articulo loasby

8/8/2019 Articulo Loasby

1/13

Research Policy 31 (2002) 12271239

The evolution of knowledge: beyond the biological model

Brian J. LoasbyDepartment of Economics, University of Stirling, Stirling, Scotland FK9 4LA, UK

Received 17 September 2001; received in revised form 22 January 2002; accepted 23 January 2002

AbstractThe analysis of the evolution of knowledge is distinguished from standard economics and neoDarwinian biology; it com-

bines purpose with the impossibility of empirical proof. Adam Smiths psychological theory includes motivation, aesthetics,

invention, diffusion, renewed search, the speciation of knowledge and the division of labour. Knightian uncertainty gives

rise to both routines and imagination; variation and selection require a baseline. Organisation and institutions, both of which

entail selective connections, aid knowledge, and knowledge consists of conjectured connections, open to refutation. Modern

biological ideas about cognition provide an appropriate basis for this analysis, but do not encompass it.

2002 Elsevier Science B.V. All rights reserved.

Keywords: Cognition; Uncertainty; Imagination; Connections; Organisation

1. Theme

In this paper, evolution is broadly defined as

a process, or cluster of processes, which combines

the generation of novelty and the selective retention

of some of the novelties generated. This defini-

tion is sufficient to distinguish evolutionary theories

from theories that are clearly not evolutionary, while

allowing us to identify distinctive kinds of evolu-

tionary theory; I intend to do both, first differenti-

ating evolutionary from non-evolutionary economics

and then arguing that evolutionary economics hasimportant differences from the biological model,

which may however provide a useful complement

to it.

The principal focus of discussion is technologi-

cal innovation and the growth of knowledge, both

theoretical and practical, which it embodies. This

is an evolutionary process, to be sharply distin-

guished from mainstream economic analysis; but it

E-mail address: [email protected] (B.J. Loasby).

is not neoDarwinian. Ex-ante as well as ex-post se-lection is an essential feature, and the processes of

variety-generation and selection, instead of being

separated, are often deeply entwined: the incubation

of a new artefact or method of production involves

frequent rejection of candidate variants, which may

lead directly to new design, and users are shapers as

well as selectors. The selection criteria correspond to

neither biological concepts of fitness nor standard

economic notions of optimality; they include emo-

tional and aesthetic as well as rational elements,

and even the rationality is often of a kind that failsto meet the emotional and aesthetic criteria of or-

thodox modern economists. Technological evolution

rests on new ways of organisating knowledge, and

is supported by the organisation of the process of

generating, testing, and modifying knowledge. Under-

pinning these activities are the biologically-evolved

capabilities and motivations of human beings, and an

understanding of these capabilities and motivations,

rather than transferable models, is the prime con-

tribution that evolutionary biology can make to the

0048-7333/02/$ see front matter 2002 Elsevier Science B.V. All rights reserved.

P I I : S 0 0 4 8 - 7 3 3 3 ( 0 2 ) 0 0 0 6 0 - 4


2/13

1228 B.J. Loasby / Research Policy 31 (2002) 12271239

study of technological innovation. This contribution

is summarised in the final section of the paper.

1.1. Evolution, rationality and knowledge

NeoDarwinians claim that the only alternative to

their explanation of life-forms by natural selection

from random mutations is the now-discredited expla-

nation by design. However, explanation by design, in

the form of equilibria based on universal optimisation,

is the foundational principle of standard economics

(though not, as relatively few economists recognise,

of Adam Smiths economics), and it is deemed suf-

ficient for standard economic analyses of technical

change, although these analyses trivialise change. An

evolutionary process within the economics profession,

including directed variation and internal as well as ex-

ternal selection, has led to the institutionalisation of a

style of modelling which relies on what outsiders may

consider to be an extremeand even irrationalform

of rationality: all agents base their choices on the cor-

rect model of the economy, which includes (usually by

implication) the correct model of every other agents

behaviour. That is why agents are so successful in

designing their plans that no revision is necessary.

In this analytical system, selection is highly effi-

cient, and takes place before the event; there is thus noroom for any kind of process that might reasonably be

called evolutionaryor indeed anything that might

be called a process in the usual sense of the word.

In both neoDarwinian theory and standard economics

selection is determined by consequences; but whereas

in neoDarwinian theory mutations must be introduced

into the environment in order to discover these con-

sequences, in standard economics the consequences

of any contemplated action are correctly deduced in

advance (if necessary, as a probability distribution).

The criteria of rigorous theorising in economics re-quire the set of possible actions and the set of possi-

ble 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 fine-

ness of its partitioning. Agents can learn only in the

form of obtaining increasingly accurate estimates of

the likelihood of each possibility by a procedure that

is fully specified in advance. Nothing can happen that

was not provided for; and nothing will ever induce

agents to envisage new possibilities. Novelty is han-

dled by specifying a new model, with no account of

the transition between models; and the possibility of

novelty is incompatible with optimisation. Time is anadditional dimension of goods and information sets,

to the exclusion of any analysis of an economy which

develops in time. This reliance on explanation by de-

sign, incorporating efficient ex-ante selection, appears

to bring economic analysis into direct conflict with

biological principles.

However, matters are not so simple. Some econo-

mists have suggested that models of rational choice

equilibria are simply convenient instruments for pre-

diction, which usually work well because the opera-

tion of markets leads to outcomes that resemble those

of well-informed optimisation. (For an extensive treat-

ment, see Vromen, 1995). Evolutionary processes in

economic systems are so effective that no study of

them is necessary; ex-ante and ex-post selection are

close to being observationally equivalent, and ex-ante

selection is easier (and more elegantone should not

overlook the aesthetics of rationality) to model.

In the most thoughtful version of this argument,

Alchian (1950) simply claimed that market selection,

operating on non-rational behaviour, produced an aver-

age response in the appropriate direction to any change

in circumstances. This, he thought, was as much ascould reasonably be hoped for; the optimality of these

outcomes was not a critical issue. Other economists

have been less cautious in asserting that market selec-

tion can duplicate the results of rationalityin strik-

ing parallel to the claims, once widely thought to be

both irrefutable and significant, that central planning

and perfect competition (under appropriate conditions)

deliver identical outcomes. Some neoDarwinians are

so impressed by the effectiveness of natural selection

(and implicitly by the ability of mutation eventually

to generate the necessary near-ideal variants) that theyare inclined to consider the structures and behaviour

of biological life-forms to be very close to optimal-

ity, and may believe (with some plausibility) that they

have better grounds than economists for this claim be-

cause biological evolution works on a much longer

time-scale (Smith, 1996, p. 291).

These opposite conceptions, of highly efficient, if

very slow, natural selection from random mutations,

and optimal choice, which is virtually instantaneous,

from known opportunity sets, both facilitate the


3/13

B.J. Loasby / Research Policy 31 (2002) 12271239 1229

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 selectionby and of practitioners. Neither, however, is a good

match to the problems of human activity, of which

technological innovation is a prominent example. The

fundamental difficulty with rational choice theory is

its untenable assumption about human knowledge:

when making important decisions, people rarely know

either what options are available or their possible

consequences (Knight, 1921); and the fundamental

difficulty with neoDarwinian explanations of human

activity, as Penrose (1952) insisted in response to

Alchians claim, is that it ignores human purpose.

Human action is often the result of human design;

but human design is inherently fallible, however se-

cure its logic, since it is based on knowledge that is

usually incomplete or erroneous, and so things rarely

turn out precisely as intended: outcomes may be bet-

ter, or worse, or just different. Technical change, like

most human activities, lies in the interval between

optimal choice and chance variation, and by opting

for either, or both, of these models (which we might

think of as corner solutions in the space of theoretical

principles), we exclude at the outset the possibility

of understanding what is happening, and not leastthough this topic will not be directly addressed in

this paperof understanding the development of

academic disciplines.

The analysis of evolutionary processes in human

societies should not exclude rationality in the broad

sense of acting for good reasons; but neither should

it exclude the essential incompleteness of knowledge.

As experienced by humans, this incompleteness re-

sults from the interaction of two fundamental factors.

Economists sometimes recognise the first of these as

Herbert Simons problem of bounded rationality;however, this term lends itself to misrepresentation as

maximisation in the presence of information costs, and

bounded cognition signifies more clearly the limita-

tions of our logical powers and the need to impose,

rather than deduce, simplifications in our representa-

tions and short-cuts in our decision-making. Making

full use of all relevant information, far from being

a definition of rational behaviour, is rarely an option.

What is even more fundamental, but totally ignored in

both general equilibrium and game-theoretic versions

of rational choice theory, is David Humes problem:

there is no way of demonstrating the truth of any gen-

eral empirical proposition, either by deduction, for

there is no way of ensuring the truth of the premises,or by induction, for there is no way of proving that

instances not observed would correspond to those that

have been observed. Reason is not enough.

The interaction of Simons and Humes problems

is most acute in situations of complexity. The need

for simplification is generally recognised, but a logi-

cally impeccable simplification must begin with a full

representation of the complex systemwhich Simon

and Hume have both shown is unobtainable. Our only

recourse is to impose a simple view, and to add com-

plexity to our representation as we are impelled to do

so and to the extent that we are capable of doing so,

trying to remember all the time that what we have is

indeed only a representation, which may be deficient

in some crucial respectas the course of technolog-

ical innovation has often revealed. That our systems

of thought rely on connections that we have invented,

or adapted from their inventors, and not those which

have been optimally chosen from a complete set, is a

fundamental principle of evolutionary economics (see

Potts, 2000; Loasby, 2001). Human knowledge is a

self-organising system.

Since information, far from being self-containedtruth, acquires meaning from its context, the im-

plications of complexity are pervasive. Recourse to

science does not resolve these difficulties, for sci-

ence also depends on the linkage of known empirical

phenomena into a wider network of acceptedor at

least potentially acceptablefacts and concepts

(Ziman, 2000a, p. 291). [T]he production of such

linkages is the main business of research, and scien-

tists have developed elaborate procedures for testing

them, using logical reasoning to identify testable im-

plications of candidate hypotheses; but no scientificproposition is in principle beyond challenge, and pre-

cision at the focus of attention is achieved only by

ignoring the lack of definition as we approach the

edges of the image (Ziman, 2000a, p. 291).

The epistemology of science is based on uncertainty

(Ziman, 2000a, p. 328), and many scientific papers are

later dismissed as embodying false hypotheses or inad-

equate tests; yet the evolutionary processes of science

have produced a remarkable growth of knowledge

which may reasonably be treated as reliable (Ziman,


4/13


1978). Reliability is a consequence of appropriate and

distinctive operating practices for generating and test-

ing ideas, based on the Mertonian norms of communa-

lism, universalism, disinteredness, originality, andscepticism, which are responses, not always explicit,

to the impossibility of final validation, and to the cor-

responding dependence on intersubjective appraisals

to produce structures of knowledge that are cogni-

tively objective (Ziman, 2000a, pp. 303, 316). These

norms are not reducible to any conventional notion of

rationality, though they embody more than an echo

of Adam Smiths (1976a [1759]) Theory of Moral

Sentiments, and indications that adherence to them

is less secure than in the past is a potential threat

to scientific credibility. Although it is possible to

make meaningful distinctions between science and

what Ziman calls life-world knowledge, science has

evolved from earlier attempts to make sense of the hu-

man situation, and continues to rely on some knowl-

edge which has not been formally brought within the

corpus of scientific analysis (Ziman, 2000a, p. 297).

This permeable boundary and the continuing resem-

blances across it offer some prospect of investigating

the behaviour of people who are not scientists, but

who are not rational maximisers either.

Responses to uncertainty include various kinds of

coping strategies, including strict observance of rou-tines, contingent application of rules (or of Simons

rather wider category of decision premises), and

taking precautions such as the building of reserves,

including reserves of goodwill from earthly or heav-

enly powers. But they also include responses of a

very different kind; for the obverse of uncompletable

knowledge is the scope for imagination. Uncertainty

can be exploited as well as endured. This has been the

most distinctive as well as the most persistent theme

in George Shackles writing (see especially Shackle,

1972, 1979). People may generate novel options andimagine new contingencies, make novel selections

among alternative hypotheses and embody some of

them in artefacts and many in institutions which guide

action; on observing the outcomes they may select

among them, according to the theories by which they

impute causality and their criteria for the acceptabil-

ity of explanations (Loasby, 1995). Selection may

lead to the generation of further hypotheses, some-

times (and especially in technological innovation) in a

closely-coupled way. Such evolutionary processes are

likely to be effective means of progress, though not

always of improvement in terms of human welfare.

Uncertainty thus justifies an evolutionary approach to

the growth of academic, technological and everydayknowledge, but an approach which goes beyond the

biological model.

NeoDarwinian evolution requires stability in both

the selection environment and the genotypes which

are subject to selection; it also requires genetic mu-

tation 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 neoDarwinian

evolution to be both incremental and extremely slow.

This is not a good model for technological change

or the development of human knowledge, though it

does encourage us to postulate stable genetic charac-

teristics 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 nei-

ther ex-ante nor ex-post selection can be significant.

The coping strategies just mentioned are often very

important in providing such a baseline.

NeoDarwinians insist that in any evolutionary pro-

cess, there should be only one unit of selection; but

techniques, artefacts and firms evolve interactively,as do institutions, organisational arrangements, and

bodies of knowledge, including know-that, know-why,

know-how and know-who. This interdependence of

both variety-generation and selection at various levels

is an important feature of human history, and presents

obvious analytical difficulties. The essential require-

ment is to distinguish, at each stage of analysis, be-

tween the elements and the connections that remain

stable and those that change. This combination varies

according to time and circumstance; and there is no

simple hierarchy. Sometimes established elementsare assembled into a novel architecture; sometimes

a modular architecture facilitiates quasi-independent

developments; in both cases, the innovation incor-

porates a great deal that is familiar, as dramatically

illustrated in Constants analysis of the Lockheed

Starfighter, which also illustrates the interaction

between technological and organisational evolution.

Stability in the direction of technological change is

likely to encourage variation within that trajectory

and also variation in the combination of techniques to


5/13


produce artefacts. Decomposition and recombination

are important principles both in technological innova-

tion and in the study of technological innovationas

in other kinds of knowledge.

1.2. The evolution of ideas and capabilities

As our foundation model of the growth of knowl-

edge as an evolutionary process, we cannot do better

than Adam Smiths psychological theory of the emer-

gence and development of science, which he illustrated

by the history of astronomy (Smith, 1980 [1795]).

Smiths theory combines two distinctive themes from

his friend David Hume: the impossibility of proving

empirical truths and the rejection of the supremacy of

reason, which alone can never produce any action

(Hume, 1978, p. 414) and therefore is, and ought only

to be, the slave of the passions, and can never pre-

tend to any other office than to serve and obey them

(Hume, 1978, p. 415). Rational choice is an inade-

quate explanation for behaviour, because neither the

empirical premises nor the objectives of behaviour can

be logically derived.

The practical significance of these twin deficien-

cies is examined (without reference to Hume) in

Chester Barnards (1938) lecture on Mind in Every-

day Affairs. Barnard (1938, p. 308) suggests thatthe insufficiency of reason to determine action is

probably why it is difficult to make correct decisions

without responsibility. In this, as in many other

ways, organisation changes behaviourwhich is the

underlying theme of Barnards book; more generally,

as Potts (2000) argues, the performance of any system

cannot be reduced to the performance of its elements

but depends on the particular nature of the connec-

tions between them. As we shall see, this principle is

Adam Smiths key to the growth of knowledge. The

search for novelty cannot be rational, for no kind ofreasoning can give rise to a new idea (Hume, 1978,

p. 164). It is therefore no accident that explanations of

entrepreneurship go beyond optimisation: Kirzners

(1973) entrepreneurs are alert, and Schumpeters

(1934) have powerful non-pecuniary incentives.

The motivation for generating new ideas is the first

element in Smiths theory. He draws attention to three

general human passions, arguing that people are dis-

turbed by the unexpected, dismayed by the inexplica-

ble, and delighted by schemes of thought that resolve

the inexplicable into plausible generalisations, and

claims that, in the absence of any assured procedure

for attaining correct knowledge, these are the motives

which lead and direct philosophical enquiries. Theyare a long way from the incentives in economists

models, but perhaps not so far from some of the in-

centives that shape the behaviour of technologists,

and of economists also.

The second element in Smiths theory is the se-

quence that is inspired by this complex motivation:

a combination of imagination and ex-ante selection

guides the invention of connecting principles which

sort phenomena into categories and link these cate-

gories by an explanation which is sufficient to soothe

the imagination. Smith (1980 [1795], pp. 61, 90)

shows how the equalising circle in Ptolemaic geom-

etry and Keplers rule that when one body revolved

round another, it described equal areas in equal times

appealed to principles of motion that conformed to

prevailing conceptions of good order; most economists

accept the notion of rational expectations because it

fits their idea of a good theory; and both technology

and business strategy are shaped by what people feel

comfortable with. Ideas must satisfy the selection cri-

teria of the imagination.

Smiths third element, implicit in the reference to

notions of good order, is the link between emotionand aesthetics. He explains the importance of aes-

thetic criteria both in guiding conjectures, for example

those of Copernicus and Kepler, and in encouraging

their acceptance, notably in discussing the rhetorical

appeal of Newtons theory, which in his Lectures on

Rhetoric exemplifies Smiths 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 el-

egance of rational choice equilibria) are occasionally

recognised but rarely explored (see Schlicht, 2000);aesthetic influences on the design of artefacts are of-

ten 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 ef-

fective performance; bridges and aircraft are obvious

examples, and the flawed design of the Millennium

footbridge in London, which caused it to sway so dis-

concertingly in use that it had to be closed, is a recent

reminder that surrogacy should not be assumed.


6/13


The fourth element in Smiths proto-evolutionary

theory is his proposition that connecting principles

which seem to work well are widely diffused because

of our readiness, when in any difficulty or discomfort,to look for guidance from others who seem to know

better, and because of our desire to act, and indeed

think, in ways that merit the approval of others. These

powerful motivations, together with the underlying

similarity in human mental, emotional and aesthetic

processes which underpins them, are foundational

principles ofSmiths (1976a [1759]) Theory of Moral

Sentiments, which is itself an essential component of

Smiths complex account of social organisation, and

applicable both to technological evolution and any

adequate understanding of organisational behaviour.

However, because by Humes argument invented

principles, however widely accepted, are not proven

trutheven, as Smith (1980 [1795], p. 105) explicitly

notes, when these principles have been invented by

Newtonthey are liable eventually to be confronted

with unexpected phenomena which they cannot be

adapted to explain. At this point, the product of human

imagination and design is rejected, and a new search

for connecting principles begins. This is the fifth ele-

ment, which renews the evolutionary sequence.

The sixth element in Smiths system is the evo-

lution of the evolutionary process itself. The basichuman activity of seeking psychological comfort by

inventing and imposing connecting principles gener-

ates an increasingly distinct category of knowledge

which comes to be called scientific, with its own

group of practitioners; and as this category expands,

we begin to observe a progressive differentiation be-

tween sciences that we might now label speciation.

The consequent differences of focus and of criteria

for acceptable categories and explanations lead to an

increasing variety of problems that are more precisely

defined, accelerating the growth of science.It is in this scientific context that the effects of the

division of labour first appear in Smiths (1980 [1795])

surviving work; it therefore seems natural that in theWealth of Nations he invokes the division of labour,

not as the best way to exploit differentiated skills

which was a very old ideabut as the chief instrument

for improving productive knowledge (Smith, 1976b

[1776]). This is the seventh element of Smiths evolu-

tionary theory; and it is easily the most important idea

in economics, since the co-ordination problem which

normally receives priority among economists would

be trivial without the continuous generation of new

knowledge and new artefacts.

Smiths prime connecting principle of the divi-sion of labour was applied to physiological diversity

in 1827 (Milne-Edwards, 1827) and this applica-

tion in turn contributed to Darwins visiona novel

connectionthat a Malthusian struggle to survive

would result in the differentiation of species (Raffaelli,

2001). The other basic elements in Smiths account

of the development of knowledge by motivated trial,

error, amendment and diffusion understandably did

not, for they go beyond biology. We may therefore

suggest that Smith provides a better basis for evolu-

tionary economics than biological models; we may

also observe that different analytical systems, fo-

cusing on different patterns of connections, may be

most effective in developing different kinds of knowl-

edge, thus explaining the value of speciation among

academic disciplines.

The differentiation of knowledge is a condition of

progress in human society. However, it has its oppor-

tunity costs, of which I will mention two. Differences

in the structure of understanding, and in the criteria

for good theory and good practice, though providing

necessary frameworks for the construction of knowl-

edge of distinct kinds, may create substantial obstaclesto the integration of these distinct kinds of knowl-

edge and impede the combination of technological

and non-technological perceptions of any particular

innovationan issue of particular relevance to public

debate on research policy, and the theme of a par-

ticularly instructive study (Pool, 1997). A particular

case of such differences in perceptual structure was

identified by Hayek very early in his career: events

which to our senses may appear to be of the same

kind may have to be treated as different in the phys-

ical order, while events which physically may be ofthe same or at least of a similar kind may appear as

altogether different to our senses (Hayek, 1952, p. 4).

The desire to assuage the discomfort of this apparent

contradiction led Hayek to construct an evolutionary

account of the development of The Sensory Order,

which preceded the emergence of scientific explana-

tion. Ryle (1949), who is cited by Hayek, similarly

pointed out that the knowledge required for effective

performance is of a different kind from knowledge

of facts and theories; theoretical developments may


7/13


therefore not map readily onto recursive practice, and

know-how may resist usable codification.

A second opportunity cost of the differentiation

of knowledge is the neglect of potentially crucialinterdependencies. When the compass of potential

knowledge as a whole has been split up into superfi-

cially convenient sectors, there is no knowing whether

each sector has a natural self-sufficiency . . . . What-

ever theory is then devised will exist by sufferance

of the things that it has excluded (Shackle, 1972,

pp. 353354). This is a key issue in the management

of innovation, as in many other fields. Unanticipated

technological disasters are frequently traceable to un-

justified assumptions (which are usually unconscious,

but not always so) about the sufferance of something

excluded from the processes of design, testing, or

operator training. The Millennium footbridge already

mentioned is an exemplary demonstration.

1.3. Implications of uncertainty

The double-edged character of uncertainty is the fo-

cus of Frank Knights classic Risk, Uncertainty and

Profit(1921), and pervades Shackles work. Knight re-

stricted 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, asin calculating the expected results of throwing dice, or

by statistical analysis of appropriate evidence. Choices

under risk may therefore be made by a standard pro-

cedure which can be demonstrated to be optimal, but

such demonstrably optimal procedures cannot be a

source of sustainable profit for any firm or individual.

For conditions of uncertainty, however, no demonstra-

bly optimal procedure can be devised; we must act in

the space between optimality and randomness.

But if uncertainty creates difficulties, it is also a

necessary condition for imagination, as in Smithspsychological theory: indeed, Knight argues that this

must be the basis of any economic explanation of

entrepreneurship and profitand also the firm, which

provides shelter for those who are unwilling to cope

with uncertainty in person and prefer the conditional

security offered by entrepreneurs. In the absence of

uncertainty, all economic activity can be arranged

by contracts between people who can use standard

procedures to agree the terms of these contracts; this

is still the basis of most economic theory, in which

uncertainty is assimilated to riskeven though the

emergence of new ideas within economics depends

on uncertainty about the adequacy or applicability of

existing theory.Knight observes that in a world without uncertainty

it is doubtful whether intelligence itself would exist

(Knight, 1921, p. 268): this locates the role of intel-

ligence squarely in the space between optimal design

and random activity, and warns us not to identify in-

telligence with formal logic. This message is repeated

in Barnards analysis of the problems of running a

business, and in Niels Bohrs warning: You are not

thinking; you are merely being logical (Frisch, 1979,

p. 95). The insufficiency of logic underlies Knights

(p. 241) observation that [m]en differ in their ca-

pacity by perception and inference to form correct

judgements as to the future course of events in the

environment. This capacity, moreover, is far from

homogenous; individuals also differ in their capacity

to change, and learning takes time (Knight, 1921,

p. 243). Though he makes no reference to Adam

Smith, Knight is here observing the effect of the di-

vision of labour on the development of differentiated

intelligence.

Knight (1921, p. 206) is also unconsciously close

to Smith in arguing that in order to live intelligently

in our world. . .

we must use the principle that thingssimilar in some respects will behave similarly in

certain other respects even when they are very dif-

ferent in still other respects: we rely on connecting

principles of association and causationtogether

with the sufferance of the things that [they have]

excludedin developing our own ideas and in adapt-

ing other peoples. (The biological basis for this

reliance is explained in the final section of this pa-

per.) For differently-formulated problems, we rely on

different contexts of similarity, and for new problems

we experiment with new connections that define newcontexts: that is how the division of labour leads to

differentiated knowledge. New connections provide

us with new rules and routines, releasing cognitive

capacity for new applications, as in Penroses (1959)

conception and use of the receding managerial limit.

By insisting on contexts of incomplete similar-

ity, Knight preserves uncertainty at the core of his

analysis. Why should we assume that, within the cat-

egories that we invent, the similarities dominate the

differences, while between these invented categories


8/13


the reverse applies? As Popper (1963, p. 44) pointed

out, any such judgement reflects a particular point

of view, and Smith showed how points of view may

change, leading to a rearrangement of categories.This inherent ambiguity of interpretation is both a

source of error, occasionally catastrophic, and an op-

portunity for imagination. The difficulty of delimiting

the capabilities of any individual or organisation is a

prominent theme in Nelson and Winters (1982) the-

ory, and underlies Penroses (1959) emphasis on the

need to connect resources that have been developed

in the course of business to new productive opportu-

nities. Ambiguities also explain why diffusion, which

typically encounters different contexts of similarity

(as my former colleague Frank Bradbury frequently

reminded us), may be unexpectedly difficult and also

a major contributor to the content of innovation.

What is deemed possible, andeven morewhat

is deemed capable of being made possible, depends

upon perceptions of the applicability of both theoret-

ical and practical knowledge to novel contexts.

Category-based judgements of possibility, which of-

ten differ between individuals in a firm and firms in

an industry, lead and direct the innovation process; but

because they are fallible conjectures they cannot allow

us to deduce a successful course of action from the

specification of a desired final state. Reverse engineer-ing may reconstruct the process of manufacturing an

existing artefact; but a successful artefact is a corrob-

orated conjecture among many conjectures that have

failed, and its success does not provide a guaranteed

procedure for developing a new artefact. The develop-

ment of science may also be reconstructed as a logical

progression; but the logic is only retrospective. There

are many divergent pathways from established ideas

and many ways of linking ideas, and which path seems

worth following depends on conjectured contexts of

similarity. Connections have to be invented. There isno better example of this than the centuries-old search

for a proof of Fermats last theorem (Singh, 1997).

Knights principle of supposedly-relevant similari-

ties is exemplified by scientific and social scientific

theories, design trajectories, recognised good practice,

and many other institutional aids to cognition, which

enable us to do far better (most of the time) than ran-

dom speculation; but all these forms of ex-ante se-

lection are themselves conjectures which need to be

reinforced, modified, and sometimes superseded by

ex-post selection. Recent advances in medical knowl-

edge have created new contexts of similarity, or institu-

tional frameworks for innovation, which have enabled

pharmaceutical companies to refine their search fornew compounds; but they do not permit the specifica-

tion of a safe and effective drug to be deduced from the

desired effects (Nightingale, 2000, p. 337). They have

not therefore reduced the number of candidate com-

pounds that are screened, and there is little evidence

that this is translating into improved performance

(Nightingale, 2000, p. 351). Moreover, we should re-

member Knights warning that a standard procedure

for attaining optimal outcomes cannot be a source of

distinctive advantage; agreement between pharmaceu-

tical businesses on the best way to organise research

is more likely to reduce than enhance their profits, un-

less they can also erect barriers against rivals. Diver-

sity remains a general condition both for profit and

the growth of knowledge; and the effect of system di-

versity on development is a basic evolutionary theme

(Pavitt, 1998, p. 439).

We noted earlier that a major opportunity cost of

diverse ways of connecting perceptions, phenomena

and ideas, is the problem of co-ordination between

individuals and between groups whose knowledge

is differently ordered. Standard economic analyses

of co-ordination focus on differences in objectivesand access to information, but ignore differences in

ways of constructing knowledge. However, an earlier

economist who recognised the value of these differ-

ences, Alfred Marshall, offers some helpful advice.

Organisation aids knowledge; it has many forms, e.g.

that of a single business, that of various businesses

in the same trade, that of various trades relatively to

each other, and that of the State providing security for

all and help for many (Marshall, 1920, pp. 138139).

The organisation of each firm privileges a particu-

lar network of connections between groups who relyon distinctive contexts of similarity, thus producing,

at individual, sub-unit and firm level, differentiated

responses to information and somewhat different

conjectures. (The influence of its administrative

framework is crucial to Penroses (1959) account of

a firms development). The knowledge structures in

these networks vary between industries, and within

the value chain of each industry, in order to facilitate

appropriate combinations of similar and complemen-

tary capabilities (Richardson, 1972); and within each


9/13


structure differences in temperament, associations

and experience lead to the variation in detail that

generates progress through trial and error (Marshall,

1920, p. 355). These organisational forms are theequivalent of differentiated species and variety within

species, but (in contrast to the biological model) they

embody purpose and imagination, internalise and ac-

celerate a considerable part of the selection process

and use the outcomes of selection as a guide to further

experimentation.

This evolutionary process extends to the organisa-

tions themselves; the most effective forms of internal

structure and external relationships change over time,

largely as a consequence of their own effects. This

theme was most forcefully expounded by Young

(1928), who insisted that the increasing returns which

have been fundamental to economic progress are not

an equilibrium phenomenon, but define a process of

developing new patterns of connections within an

economy. We may extend Youngs argument from

changes in the distribution of activities between firms

to similar changes within each firm and indeed to the

changing patterns of knowledge within each person,

and their connections to other knowledge. All this may

be derived from Adam Smiths original account of

the growth of knowledge; that the derivation has been

far from obvious is a particularly striking example ofthe difficulties of improving our own knowledge.

Knowledge itself is organisation, produced by trial

and error, and always subject to challenge, including

changes in its form and its relationships to other bodies

of knowledge; it is a product as well as a precondition

of decisions. Knowledge lies in the particular connec-

tions between elements, rather than the elements them-

selves; this is a concept foreign to microeconomics, in

which connections are assumed to be complete except

when the absence of a particular connection is identi-

fied as a source of market or organisational failure. Itis a unifying principle of evolutionary economics that

the performance of any system depends not only on

the elements of which it is composed but on the par-

ticular pattern of connections, both direct and indirect,

between them (Potts, 2000; Loasby, 2001).

Since technological innovation is an expression of

the development of human knowledge, and especially

of knowledge how, an understanding of human knowl-

edge provides a basis for understanding technological

innovationnot least because the power and fallibility

of human imagination and human calculation seem

to correspond to the remarkable successes and myr-

iad failures of technology. It is the combination of

uncertaintythe unlistability of possibilities and theabsence of any procedure, known to be correct, for

assessing and evaluating those possibilities which are

listedand the evolved characteristics of human cog-

nition that warns of the likelihood of failure but also

creates the alluring prospect of extraordinary success,

as well as explaining our reliance on institutions.

Progress in both knowledge and technology depends

on the diversity of individual initiative, but also on the

relationships, formal and informal, between individu-

als; for every one of us, as well as for the communities

to which we belong, knowledge depends on the or-

ganisation 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.

2. A biological foundation

As Adam Smith knew, the appeal of connecting

principles is enhanced by their scope; so the attraction

of reducing evolutionary processes to neoDarwinism

is easy to understand. However, as Ziman (2000a,pp. 324326) explains, each level of complexity must

be studied on its own terms, and evolutionary pro-

cesses in social and economic systems, as we have

seen, have distinctive features. Nevertheless compata-

bility between levels is a useful criterion, and bio-

logical models may be very helpful in understanding

the cognitive characteristics of biological creatures

who are capable of producing true novelty, and yet

are so dependent on rules and routines. The recent

shift of attention from the simple artificial intelli-

gence model of the human brain as a serial logicalprocessor in favour of a conception of multiple neural

networks has produced an understanding of evolved

human capabilities which is entirely consistent with

the Smith/Knight theory that the growth of intelli-

gence is driven by the imperative of coping with

situations that are not amenable to logical solutions.

That human intelligence relies on connecting prin-

ciples rather than formal logic is suggested by the

results of a wide range of experimentation by psycho-

logists with versions of the Wason test, in which


10/13


subjects are asked to identify evidence which is rele-

vant to the refutation of a simple proposition. These

experiments have repeatedly demonstrated very poor

performance when the test is presented in an abstractform which most clearly displays the underlying log-

ical structure, and far better performance in contexts

which are more complex but with which the subjects

are familiar. The human brain appears to recognise

similarities much more readily than logical implica-

tions. This propensity to impose similarities and to

accept similarities which can be assimilated to fa-

miliar categories is the psychological foundation for

Smiths (1980 [1795]) account of the creation and ab-

sorption of new knowledge, including his recognition

of the obstacles to absorption among those for whom

no such assimilation is possibleor in other words,

who lack the relevant absorptive capacity.

A plausible biological pathway to a human brain

with such characteristics may be traced by consid-

ering the environment in which it has evolved. The

evolutionary success of our predecessor species was

promoted by rapid identification of threats and op-

portunities and effective and specific responses to

each; and both identification and response required

the close co-ordination of sensory impressions and

physical activities. In the early stages of animal evo-

lution, locally-appropriate networks were geneticallyprogrammed, as is still true of many of the neural

networks that regulate human activity; and later mu-

tations produced programmes for the development of

new networks in response to new threats and oppor-

tunities.

A generalised capacity for making patterns provides

the potential for much more flexibility than a general

capacity for logical reasoning. There must, however,

be sufficient motivation to create new patterns when

appropriate; and the importance of pattern-making ac-

tivity suggests an obvious role for aesthetic sensibil-ity as a motivator which has enhanced evolutionary

success and both stimulated and shaped scientific as

well as other forms of knowledge. Smiths 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 eco-

nomics, 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 Smiths principle of improved performance as

a consequence of the division of labour, more easily

achieved by this means than by incremental adapta-

tions towards a general-purpose logical processor.The neural structures of each persons brain are the

product of two selection processes: genetic selection

provides the basic architecture and some of the con-

nections, but much of the brains development occurs

after birth in the process of making sense of the world

by imposing order on events. It is this imposed or-

der, not the events themselves, that constitutes experi-

ence (Kelly, 1963, p. 73); though genetically enabled,

it goes beyond genetics. The development of individ-

ual knowledge and skills is path-dependent, but not

path-determined: the network supports routines of be-

haviour and rules for conceptualising and resolving

problems, and it strives to preserve itself, sometimes

by denying the validity of information. This inertia

is necessary despite the obvious dangers; for without

firm anchors no intelligent variation is possible. A sta-

ble baseline is a condition of change.

Furthermore, change embodies stability; the devel-

opment of new networks embodying new knowledge

typically relies on exaptationthe use, often with

some modification, of existing structures for new

purposes. As Potts (2000) explains, change is pre-

dominantly to adjacent states; the novelties attainableby any person at any point of time are conditioned

by pre-existing structures and the history of past

adaptationsthe evolved institutions of the brain.

Innovation may require the breaking of some estab-

lished connections, but they must be replaced by new

connections which form complementary relationships

with some preserved patterns. The new ideas and the

old may be incommensurable in the straightforward

sense of not being partitions of a single structure

of knowledge, but there is no absolute break. This

is a notable feature of Smiths account of scientificprogress; the contrary emphasis on discontinuity in

Kuhns (1970, 1962) account of paradigm change and

Schumpeters (1934) invocation of entrepreneurial

vision is misleading. Good continuity (Schlicht,

2000) is important, and Schlicht has drawn attention

to the importance of aesthetic factors in determining

what is good continuitywhich over a long period

may resemble Wittgensteins rope.

The development of an architecture of the brain

which facilitated the creation of neural networks


11/13


necessarily preceded the emergence of conscious

thought, which did not displace these networks of

unarticulated knowledge how. It is therefore neces-

sarily true that we know more than we can tell, andthat codification rests ultimately on tacit knowledge,

which is coded in our neural networks. Hayeks (1952)

account of the formation of our sensory order, noted

earlier, is a remarkable anticipation of this model of

evolutionary psychology. By the normal rules of bi-

ological evolution by exaptation, conscious thought

was similarly built upon connections rather than log-

ical processes; Hayek explains why the connections

of science may not correspond to the evidence of

our senses. An evolutionary sequence from connec-

tions between impressions and actions to connections

between ideas of impressions and actions, including

the imagination of possible connections, was con-

jectured in Alfred Marshalls (1994) early paper Ye

machine, which predates his interest in economics

but may have had a substantial influence on his un-

derstanding of economic processes (Raffaelli, 2001).

The ability to construct logical sequences is a rela-

tively recent and relatively weak development, almost

an artificial form of intelligence, and its effective-

ness depends on the prior creation of appropriate

categories, as has been repeatedlyand sometimes

spectacularlydemonstrated.

3. Biology, institutions and knowledge

The genetic capability of developing a particular

set of behaviours, out of a very large potential, by

selecting connections in response to perceptions of

phenomena, together with the emotional impulse to

develop such connections, is the biological precondi-

tion of modern economic systems; for if differences

of interest and situation lead members of a popula-tion to develop different parts of this potential, then

the capabilities of that population may far exceed

what even the most gifted individual can attain. The

fostering of differences in interest and situation by

both formal and informal organisation encourages

and shapes the development of these capabilities. The

division of labour is an evolutionary process; and it

is Smiths conception of the character of human cog-

nition that led to his recognition of its importance.

It has its own pathology, not least in technological

innovation; yet this combination of capabilities and

motivation has made possible a non-biological evolu-

tionary process that has operated much faster and en-

compassed unprecedented categories of applications.One may claim, with Cosmides and Tooby (1994),

that the mental capabilities that have resulted from

our biological evolution are better than rational for

coping with the range of problems that lie between

randomness and the economists concept of rational-

ity. These certainly include the problems of uncer-

tainty and imagination; indeed we may agree with

Rizzello (1999, p. xv) that [t]he economics of the

mind is the economics of creativity, uncertainty and

complexity.

Individual cognition is governed, though not de-

termined, by a dense network of rules and familiar

relationships, many of them partly or wholly tacit.

When these rules and relationships are shared within

a community, we call them institutions, and many of

the rules and relationships on which each of us relies

are indeed institutions in this sense; but their origin

as a class of phenomena lies not in the management

of interactions but in the requirements of effective in-

dividual cognition. Indeed this fundamental cognitive

requirement produces the possibility, as well as the

incentive, for developing the institutions that guide

interactions. The principles on which human brainsare organised gives some prospect of understanding at

least aspects of other peoples behaviour; and Heiner

(1983) has suggested that it was our dependence

on rules which could not be precisely adapted to

specific situations that made possible the interpre-

tation of other peoples behaviour without detailed

knowledge of their circumstances. Choi (1993) sub-

sequently drew on Smiths Theory of Moral Senti-

ments in arguing that this possibility of interpretation

allows us to conduct vicarious experiments by ob-

serving others and imitating apparently successfulperformance. This behaviour leads to shared rou-

tines and shared rules, even when actions are quite

independent. These shared routines and rules may

provide a natural basis for concerted action, and en-

courage the development of new routines to resolve

co-ordination problems: thus exaptation seems to be

a promising clue to the explanation of institutions in

the popular sense, and in particular to the general ac-

ceptance, usually tacit, of the idea that organisations

are incubators of institutions. Among the significant


12/13


organisations which depend on both individual and

shared routines and rules are research communities,

firms and markets. Without such institutions, eco-

nomic evolution as we have experienced it would notbe possible.

Acknowledgements

This paper has been developed from my response

to a group study of technological innovation (Ziman,

2000b) which endorsed both the value and the variety

of evolutionary reasoning, and provided ample evi-

dence for both endorsements. I strongly recommend it.

References

Alchian, A.A., 1950. Uncertainty, evolution and economic theory.

Journal of Political Economy 58, 211221.

Barnard, C.A., 1938. The Functions of the Executive. Harvard

University Press, Cambridge, MA.

Choi, Y.B., 1993. Paradigms and, Conventions: Uncertainty,

Decision-making and Entrepreneurship. University of Michigan

Press, Ann Arbor, MI.

Cosmides, L., Tooby, J., 1994. Better than rational: evolutionary

psychology and the invisible hand. American Economic Review

84, 327332.Frisch, O., 1979. What Little I Remember. Cambridge University

Press, Cambridge.

Hayek, F.A., 1952. The Sensory Order. University of Chicago

Press, Chicago.

Heiner, R.A., 1983. The origin of predictable behaviour. American

Economic Review 73, 560595.

Hume, D., 1978. In: Selby-Bigge, L.A. (Ed.), A Treatise on

Human Nature, 2nd Edition. Clarendon Press, Oxford (revised

by Nidditch, P.H.).

Kelly, G.A., 1963. A Theory of Personality: The Psychology of

Personal Constructs. W.W. Norton, New York.

Kirzner, I.M., 1973. Competition and Entrepreneurship. University

of Chicago Press, Chicago.

Knight, F.H., 1921. Risk, Uncertainty and Profit. Houghton Mifflin,

Boston.

Kuhn, T.S., 1962, 1970. The Structure of Scientific Revolutions,

1st and 2nd Editions. University of Chicago Press, Chicago.

Loasby, B.J., 1995. Acceptable explanations. In: Dow, S.C.,

Hillard, J. (Eds.), Keynes, Knowledge and Uncertainty. Edward

Elgar, Aldershot, pp. 624.

Loasby, B.J., 2001. Time, knowledge and evolutionary dynamics:

why connections matter. Journal of Evolutionary Economics

11, 393412.

Marshall, A., 1920. Principles of Economics, 8th Edition.

Macmillan, London.

Marshall, A., 1994. Ye machine. In: Research in the History of

Economic Thought and Methodology, Archival Supplement 4.

JAI Press, Greenwich, CT, pp. 116132.

Milne-Edwards, H., 1827. Nerf. In: Bory de Saint-Vincent, M.

(Ed.), Dictionnaire Classique de lHistoire Naturelle. Rey etGravier, Paris.

Nelson, R.R., Winter, S.G., 1982. An Evolutionary Theory of

Economic Change. Belknap Press, Cambridge, MA.

Nightingale, P., 2000. Economies of scale in experimentation:

knowledge and technology in pharmaceutical R&D. Industrial

and Corporate Change 9, 315359.

Pavitt, K., 1998. Technologies, products and organization in

the innovating firm: what Adam Smith tells us and Joseph

Schumpeter doesnt. Industrial and Corporate Change 7, 433

452.

Penrose, E.T., 1952. Biological analogies in the theory of the firm.

American Economic Review 42, 804819.

Penrose, E.T., 1959. The Theory of the Growth of the Firm, 3rdEdition. Oxford University Press, Oxford, 1995.

Pool, R., 1997. Beyond Engineering: How Society Shapes

Technology. Oxford University Press, New York.

Popper, K.R., 1963. Conjectures and Refutations. Routledge and

Kegan Paul, London.

Potts, J., 2000. The New Evolutionary Microeconomics: Comp-

lexity, Competence and Adaptive Behavior. Edward Elgar,

Cheltenham and Northampton, MA.

Raffaelli, T., 2001. Marshall on mind and society: neuro-

physiological models applied to industrial and business

organization. Journal of the History of Economic Thought 8,

208229.

Richardson, G.B., 1972. The organisation of industry. Economic

Journal 82, 883896.

Rizzello, S., 1999. The Economics of the Mind. Edward Elgar,

Cheltenham and Northampton, MA.

Ryle, G., 1949. The Concept of Mind. Hutchinson, London.

Schlicht, E., 2000. Aestheticism in the theory of custom. Journal

des Economistes et des Etudes Humaines 10, 3351.

Schumpeter, J.A., 1934. The Theory of Economic Development.

Harvard University Press, Cambridge, MA.

Shackle, G.L.S., 1972. Epistemics and Economics. Cambridge

University Press, Cambridge.

Shackle, G.L.S., 1979. Imagination and the Nature of Choice.

Edinburgh University Press, Edinburgh.

Singh, S., 1997. Fermats Last Theorem. Fourth Estate, London.

Smith, A., [1759] 1976a. The Theory of Moral Sentiments. In:

Raphael, D.D., Macfie, A.L. (Eds.). Oxford University Press,

Oxford.

Smith, A., [1776] 1976b. An Inquiry into the Nature and Causes

of the Wealth of Nations. In: Campbell, R.H., Skinner, A.S.,

Todd, W.B. (Eds.). Oxford University Press, Oxford.

Smith, A., [1795] 1980. The principles which lead and direct

philosophical enquiries: illustrated by the history of astronomy.

In: Wightman, W.P.D. (Ed.), Essays on Philosophical Subjects.

Oxford University Press, Oxford, pp. 33109.

Smith, A., 1983. Lectures on Rhetoric and Belles Lettres. In:

Bryce, J.C. (Ed.). Oxford University Press, Oxford.


13/13


Smith, J.M., 1996. Conclusion. In: Runciman, W.G., Smith, J.M.,

Dunbar, R.I.M. (Eds.), The Evolution of Social Behaviour

Patterns in Primates and Man. Oxford University Press, Oxford,

pp. 291297.

Vromen, J.J., 1995. Economic Evolution. Routledge, London.Young, A.A., 1928. Increasing returns and economic progress.

Economic Journal 38, 527542.

Ziman, J.M., 1978. Reliable Knowledge. Cambridge University

Press, Cambridge.

Ziman, J.M., 2000a. Real Science. Cambridge University Press,

Cambridge.

Ziman, J.M. (Ed.), 2000b. Technological Innovation as anEvolutionary Process. Cambridge University Press, Cambridge.

articulo loasby

Documents