
better to begin with bounded cognition. This has the advantage of corresponding with
current
403
ideas
about the development of human cognitive abilities. In the early stages of evolution, standard behaviours were genetically programmed; later creatures
were genetically endowed with some capacity to vary behaviour
by forming new linkages in their brains; performance received evolutionary
priority over logical processing and neurological coding over explicit
codification. Nevertheless what appeared
to be appropriate could differ between individuals because of differences in
the sequence of their experiences. Despite
our intellectual pretensions, this is still the basic method of knowledge formation
in modern humans; that is why ‘we know more than we can tell’, and in
particular why we can perform many actions that we are unable to specify in
detail. However, the emergence of
consciousness introduced the important novel possibility of creating ideas
about the future by making conjectures about new categories and relationships
as yet unrecognised, leading to the possibility of
taking novel actions with the intention of producing novel effects. The scope for variation between individuals
was correspondingly increased, and with it the rate at which knowledge could
grow. This new possibility, we should
remember, is a modification of the old capabilities, which are not displaced,
and relies much more on linkages than logic. [All this is portrayed in Marshall’s (1994)
mental model of ‘Ye Machine’, the product of his early venture into evolutionary
biology.] Indeed, as psychologists have
shown, our powers of logical reasoning are still primitive in relation to the
ability to make novel connections; and if uncertainty is to be gradually
replaced by knowledge the latter is far more valuable.
Kelly (1963)
based his theory of personality on the need to create representations of parts
of a universe that he assumed to be interrelated, though not in the sense of
integral space; some connections were very indirect, some were very weak, and
the ultimate bond was provided by time. The need to construct knowledge, and the role
of imagination in doing so, was emphasised by Adam
Smith ([1795] 1980) in his psychological theory of the emergence and growth of
science as a combination of classification systems and causal links, which he
illustrated by the History of Astronomy. The stimulus to imagination was provided
by the failure of existing patterns of knowledge to account for newly-observed
phenomena - an intrinsic motivation, beginning with unwelcome surprise and
concluding with delight in creating a new pattern that worked, that appears to
have had substantial survival value and still to be effective, but which is not
prominent in economic theory. Since new
‘connecting principles’ led to new expectations, new activities and new
observations, what began as an aid to ordinary living gradually incubated a new
category of knowledge called ‘scientific’. As the psychological and practical value of
this knowledge became more apparent some people came to devote particular
attention to it; and as its growth accelerated it began to divide into distinctive
branches, each with its own set of connections which gave rise to its own
anomalies and consequent stimulus to imagination.
Having
explained how the dynamics of scientific development led to specialisation
which accelerated the process, Smith later transferred this analysis from science
to the economy, and made the power of the division of labour
to increase productivity the basis of his dynamic economic theory (Smith,
[1776]
1976b). Smith was well aware that increased specialisation had its opportunity costs in the neglect of
potentially important connections; this led him to include education as an
important function for government, and to give a special role to “philosophers
or men of speculation” who imagined novel connections between divergent specialisms - or, in Schumpeter’s (1934) language,
conceived of “new combinations”.
Knight (1921,
p. 206) observed that “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”. One class of ‘connecting principles’ serves to
indicate which things should be treated as similar, despite their differences
(and also which things should be treated as different, despite their
similarities); and a second class of principles suggests which categories, so
ordered, should be assumed to be linked, and in what way. Popper (1963, p. 44) pointed out that a
perception of similarity (and also, we may add, of complementarity)
always “presumes a certain point of view”. Thus the construction of knowledge is always
potentially subject to interpretative ambiguity, and the boundaries of
categories are likely to be differently construed by people with somewhat
different histories. Now changes in
knowledge systems, as Potts argues, are mainly changes to adjacent states; and
Marshall expected experimentation to occur at the margins of knowledge. But for any system of any complexity there are
many adjacent states; moreover, what is adjacent tends to differ between people
because of the heterogeneity of their experience, and which of these possibilities
is perceived also tends to differ. Thus at any time there are many margins of
knowledge, arid thus the potential for a great deal of variation.
In one of his
early papers, Marshall noted that “in economics every event causes permanent
alterations in the conditions under which future events can occur” (Whitaker,
1975, 2, p. 163). Not the least of these
alterations is the state of knowledge, which may change both directly through
perception of the event or indirectly because the event prompts a search for a
new interpretation (or in other words for a new connection between categories).
Since, as Metcalfe (2000, p. 148)
reminds us, “the supply conditions for new knowledge depend on the current
state of knowledge”, the growth of knowledge is a
path-dependent process. That does not
mean that it is path-determined, because the conjectures that are represented
by new or modified structures are subject to many different selection processes
- of which selection in markets is no doubt of most interest to the majority of
economists (though we must not forget that a market is a set of institutional
arrangements based on knowledge structures which are themselves subject to
challenge).
However, although evolution is undoubtedly about the emergence of novelty through processes of variation and selection, it is also about stability - and necessarily so. If everything is changing, or even liable to change at any moment, then nothing can be relied on - for making decisions, interpreting information, or constructing new knowledge. Any process of variation and selection is meaningless unless both the variants and the selection environment persist for a time.
405
In Marshall’s
(1994) mental model of a ‘machine’ the lower level maintains a collection of
routines which have worked satisfactorily: and this both frees the higher level
for imaginative exploration and presents clearly defined problems when an
established routine fails to cope with a new situation. Penrose’s firm similarly requires both
evolving resources arid an administrative structure; firms are sense-making
systems which (if successful) combine the cognitive distance which supports specialisation with cognitive similarity in the dimensions
which maintain focus on the objectives of the business.
Individuals
develop structures of knowledge, including knowledge of how and when to perform
particular actions, and how to frame sets of premises as a basis for deductive
reasoning. They learn how to make sense
and how to make decisions, both of which require more than logic, as Chester
Barnard (1938, p. 305) emphasised. However, unless they live a purely solitary
existence they do not have to do this on their own. The activities of others create a range of
vicarious experiments which all individuals may use to test their own conjectures
or to incite their imagination to produce new conjectures; or they may simply
adopt apparently successful patterns of behaviour or
satisfying ways of organising knowledge. This is how we all start as infants; and it is
perhaps the basis of our willingness to accept the authority of many communications
and demonstrations, not only from ‘persons of authority’ (Barnard, 1938, p.
163). It is an obvious economy, and
sometimes an aesthetic pleasure, to free-ride on other people’s wisdom; that is
how Smith ([1795] 1980) explained the diffusion of new cosmological theories
which appeared to resolve worrying problems, and also the adoption of rules of behaviour which appeared to conform with
moral sentiments (Smith, [1759] 1976a). Ways
of thinking and ways of acting that are common within a community need not be
explained as solutions to co-ordination games; they may arise from individual
efforts to solve individual problems.
If the
sharing of patterns and routines has such origins, that
helps to explain how members of a group who have been acting in parallel may
converge on a particular set of procedures for managing interactions. (Smith was well aware of the importance of
this sequence in making civil society possible.) What we call ‘institutions’ when they are
interactive routines are not inherently different from the routines and
assumptions on which people necessarily rely in order to economise
on cognition for their own private purposes; they are an external supplement to
the structure of internal cognition (Choi, 1993), for
every person, like every firm, needs both an internal and an external organisation. Access
to this external cognitive capital depends on the appropriate absorptive
capacity, which, as Cohen and Levinthal (1989)
reminded us, is so important in human progress. We may think of this capability as a set of
receptors which can connect imported elements to internal structures: since it
depends on the connecting principles which are already being used by the
prospective absorbers it is not
a
general ability but context-specific, and therefore embodies opportunity costs.
This specificity is substantially
influenced by the division of labour. The development of such capabilities is a
major function of education; and studies of organisational
learning have shown the importance of social interaction within and between
productive organisations in facilitating such
learning. In both private and interactive
contexts, predominant reliance on routines is necessary in order to create
space for thought; and in both contexts, variation between individuals widens
the range of material about which to think. Codification is an institution which partially
formalises tacit knowledge and thus provides the
basis for the creation of further tacit knowledge.
An obvious
but neglected application of the importance of institutions in encouraging the
growth of knowledge is the emergence of markets. A market reduces the costs of making certain
kinds of transactions by establishing powerful connections. Mark Casson (1982)
deserves the credit for noting that the costs of continuing transactions may be
reduced by appropriate investment, and identifying the entrepreneurial role of
those who make such investments - though as recent events have amply
demonstrated many entrepreneurs may be unfortunate or misguided. When a particular class of transactions has
been substantially reduced to routine, those using that market, as buyers or
sellers, no longer have to think about how to transact and are therefore free
to think about what to transact, how to produce the goods or services to be
transacted and how to make good use of them (Loasby,
2000). Thus the institutionalised
connections provided by a market allow the formation of new connections, both
in trading relationships and in the form of knowledge about both production and
consumption. The emerging interest in
the role of the consumer builds on an understanding of market institutions.
Institutions
provide the connections which support dynamics; they also have their own dynamics,
primarily of adjustments at the margin, but also of regime changes (Dopfer, 2001), which typically draw on patterns of
connections from some other sphere of activity and may be treated as
adjustments at the margin of a higher-level system: the idea that structure
influences behaviour, for example, appears in
different forms across many fields of human knowledge. Knowledge changes institutions, as
institutions shape knowledge. This
process drives the history of economic thought, as well as the development of
productive knowledge and both managerial and entrepreneurial skills.
According to Roger
Myerson (1999, p. 1068), “today economists can define their field more broadly
as the analysis of incentives in all social institutions’. Economic organisation,
which at one time focussed on the relationship
between market structure and economic performance, is now interpreted as the organisation of incentive structures. This is certainly a broadening in one
dimension, but imposes serious constraints in others. Incentives matter; but co-ordination, both
407
within
and between firms (and for individuals too - see Kelly, 1963) is first of all a
cognitive problem, as Richardson (1972) showed. Marshall (1920, p. 138) linked organisation specifically to knowledge, and half-explicitly
linked different forms of organisation to different
kinds of knowledge. But Williamson, in
assessing the merits of different organisational
arrangements, treats governance systems as protective devices against
pernicious incentives and does not, like Penrose (1959, 1995), consider them as
bases for the generation and application of knowledge. Williamson’s (1985, p. 48) declaration that “were
it not for opportunism, all behaviour could be rule governed”
ignores Knightian uncertainty and its counterpart, Shackleian imagination; opportunities, rather than
opportunism, drive the growth of a Penrosian firm,
and no two Penrosian firms are identical.
These
opportunities result from new knowledge which is shaped by institutions that
are fostered by organisational arrangements; the Penrosian process in its administrative framework combines
cognitive, institutional arid organisational dynamics.
The organisation
of a new activity requires new connections to be made, in formal
responsibility, in patterns of interaction and in individual cognition. If the activity is successful most of these
connections cease to require conscious attention; a new set of institutions
releases cognitive skills and organisational
capabilities for other purposes (as in Marshall’s ‘machine’). This is ‘the
receding managerial limit’. At the same
time, the absorbed patterns of behaviour, at all
levels, change the firm’s resources, which may be deployed in directions which
are conjectured by the use of these cognitive skills.
That such
connections between resources and profitable uses are not simply deduced from
the data, as in standard theories which are located in integral space, but need
to be made is a clear and fundamental difference between Penrose’s theory and
the standard ‘theory of the firm’, a difference emphasised
by Penrose’s distinction between resources and productive services (see also
Lane et al., 1996). Writers on strategy
who adopt the ‘resource-based view’ often underrate the significance of this
distinction. We may also think of a firm’s
resources as equivalent to Lachmnann’s conception of
capital: they are elements which may be substituted between uses but which in
any particular use are valuable because of their specific complementarity
(or connections) to certain other elements. If this complementarity
produces what was once called synergy or what we now call superadditivity,
the additional productivity may be attributed not to the elements but to the
connections between them.
These Penrosian single-firm dynamics should be supplemented at
least by the two other Marshallian categories of
forms of organisation that aid knowledge: the firms
within a single trade, by thinking and acting somewhat differently but in
readily comprehensible ways, provide vicarious experiments and vicarious hypotheses
to supplement and interact with the particular knowledge of each, and the
network of complementary trades is structured on Richardsonian
principles of dissimilar ways of organising knowledge
to gain the advantages of the division of labour
while avoiding unhelpful connections (Richardson, 1972), and linked by
incremental adaptations and by speculative visions. The organisation of
production is also the organisation of knowledge, and
both kinds of organ-
isation change over time as the result of what
happens in time. The dynamics of
industrial organisation have never been better
presented than by Allyn Young (1928) in a paper which
rejected the applicability of equilibrium modelling
to an understanding of this process of generating value as a consequence of
rearranging connections by reconfiguring the internal and external boundaries
of the firm, creating new connections to make new markets. Increasing returns are returns not to the
elements but to the connections between them. Such an imputation is impossible in a
theoretical system which assumes a fully-connected economy, but it is a natural
implication of Marshall’s (1920, p. 318) definition of increasing returns as
mediated by organisational change.
The concept
of general equilibrium is not applicable to these dynamics, but local and
temporary equilthria may serve very well to indicate
the knowledge and relationships - well-established connections of various kinds
- on which people may reasonably rely in order to construct useful novel
connections. Innovation is always
innovation in particular respects and at particular levels, and is carried by
continuity, or maintained connections, in other respects and at other levels;
and continuity may be expressed by an appropriate concept of equilibrium,
applied to particular structures of knowledge, institutions, or organisation.
Evolutionary
economics relies on differences, not only between but also within industries; the
effects of these differences on behaviour,
continually modifying and occasionally disrupting the environment in which
firms are operating, requiring new interpretations and sometimes prompting new
perceptions, provide the dynamics. These
processes combine the generation of variety and the elimination of variants
which do not match the criteria by which they are judged; and these criteria
are themselves a proper and neglected field for analysis, for
there are different criteria in different selection environments. However, there is danger in simply replacing
the field theory of physics with neo-Darwinian biology, which excludes human
purpose and sharply differentiates the context of variety generation from the
context of selection, whereas in human brains and human organisations
the contexts are often combined. It is
safer to draw inspiration from Adam Smith’s ([1795] 1980) evolutionary model,
which includes complex motivation, imaginative conjecture (often driven by
aesthetic considerations), selection and diffusion, falsification as a stimulus
to novel conjectures, the evolution of the evolutionary process itself through
increasing differentiation and the crucial importance of the division of labour. Neo-Darwinians
seek to confront us with a stark choice between design and natural selection
among blind mutations; standard economic theory opts decisively for design,
occasionally supplemented by appeals to unanalysed
selection processes to ensure that the design is optimal. Both are corner solutions in the space of
theoretical strategies; evolutionary economics avoids corner solutions by
choosing a sequence of ex-ante decisions and ex-post realisations that may lead to fresh decisions.
409
Contemporary
models of economic organisation often depend on the
concept of asymmetric information, which certainly corresponds to an aspect of
reality; but the more important asymmetry is of interpretation and of
perception, which leads some individuals and some organisations
to take actions that others have dismissed, or never even thought of. Frank Knight’s theory of entrepreneurship and
the firm was based on interpersonal differences in the capacity for judgement - what we might call making connections that
prove to be appropriate - and of differences for each individual between fields
of activity (Knight, 1921, p. 241). Shackle’s (1979, p. 26) beautiful phrase “the imagined, deemed
possible” invites us to consider the stimulus and sources of imagination and
why some products of the imagination are deemed possible by particular
individuals while others are not.
Imagination
and the assignment of possibility require the making of new connections, and
often the discarding of old connections which appear to conflict with them, a
process that is easier to understand in retrospect than to predict. Because new knowledge, new institutions and
new organisations must all develop from connected
systems (at some level) that already exist, change is always path-dependent; but
this dependency may vary greatly in both degree and kind, often leaving much
scope for imagination, especially if we extend Shackle’s phrase to include the
imagined, deemed capable by some entrepreneur of being made possible. Since the number of connected networks that
are conceivable is unimaginably greater than the number that can be handled by
any human brain - or indeed by any organisation that
depends on manageable interactions between human brains - it is not surprising
that there will be a great variety of opinions about what will work, and what
will be profitable. There will be a high
rate of failure; the dynamics of evolutionary economics requires both ex-ante and ex-post selection.
This variety,
and its potential, justify concluding this sketch of evolutionary dynamics by
invoking George Richardson’s (1975, p. 359) principle: “Surely it is of the
essence of competition that the participants hold uncertain and divergent
beliefs about their chances of success”. This is competition between different ways of
thinking; and the co-ordination problem within an economy is that of achieving
the necessary compatability between different ways of
thinking while preserving the differences. There are difficult incentive issues here, but
they are not the incentive issues that dominate Myerson’s conception of
economics, for they are linked to co-ordination problems at many levels, at
each of which some connections are to be encouraged and others avoided. Knight (1921, p. 268) observed that “[w]ith uncertainty absent... it is doubtful whether intelligence
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