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