THE MARSHALLIAN AND SCHUMPETERIAN MICROFOUNDATIONS OF EVOLUTIONARY COMPLEXITY. AN AGENT BASED SIMULATION MODEL1
Cristiano Antonelli, Dipartimento di Economia e Statistica “Cognetti de Martiis”, Università di Torino & BRICK, Collegio Carlo Alberto.
Gianluigi Ferraris, BRICK, Collegio Carlo Alberto.
1. Introduction
The identification and appreciation of the Marshallian foundations of evolutionary thinking in economics is necessary to identify and overcome the limits of biological evolutionary framework of analysis and to contribute the new emerging evolutionary complexity with a consistent microeconomics of endogenous innovation that implements the reappraisal of the Schumpeterian notion of “creative response” (Arthur, 2009; Kirman, 2016).
The Schumpeterian notion of ‘creative response’ received very little attention so far and yet is indispensable to go beyond the shortcomings of biological evolutionary approaches (Antonelli, 2015 and 2017). Indeed, Schumpeter (1947: 150) provided a founding framework to grasping the endogenous microeconomic determinants of innovations. Innovation is the result of a creative response to unexpected events conditional to the availability of substantial externalities. Schumpeter defines the creative response as “something that is outside of existing practice”, by highlighting three essential characteristics: “it cannot be predicted by applying the ordinary rules of inference from pre-existing facts”… “ (it) shapes the whole course of subsequent events and their long-run outcome”… “its intensity and success or failure has.. to do.. with the socio-economic context”. The notion of creative response –and its contrast with the adaptive one- enables to articulate an evolutionary complexity that puts the microeconomic determinants of the decision to innovate at the center of the analysis. In so doing, the reappraisal of the notion of creative response enables to articulate an endogenous model of the innovation process that can be enriched and implemented by the explicit identification and appreciation of the Marshallian legacy (Antonelli, 2008; 2011; 2017; Antonelli and Ferraris, 2011).
Specifically, the paper articulates the view that the analysis of the Marshallian and Schumpeterian microfoundations of endogenous innovation enables to elaborate a clear analytical separation between the evolutionary approaches based upon biological metaphors and the new emerging evolutionary complexity overcoming the limits of the former and contributing the latter. The integration of the Marshallian notions of imitation externalities and selection process with the Schumpeterian notion of creative as opposed to adaptive response, provide a rich and coherent microeconomic analysis of the determinants of the innovation process at the firm level that is able to take into account the effects of the system into which the individual decision making of heterogeneous agents takes place.
The rest of the paper is structured as it follows. Section 2 highlights the microeconomic limits of biological evolutionary approaches and calls attention on the lack of consistent microfoundations. Section 3 spells out the basic ingredients of the Marshallian-et-Schumpeterian frameworks of analysis and shows how their integration provides coherent microfoundations to the dynamics of evolutionary complexity. Section 4 presents an agent based simulation model (ABM) to test the coherence of the analysis. The conclusions summarize the main results of the paper and confirm the central role of the creative response in the introduction of innovation based upon the use of knowledge externalities as the basic mechanism of an effective and microfounded evolutionary dynamics able to move away from the ambiguities of biological evolutionary approaches.
2. The limits of biological evolutionary approaches
Different waves of evolutionary frameworks have been elaborated since the founding contribution of Thorstein Veblen (1898) focused on the role of heterogeneity of agents and institutions. After the decline of the second framework centered primarily by Armen Alchian (1950) on the role of uncertainty, evolutionary economics has been revived at the end of XX century by Richard Nelson and Sidney Winter with the grafting of biological metaphors. This (third) approach emphasizes the role of innovations and made crucial contributions to understanding industrial dynamics based upon the selective adoption and imitation of innovations. The biological evolutionary framework explores the effects of the exogenous introduction of a variety of innovations and their sequential and cumulative selection on the dynamics of market shares of firms and aggregate growth. The introduction of innovations, however, is assumed to be automatic and random. Biological evolutionary models pay very little attention to the endogenous determinants of innovation. The decision whether to innovate or not is poorly explored. The limits of this approach are becoming more and more evident. A new (fourth) generation of evolutionary economics is being implemented applying the tools of the economics of complexity that enable to focus the role of interactions among agents as the carriers of emerging system properties that include the introduction of innovations (Arthur, 2007). The analysis of the Marshallian and Schumpeterian legacies enables to identify the discontinuity between biological evolutionary approaches and the new emerging evolutionary complexity stressing at the same time their common origins and their radical diversity. 2.1 The incipit of the biological metaphor
The book by Nelson and Winter (1982) has been the basic reference for the evolutionary approach based upon biological metaphors. An Evolutionary Theory of Economic Change makes three important contributions: i) it place innovation at the center of the analysis; ii) it introduces the notion of routine to explain how firms change their conduct; iii) it highlights the central role of the selective adoption of innovations and their diffusion. The introduction of routines is a major contribution to organization theory and helps understanding how –large- firms change their strategies. Routines are based upon satisficing rather than optimization. As a matter of fact the first chapters of the book pay much attention to explaining how do firms change their routines, but never explain why would firms change them. In the rest of the book the analysis of the determinants of the introduction of innovations is considered but is quite confusing. The volume swings between two opposite views: A) the Lamarckian approach according to which firms innovate only when their performances fall below some thresholds2; B) the Darwinian approach where innovation takes place
as a random process3. Incumbents keep changing their routines and have occasionally the chance to
introduce actual innovations i.e. new superior technologies. Nelson and Winter (1982) never try to reconcile their opposite views4.
2 See Nelson and Winter (1982:211): “…we assume that if firms are sufficiently profitable they do not ‘searching’ at all. They simply attempt to preserve their existing routines, and are driven to consider alternatives only under the pressure of adversity. Their R&D activity should thus be conceived as representing an ad hoc organizational response rather than a continuing policy commitment. This satisficing assumption is a simple and extreme representation of the incentives affecting technical change at the firm level”. In the failure inducement hypothesis innovation is introduced only as a response to performances that fall below some satisficing levels. Firms with performances above the average, or simply in the average are not expected to innovate.
3 Nelson and Winter are very clear: “In the orthodox formulation, the decision rules are assumed to be profit-maximizing over a sharply defined opportunity set that is taken as a datum, the firms in the industry and the industry as a whole are assumed to be at equilibrium size, and innovation (if treated at all) is absorbed into the traditional framework rather than mechanically. In evolutionary theory, decision rules are viewed as a legacy from firm's past and hence appropriate, at best, to the range of circumstances in which the firm customarily finds itself, and are viewed as unresponsive, or inappropriate to novel situations or situations encountered irregularly. Firms are regarded as expanding or contracting in response to disequilibria, with no presumption that the industry is "near" equilibrium. Innovation is treated as stochastic and as variable across firms.” (Nelson and Winter, 1982: 165-166). 4 See Erixon (2016) for an articulated effort to reconcile the failure-induced hypothesis with an extended Darwinian
The evolutionary approach framed by Nelson and Winter (1982), beyond the awareness of the authors and the related literature, is much closer to the Marshallian legacy and to its direct grafting by the early Schumpeter into The Theory of Economic Development, rather than to the Schumpeterian dynamics forged by the contributions of 1928 and 1942 that lead to the Schumpeterian synthesis of 1947 and to the Neo-Schumpeterian literature (Scherer, 1986).
2.2 The biological evolutionary literature
The evolutionary literature that impinges upon the pathbreaking contribution of Nelson and Winter fully retains their second assumption that the introduction of innovations is a spontaneous and automatic process that is not characterized by intentionality and has no microeconomic foundation. According to this literature all agents are potential innovators that have no risk aversion. The homo oeconomicus of this literature is automatically an innovator.
The history friendly models elaborated by Malerba, Nelson, Orsenigo and Winter (2001) simply assume that some firms innovate: “At the beginning of our episode, the only available technology for computer designs is transistors. N firms engage in efforts to design a computer, using funds provided by "venture capitalists" to finance their R&D expenditures. Some firms succeed in achieving a computer that meets a positive demand and begin to sell. This way they first break into the mainframe market. Some other firms exhaust their capital endowment and fail. Firms with positive sales use their profits to pay back their initial debt, to invest in R&D and in marketing. With R&D activity firms acquire technological competencies and become able to design better computers. Different firms gain different market shares, according to their profits and their decision rules concerning pricing, R&D and advertising expenditure. Over time firms come closer to the technological frontier defined by transistor technology, and technical advance becomes slower.” (Malerba, Neslon, Orsenigo and Winter, 2001: 4-5). In history friendly models the microeconomic decision of whether to innovate or not is completely missing. Innovation is assumed as a given characteristic of the system.
The influential contributions of Iwai (1984 and 2000) make this point very clear: the analysis moves from the assumption that an innovation has been introduced. The analysis does not explore who, why, when and where did try to innovate. His analysis of the characteristics of the selective diffusion of many competing technologies remains one of the key contributions of the biological evolutionary literature.
The inclusive review of the evolutionary literature of Safarzyńska and van den Bergh (2010: 347) concludes that: “Although innovations are intrinsically uncertain, and for this reason in most evolutionary-economic models treated as stochastic, it would be incorrect to consider the process of innovation as totally random. Innovations may be expected to occur in a systematic manner, namely preceded by the cumulativeness of relevant technical advances. The innovative process is often depicted as following relatively ordered technological path- ways, as is reflected by notions such as natural trajectories (Nelson and Winter 1977), technological guide points (Sahal, 1985), technological paradigms (Dosi 1982), and socio-technological regimes (Geels 2002, 2005). Innovations are conceptualized in formal models in a number of ways: as a stochastic process (e.g., Poisson) that can result in structural discontinuity, variation and recombination of existing technological options, or random or myopic search on a fitness (technology) landscape. Innovations may be associated with a new vintage of capital (e.g., Iwai1984a, b; Silverberg and Lehnert 1993; Silverberg and Verspagen 1994a, b, 1995). “
The models of industrial dynamics that impinge upon the basic contribution of Dosi, Marsili, approach.
Orsenigo and Salvatore (1995) assume that innovations are determined by technological opportunities, but no analysis is provided about the specific characteristics of the decision process at the firm level: all firms are expected to innovate when, where and if technological opportunities are large. The determinants of technological opportunities are missing: as such they must be regarded as exogenous. The important contribution by Winter, Kaniovski, and Dosi (2000) explores an alternative route: innovation is the direct and automatic consequence of learning. Learning processes are deemed to engender the accumulation of technological knowledge and the eventual introduction of innovations. The introduction of innovations is simply the consequence of learning processes: as such they take place at all times, in all conditions, in all locations. There is no variety in these models with respect to the innovation process: all firms do equally learn and do equally innovate. The possibility that some firms learn and innovate (more) and (than) others do not is not taken into account.
Windrum and Birchenhall (2005) provide the basic reference for the analysis of the models of selective adoption and implementation. Windrum and Birchenhall (2005) highlight the role of network externalities in the selection of alternative –given- technological innovations. For a given set of potential technologies, network externalities play a critical role in sorting out those that have stronger chances of further development and implementation. Once more the analysis does not take into account the determinants of the process by means of which agents did try to introduce each of the many alternative innovations. The variety of possible technological innovations is assumed but not explained.
As Dawid (2006) shows in his comprehensive review of the biological evolutionary models of innovation and technological change that impinge upon the agent-based approach, the decision to innovate is little explored: the focus of the analytical exploration is concentrated on the characteristics of the selective diffusion process, rather than on the determinants of the innovation process.
3.3 Away from biology, back to Schumpeter: towards evolutionary complexity.
The microeconomic limits of the approach that impinges upon biological metaphors are becoming more and more evident. The empirical evidence documents, in fact, the large variance among firms in terms of rates of introduction of innovations as proxied by R&D expenditures, patents, total factor productivity levels, innovation counts, as well as rates of growth and strategies. This unexplained variance calls for an effort to build consistent microfoundations of endogenous innovations. To do so, it seems necessary to move away from biological metaphors,
Routines can be regarded as an attempt to elaborate an economic equivalent of the biological genotype, but the actual causes of the changes of phenotypes and their effects on the characteristics of the genotypes are never elaborated. The detailed exposition of the procedures by means of which firms change their routines, in fact, contributes understanding how do –large- firms implement their changing strategies, but does not provide a clear hint about the motivations that induce to change them. Their application to small firms, moreover, seems rather difficult. Routines apply to understanding how do corporations change their behavior, not why. Biological evolutionary theorizing seems to be trapped by its ambiguity between the Lamarckian metaphors where innovation is failure-induced – the changes in the phenotypes do affect the changes in genotype, but only when performances are not satisfactory- and Darwinian ones where, as a matter of fact, variation is fully random and exogenous: genotypes cannot be changed intentionally. This second view is fully consistent with to the Darwinian legacy retained by the evolutionary literature of the last decades of the XX century: the changes in genotype take place by chance. The characteristics of the new species do not reflect the purposes of their relatives. Their selection, instead, is endogenous as it enables to identify, out of the many variations, those that fit better into the
environment. The grafting of the biological metaphor into economics prevents the understanding of the determinants of the introduction of innovations at the firm level (Levit, Hossfeld, Witt, 2011). As Edith Penrose (1952 and 1953) had already remarked more than thirty years before the publication of An Evolutionary Theory of Economic Change: “To abandon their (firm’s) development to the laws of nature diverts attention from the importance of human decision and motives, and from problem of ethics and public policy, and surrounds the whole question of the growth of the firm with an aura of naturalness and inevitability“ (Penrose, 1952:809).
The growing concern about the missing microfoundations of the biological evolutionary approach has been the main determinant of its progressive demise and of the attempts to implementing the new emerging framework provided by complexity economics. The identification of the central role of feedbacks engendered by interactions among heterogeneous learning agents credited with the capability to innovate and of the context into which such interactions take place lies at the hart of the new emerging complexity economics (Kirman, 1997and 2011; Arthur, 1989 and 2007).
The innovative model of Yildizoglu (2002) based upon the application of genetic algorithms can be regarded as the starting point of the new approach because of its important merits: i) it stresses the limits of the biological trap where decision making about innovation is automatic and is not sensitive to the changing conditions of the environment: ” Firms invest a fraction rdjt of their gross
profits on R&D. A minimal investment is necessary to keep alive the R&D potential (research equipment and team)…… Learning of firms about their environment does not influence their R&D behaviour. This is the common approach retained in many evolutionary industry models” (Yildizoglu (2002:55); and ii) it articulates the basic argument of evolutionary complexity where interactions among agents play the central role. To overcome the limits of the “common approach retained in many evolutionary industry models” 5 Yildizoglu introduces an individual genetic
algorithm in order to adjust the R&D strategy of firms to the changing conditions of the industry. In so doing he clearly overcomes the foundations of biological evolutionary models and paves the way to a reappraisal of the Schumpeterian approach based upon the notion of interactions and hence externalities. The analysis of firms strategies where the introduction of innovations is a strategic tool conditional to the characteristics of the environment (Bischi, Dawid, Kopel, 2003) adds innovative elements that enable to move away from biological evolutionary approach towards an evolutionary complexity.
Recent evolutionary theorizing in a radical departure from the biological evolutionary approach abandons the hypothesis that innovation efforts are random and exogenous and explores the micro determinants of the innovation process stressing the role of consumers’ preferences and exploring the microeconomic effects of the changes in the aggregate levels of demand implementing a microeconomic demand pull approach (Aoki and Yoshikawa, 2002). Along these lines Napoletano et al. (2012) introduce the hypothesis that firms change their strategies according to the levels of profits that in turn are influenced by the dynamics of aggregate demand. Antonelli and Scellato (2011) test the hypothesis that firms try and innovate when they are found in out-of-equilibrium conditions: when they enjoy profits risk aversion and financial constraints are low and firms are more likely to try and innovate; when firms face losses, innovation is the single way to avoid exit. Lorentz et al. (2016) model the effects of changes of consumption preferences that are engendered by the changing distribution of income on the structural and technological change. According to Antonelli and Scellato (2013) social interaction is a specific form of interdependence whereby the changes in the behavior of other agents affect the structure of the utility functions for households and of the production functions for producers. They graft the methodology of social interactions to the knowledge externalities literature and the Schumpeterian notion of creative reaction to 5 Italics added.
understanding innovation and technological change within the new evolutionary approach that builds on complex dynamics.
It seems more and more necessary to contribute the new evolutionary complexity with an explicit analysis, at the firm level, of the determinants of innovation and the role of externalities in the decision process that leads to their –possible- introduction (Arthur, 2007; Pyka and Fagiolo, 2007). In the new emerging evolutionary complexity the innovative strategies of firms are at the same time sensitive to the contextual conditions of product and factor markets and play a central role to understanding why do firm innovate: i.e. both a consequence and a cause of system dynamics. The changing characteristics of the context into which decision-making takes place are the complementary and indispensable variables. Agent based decision making that includes creativity and externalities are the two basic tools -provided respectively by Joseph Schumpeter and Alfred Marshall- that enable to articulate the shift away from the biological evolutionary approach and reintegrate in an evolutionary complexity major chapters of the economics of endogenous technological change ranging from the Hicksian induced technological change approach to the Kaldorian demand pull and the Schumpeterian oligopolistic rivalry, that biological evolutionary approaches had abandoned (Ruttan, 1997; Dosi, 1997).
The appreciation of the Marshallian roots enables to focus the attention on the role of imitation externalities not only in the selective adoption of new technologies but also and primarily in their introduction and the reappraisal of the Schumpeterian dynamics where innovation is not the outcome of a random process but the result of the creative response of firms and their intentional pursuit of new technologies made possible by the properties of the system into which the process takes place, provides key inputs to implementing the new evolutionary complexity. The analysis of the Marshallian origins of the Schumpeterian approaches enables to understand at the same time their complementarities and yet their diversities. This in turn makes it possible to draw a clear line between the evolutionary approaches that impinge upon biological metaphors and the emerging evolutionary complexity (Caldari, 2015).
3. From the Marshallian search for equilibrium to the Schumpeterian dynamics: The basic role of externalities
In the essay in honor of Alfred Marshall Schumpeter (1941) acknowledges the many contributions of the Marshallian legacy to his own understanding of the role of the selective competition among heterogeneous firms. The Marshallian approach has been a fundamental and constant source of inspiration for Joseph Schumpeter from The theory of economic development to the The creative response in economic history6 The Marshallian approach, in fact, can be regarded as the basic foundation not only of the early contributions but also and primarily of the 1947 attempt to provide an endogenous understanding of the innovation process able to integrate the analysis at the firm level with the appreciation of the role of externalities embedded in the system.
The Marshallian model rests on three building blocks: i) exogenous innovations; ii) no appropriability, and iii) imitation externalities. Let us consider them in turn. In the Marshallian framework innovations are the starting point. For unknown reasons they are introduced occasionally and randomly. Their exogenous introduction puts –and keeps- the system in motion. According to Marshall, knowledge cannot be appropriated by inventors and spills freely like information so that everybody is immediately aware of the details of the best practice. The perfect access to the best knowledge at each point in time is a key aspect of the notion of ‘normal’ cost: “But though everyone acts for himself, his knowledge of what others are doing is supposed to be generally sufficient to prevent him from taking a lower or paying a higher price than others are doing.” 6 See the contributions of Stan Metcalfe (2007a and b, 2009a, 2009b) that have highlighted the Marshallian
(Marshall, 1920, V, 3, p.199). As a matter of fact Marshall introduced the notions of limited appropriability and spillover, well before Arrow (1962) and Griliches (1979). The imitation of exogenous innovations introduced randomly is the focus of the analysis and the engine of the dynamics both in Marshall and biological evolutionary models. Marshall considers two types of externalities: agglomeration externalities and imitation externalities. Agglomeration externalities have received much attention while imitation externalities did not. Yet imitation externalities are at the heart of the Marshallian dynamics that leads from variety to homogeneity by means of selection and from out-of-equilibrium to equilibrium. Marshall assumes that firms are heterogeneous: some firms perform better than others. Selective competition drives the system to generalize the competence of the most performing firms. In Marshall equilibrium is the result of a competitive process that reduces heterogeneity to homogeneity7. Exogenous and random innovations and
consequently the variety of firms are the cause of the Marshallian imitation externalities. Externalities and variety decline together, along a competition process -intertwined with a selection process- that accounts for the growth of the system but reduces variety and consequently destroys the very origin of externalities. They display their effects along with the selection process and the reduction of heterogeneity to homogeneity. Marshallian imitation externalities are endogenous to the system and intrinsic to the Marshallian search for equilibrium. As such, however, they are bounded.
Marshall assumes that a variety of firms try and produce, enter and exit the market place with different levels of productivity and costs. At each point in time firms are confronted with partial equilibrium that unveils their heterogeneity in terms of production costs. Less efficient firms are sorted out while more efficient ones can enjoy the benefits of transient rents and increase their size. In the Marshallian process, new entrants and less performing incumbents, however, can imitate freely the most performing ones. The efficiency of most performing firms spills freely in the system and can be accessed and shared by any other agent8. The imitative entry of new competitors and the
imitation of incumbents affect the shifting position of the supply curve that engenders a sequence of lower market prices and larger quantities. The variance of profitability levels shrinks. In the long term the process leads to the eventual identification of the equilibrium price according to which only most efficient firms can survive with normal profits. The identification of a stable equilibrium stops the endogenous generation of externalities. In equilibrium there is no growth. Growth lasts as long as the selection&imitation process that enables to push the allocation of inputs towards their most effective use is in place. Marshallian externalities are endogenous, but bounded.
The influence of Marshall on The theory of economic development is clear. The role of 7 See Metcalfe (2007a:10): “In a famous passage Marshall claims that the tendency to variation is the chief source of progress (Marshall, 1920, V, 4, p. 355). This telling phrase captures in a single step the deep evolutionary content of Marshall’s thought but “What is meant by this?” The rest of the Principles make clear that variation and progress are connected by a variation cum selection dynamic, Marshall’s principle of substitution in which more profitable firms prosper at the expense of weaker brethren. Outcomes are tested in the market so that “society substitutes one undertaker for another who is less efficient in proportion to his charges” (Marshall, 1920, V, 3, p.341). Indeed, in introducing a discussion of profit in relation to business ability, Marshall is quite explicit that this principle of substitution is a “special and limited application of the law of “the survival of the fittest” (Marshall, 1920, VI, 7, p. 597). Furthermore, innovation is inseparable from the competitive process. For the advantages of economic freedom “are never more strikingly manifest than when a business man endowed with genius is trying experiments, at his own risk, to see whether some new method or combination of old methods, will be more efficient than the old” (Marshall, 1920, V, 8, p. 406). The relation runs two ways and mutually reinforces the links between free competition and business experimentation.”
8 See Ravix (2012:53): “In Marshall, entry and exit appears in different contexts. For instance, economic change leads
to the distinction between ‘those who open out new and improved methods of business, and those who follow beaten tracks (Marshall, 1920, VI, VII, 1, 496)”.
entrepreneurship is a first attempt to fill the Marshallian gap about the origin of innovations. Schumpeter (1911-1934) however does not really provide an endogenous account of the origin and determinants of entrepreneurship. It remains unclear whether the flows of innovations introduced by entrepreneurs and their entry are steady through time and space, or do exhibit relevant and systematic changes. As a matter of fact the evolutionary models that impinge upon Nelson and Winter (1982) are intrinsically Marshallian and are consistent with the legacy of Schumpeter (1911-1934) where, following Marshall, innovations are exogenous as they are introduced by entrepreneurs that enter the economic system from outside without any economic causality, rather than the frame elaborated by Schumpeter with the 1928 and 1942 contributions and their greats synthesis of 1947.9
The Marshallian dynamics of imitation externalities provides the foundations for the path-breaking contribution of Schumpeter (1947). This frame can be regarded as a full-fledged evolutionary process based upon the notion of endogenous innovation as the outcome of a creative reaction able to reshape the existing map of isoquants that takes place in out-of-equilibrium conditions when firms’ plans do not meet the actual product and factor market conditions, provided the system is able to support their reaction with the provision of knowledge externalities10. If knowledge
externalities are not available the response of firms will be adaptive and consists only in the traditional movements on the existing map of isoquants.
The Schumpeterian dynamics elaborated in the 1947 essay differs from the Marshallian one for two 9 Careful reading of the celebrated notion of “forest trees” introduced by Marshall (1920) is useful to support the hypothesis that young trees are the carriers of innovations that account for the growth of the system and the continual reproduction of out-of-equilibrium conditions: “We saw how these latter economies are liable to constant fluctuations so far as any particular house is concerned. An able man, assisted perhaps by some strokes of good fortune, gets a firm footing in the trade, he works hard and lives sparely, his own capital grows fast, and the credit that enables him to borrow more capital grows still faster; he collects around him subordinates of more than ordinary zeal and ability; as his business increases they rise with him, they trust him and he trusts them, each of them devotes himself with energy to just that work for which he is specially fitted, so that no high ability is wasted on easy work, and no difficult work is entrusted to unskillful hands. Corresponding to this steadily increasing economy of skill, the growth of his business brings with it similar economies of specialized machines and plant of all kinds; every improved process is quickly adopted and made the basis of further improvements; success brings credit and credit brings success; credit and success help to retain old customers and to bring new ones; the increase of his trade gives him great advantages in buying; his goods advertise one another, and thus diminish his difficulty in finding a vent for them. The increase in the scale of his business increases rapidly the advantages which he has over his competitors, and lowers the price at which he can afford to sell. This process may go on as long as his energy and enterprise, his inventive and organizing power retain their full strength and freshness, and so long as the risks which are inseparable from business do not cause him exceptional losses; and if it could endure for a hundred years, he and one or two others like him would divide between them the whole of that branch of industry in which he is engaged. The large scale of their production would put great economies within their reach; and provided they competed to their utmost with one another, the public would derive the chief benefit of these economies, and the price of the commodity would fall very low. (Book IV. XIII. 3). But here we may read a lesson from the young trees of the forest as they struggle upwards through the benumbing shade of their older rivals. Many succumb on the way, and a few only survive; those few become stronger with every year, they get a larger share of light and air with every increase of their height, and at last in their turn they tower above their neighbours, and seem as though they would grow on for ever, and for ever become stronger as they grow. But they do not. One tree will last longer in full vigour and attain a greater size than another; but sooner or later age tells on them all. Though the taller ones have a better access to light and air than their rivals, they gradually lose vitality; and one after another they give place to others, which, though of less material strength, have on their side the vigour of youth”. (Book IV. XIII. 4). The Theory of Economic Development can now be read as the evident grafting of the Marshallian intuition about the role of entrepreneurs as the vehicles of innovation and growth.
10 As a matter of fact Schumpeter had already overcome, the limits of the exogenous role of entrepreneurship not only in the 1928 essay, but also and more consistently in Business cycles (1939) where the cause/effect relationship between the phases of the economic cycle and the flows of innovations is investigated in depth, at least at the aggregate level..
key reasons: i) in Schumpeter, externalities are knowledge externalities rather than imitation externalities. Knowledge externalities make it possible to every firm to introduce productivity enhancing innovations that keep the system in a cost-reducing process further reinforced by the increased levels of generation of new technological knowledge that is able to reinforce the further creation of endogenous knowledge externalities; ii) in Schumpeter the creative reaction of firms supported by the self-sustained dynamics of knowledge externalities enables the introduction of innovations that are by definition the cause of unexpected changes in product and factor markets. Marshallian agents can imitate only from advanced firms. Advanced firms cannot take advantage of their transient competitive advantage to introduce new innovations. Schumpeterian agents, on the opposite, exhibit the distinctive characters of entrepreneurship that enable them to try and react both to bad and good performances. In both cases, in fact, they will try and introduce innovations either to contrast their decline and eventual exit or to take advantage of their competitive advantage and increase it with the introduction of new technologies. The levels of the actual reactivity of firms and of the quality of knowledge externalities provided by the system are the key variables of the Schumpeterian approach that enable to account for endogenous growth of output and productivity (Antonelli and Scellato, 2011; Erixon, 2016). Both are the results of implementation of the Marshallian framework. The identification of the Marshallian legacy enables to better appreciate the strength of the late contribution by Schumpeter.
4. The simulation11
The typical bottom-up approach of interactions nested in a systemic context of ABM provides an excellent tool for theoretical investigations. This use of ABM, next to its traditional application to forecasting, seems to open an innovative field of investigation to validate the robustness and consistency of theoretical hypotheses (Pyka and Fagiolo, 2007; Mueller and Pyka, 2016). ABM seems most appropriate to show how the implementation of a microfounded evolutionary complexity that integrates the legacies of Marshall and the late Schumpeter (1947) is able to overcome the microeconomic limits of the biological evolutionary framework with an endogenous account of the innovation process. By setting appropriate values for the key simulation’s parameters (imitation externalities, knowledge externalities, knowledge governance, and reactivity), the ABM, in fact, enables to compare the alternative bottom up system dynamics: the “Marshallian” and the full range of “Schumpeterian” ones determined by the varying levels of reactivity and knowledge governance.
The ABM used to compute the simulations reproduces a stylized economy where a variety of firms produce a unique output, useful both for investment and consumption. The simulated economy is closed - no import neither export activities are allowed - and systematically reach a state of local equilibrium – the whole production is sold, firms fully redistribute profits as well as shareholder immediately contribute to cover losses. No form of accumulation either in savings or equity is allowed. The levels of capitalization of firms are given and are maintained by the shareholders through immediate contributions to cover potential losses. Profits are always fully distributed, do not eat/add capital funds of any kind.
Wages (w) are constant and there is an unlimited supply of labor. The cost of labor per unit is set by a simulation parameter. Provided that new technological knowledge is produced by employing labor, all the costs the firms afford produced a monetary transfer to the workers. The utility and demand functions of consumers, employees, shareholders are not simulated explicitly: their behavior is summarized in the price equations of the goods. Output prices are set by a market maker that ensures that the whole –fixed- amount of money is totally allocated to consumption.
Because all of remuneration (dividends, wages, contribution to research activities) will be 11 See Antonelli and Ferraris (2011, 2017) for complementary specifications of the basic ABM presented in this paper.
immediately used to buy all the goods produced and money circulates twice for each production cycle, the demand for goods, in value terms, is equal to the amount of money in the system. The velocity of circulation of money (per cycle) is equal to 2 (all the money is paid in the form of remuneration of workers, researchers and shareholders (either as positive amount in case of profits or negative one in case of losses). During the same cycle all the money is spent to buy goods. At the aggregate level the model can be resumed as:
1) Gs = ΣYi.
Where Gs represents the aggregate supply, and Y are the individual revenues.
2) Gd = Gs.
Where Gd represents the demand of goods, both for investment or consumption.
3) Gp = M / Gs.
Where Gp represents the price of a single unit of production, and M the whole amount of money that circulates into the system. Note that the whole amount of M is always available for consumption: when enterprises are subject to losses, the aggregate expense for salaries is greater than the value of the production, so shareholder receive negative profits and vice versa: at the aggregate level the amount of money available for buying the productions sticks always to M. The aggregate production function could be expressed as:
4) Y = (A)L.
Where A is the productivity and L the amount of labor the enterprises employ.
The productivity reflects both agglomeration effects and the purchase of knowledge as it may be influenced by the current amount of technological knowledge (T) that the firm is able to mobilize and by a small fraction (g) of the amount of technological knowledge mobilized in the past production cycles, as it follows:
5) A = ∑ [Li* (ai * (1+ ti0+ g * ∑ti-j)] / ∑ Li.
Equation 5 makes explicit the work of knowledge externalities: Li represents the input the i-th
enterprise employs, ai represents its labour productivity, ti represents the level of technological
knowledge this enterprise has just bought and ∑ti-j the sum of the past technological knowledge
acquisition, weighted by a decay parameter g (the small fraction the enterprises permanently acquire in their knowledge estate for each technological knowledge acquisition). New technological knowledge acquisition is a risky activity, so its effects on productivity levels take place with a risk coefficient (R): each new technological knowledge acquisition (ti0) could fail and in that case the
productivity of the single enterprise becomes:
6) Ai = (ai * (1 + g * ∑ti-j).
Under the Marshallian restriction, the contribution of technological knowledge is zeroed. As a consequence the productivity is limited and it reaches a maximum that is given by the equilibrium level output in the system where all firms use the best technology. The productivity equation becomes:
7) A = ∑ Li* ai / ∑ Li.
Each period ai canbe upgraded by a fraction of the difference between its level and the productivity
level of the best enterprise, due to imitation effects. This upgrade is subject to risk, too, in this way:
8) Ai1 = ai0 + h (amax – ai0) | E >= R.
Where amax is the productivity of the best in class enterprise, E is a random number tossed from a uniform distribution, and R measures the probability the imitation fails. When E < R the productivity level of the enterprise remains at the latest reached level.
9) TC = wL + zT.
where L is the single input labor and w are the unit wages, as well as z is the costs of a technological knowledge unit and T is the amount of employed technological knowledge.
Note that in equation 9 the effect of T on productivity is usually such that Y/T=c>z where c is the cost of technological knowledge if it were a standard good while z is the actual market costs of technological knowledge. There is an equilibrium level c of knowledge costs that reflects the equilibrium conditions for its generation. If technological knowledge were a standard economic good its cost c would be equal to w, the cost of labor. The case for adaptive reaction takes place when the technological knowledge is acquired at its equilibrium cost c. The use of T will allow them to introduce novelties without direct economic effects on output levels that are higher than the total cost of the technological knowledge acquired. There is no chance for firms to introduce innovations that enhance output beyond the levels of the costs incurred to purchase the technological knowledge. When z=c the value of the technological knowledge T matches the equilibrium value of its marginal product12.
If positive pecuniary knowledge externalities are at work the reaction applies successfully and becomes creative. This amounts to assume that when the cost z of technological knowledge (z<c) falls below equilibrium levels, technological knowledge can be treated as a factor that enhances output beyond its costs. In this case, total factor productivity increases because of the discrepancy between the equilibrium levels of technological knowledge costs c and its actual -lower- levels z. The economic effects of technological knowledge purchased at a cost z that is lower than the equilibrium cost c, consist in the positive outcome of the reaction: the creative reaction takes place exactly in these circumstances. Firms can enjoy an ‘unpaid’ increase of the productivity levels that is equal to the levels of pecuniary knowledge externalities i.e. the difference between the equilibrium levels of the technological knowledge costs and their actual levels as determined by the working of pecuniary knowledge externalities. When, instead, the actual costs of knowledge are in “equilibrium” the reaction of firms will be adaptive.
The stylized economy configuration depends on a wide set of parameters. The model allows different setups to compare the simulation outcomes of different theoretical frameworks. For each simulation some parameters have a key role and vary to configure different simulation scenarios, whereas the other ones are usually set at fixed values. The configuration of the economy used for the simulations was based on: i) the presence of one thousand agents, ii) the availability of 10,000 unit of money for the whole transactions, iii) a fixed labor price – 1 unit of money – and an infinite labor offer, iv) out of business enterprises were replaced by new entrants with 20% probability, whereas enterprises went out of business when their demand for factor became less than one unit. In order to set up an initial variety of agents, the employed labor and the productivity at the start of the 12 The recent advances of the economic of knowledge enable to substantiate the dynamics of knowledge costs. Technological knowledge as an economic good is characterized not only by limited appropriability, but also by non-exhaustibility and non-divisibility (Arrow, 1962 and 1969). Technological knowledge moreover has the unique characteristic to be at the same time the output of a dedicated process and a necessary, indispensable input into the generation of new knowledge as well as into the production of all other goods (David, 1993). Finally, the generation of technological knowledge is a recombinant process characterized by the central role of the stock of existing knowledge, both internal and external to each learning agent (Weitzman, 1996; Fleming, 2001; Sorenson, Rivkin, Fleming, 2006). The understanding of the unique characteristics of technological knowledge as an economic good and the features of its generation process enables to better grasp the dynamics of knowledge externalities. Each learning agent –not only least performing firms but also most advanced ones- can actually benefit from the spillovers of the knowledge generation processes at work in the system (Griliches, 1979 and 1984). The actual access conditions to knowledge generated at each point in time and hence the mechanisms governing its dissemination are crucial to make persistent the working of pecuniary knowledge externalities (Antonelli and Ferraris, 2011, 2017).
simulation have been tossed randomly; the labor was allowed to vary in the range ]1,10[ and the productivity in the range ]0,0.2[. Some agents have been endowed with higher values, respectively labor was tossed in the range ]10,20[ and productivity in the range ]0.2,1.0[, the number of such “smarter” agents was set to the 15% of the one thousand agents that populate the economy. Agents were endowed the capability to both adapt and react. Adaptation has been simulated by setting up the agents to increase or decrease the amount of the employed factor of 10%, respectively if the previous production cycle ended with a profit or a loss. In computing their results (either profits or losses) agents rounded the amount with a tolerance of 0.001 due to computation matters.
Externalities have been simulated as productivity enhancement subject to a failure risk of 10%; technological knowledge had set as suitable for one production cycle only, except for a small fraction (0,1% in the simulations) that is added to the knowledge estate of the firms to mimic the learning process that always takes places both at the workers level and organizational level during the innovation exploitation. In fact the contribution of new technological knowledge is immediately subject to a decay of 99,9% (the parameter is named “techDecay”). The Marshallian imitation externalities have been simulated simply by granting each cycle each agent a labor productivity increase of 1% (the value is set by the parameter “imitation”) the difference between their own productivity and the one of the “smartest” agents, i.e. the firms that as the highest productivity in the whole simulated economy. As mentioned this upgrade may fail: the probability of success was set at 90%. The Schumpeterian scenario was based on the possibility for the agents to buy technological knowledge instead of receiving labor productivity upgrade – the parameter “imitation” was set to zero. The amount of technological knowledge each agent buys in each production cycle, depended on two key parameters: i) “techRate” that measures the reactivity of firms as the percentage of the total output each agent would invest, and ii) “governancePerformance” that measures the quality of the knowledge governance by means of the discount factor applied for the price of technological knowledge, as in equation 10:
10)z = ∂Y/∂T * (1 – governancePerformance).
Note that if governancePerformance is set to zero, the cost of technological knowledge becomes equal to its marginal contribution and no knowledge externalities are available in the system, in this way the behavior of the agents cannot be reactive, they can only adapt their factor allocation. The computation of ∂Y/∂T has been based upon the forecast of the production each agent is planned to obtain at the end of the production cycle, in order to forecast the level of price the produced good will be sold and set up a plausible base to compute the productivity of the technological knowledge in monetary terms to set up a plausible base to compute z. More details are available in Annex A11.
The amount each agent invests is subject to financial constraints: because, intentionally, no financial institution has been included into the model, the whole investment amount has to be covered either by profits or through savings obtained by reducing the input of factors. The amount an agent invests can be expressed as:
11)I = min (Y-1 * techRate, profit) | profit > 0; or as:
12)I = min (Y-1 * techRate, Labor-1 * 0,1) | profit < 0;
Where I represents the investment the i-th firm is going to afford in the production cycle 0. Labor and Y stand, respectively, for the labor the i-th agent employed in the previous production cycle and the output of the i-th agent in the previous production cycle. Schumpeterian agents are credited with the capability to react to out-of-equilibrium conditions: they invest and purchase new knowledge both when they enjoy extraprofits (Eq. 11) or face losses (Eq.12)13.
13 Agents that face losses reduce their input by a parametrical amount that was set to 0,1 in all the simulations
Table 1 exhibits the six different simulations scenarios generated by alternative values of the three key parameters: i) imitation, ii) techRate, iii) governancePerformance. The single scenario devoted to Marshallian externalities is the first one, named “Imitation”, the second, named “Zero Gov” has been run to demonstrate the results achieved with no knowledge governance are close to them of the Marshallian scenario. The others explore the different combinations between high/low reactivity (techRate) and high/poor knowledge governance (GovernancePerformance). The parameter: i) “techLife” - that indicates how many production cycles, after the current one, the benefits of a technological knowledge acquisition lasts – has always been set to 0, and ii) “techDecay” - that measures the fraction of the acquired technological knowledge that is lost after its usage – has always been se to 0.999; practically each knowledge acquisition gave productivity advantage only for one production cycle, with the exception of the 0.1% of the acquired technological knowledge that became a consolidated component of the knowledge of the enterprise.
Table 1 – Simulation scenarios
The simulation process consists in repeating a sequence of actions, managed through a precise schedule to control the information level of the agents and compute some statistics and aggregate figures. Before starting the simulations the model is charged to:
create the planned number of agents and assign each of them different size and productivity; create a random generator for each agent and assign one to each, in order to avoid indirect
and uncontrolled influence among agents;
create the market maker object, that will manage exchanges and set prices, either for goods, factor and knowledge;
initializing a common variable, called theWatch used by agents and marketMaker to synchronize their actions.
Each simulation cycle mimics a whole production cycle as shown by the flow chart in Appendix B14, that illustrate the sequence of order the model gives the agents and other components each
simulation cycle. To avoid single pseudo random distribution that could pollute the results, a large number of simulations have been run for each scenario and results have been resumed as average values, paying attention to their variance that was not significant. The evolution of the simulated economy has been studied by means of the trend of the global output and productivity and by 14 The set of Appendices is available on request.
computing concentration indexes: i) sum of the relative contribution to the global output by the three larger firms (GB method), ii) sum of the contribution of the four larger firms (US method) and iii) Hall and Tideman aggregation index.
Tables 2, 3 and 4 resume the results obtained during one hundred simulations, two thousand production cycles long, by reporting the average values of the one hundred results, for each scenario.
Table 2 – Productivity and output obtained during the simulations of the different scenarios
All the simulations have been executed under the same parameter configuration but with different random distribution, the random seed has been set randomly for each run. The first evidence that come out of the figures is that the results are not depending on the random distributions, due to the fact the variance figures are negligible and confirm the results are robust and due to the endogenous dynamic the model is able to mimic.
Productivity and output values show that the Marshallian scenarios were systematically able to reach the highest productivity value the system was initially endowed with. In each of the one hundred simulations, under Marshallian dynamic, the economy reached a stable equilibrium. The results of the Schumpeterian scenarios demonstrate the role played by the knowledge governance: with no governance at all or with poor governance, either with high or low reactivity, the economy had a slow growth achieving final figures close to those obtained under Marshallian simulation. No imitation was allowed in the Schumpeterian scenarios. Thus result confirms the importance of the quality of knowledge externalities. When knowledge governance is effective, externalities arise and both total output and productivity reach higher levels. The compound effect of good knowledge governance and high reactivity is able to push the system to overcome the initial limitation by achieving a productivity level that is 7 times as large the highest level the best firm, as endowed at the start of the simulation. As Tables 3 and 4, and Figure 1 confirm, strong knowledge externalities and high levels of reactivity lead not only to fast growth, but also to higher concentration, configuring product markets with a few large firms and many small competitors. The results of the High Gov High React scenario shows that more than 90% of the output is done by 4 firms (CR-USA value) and the 3 big ones cover the 89% of the production (GR-GB value), providing that they were able to spend a larger amount in investing to buy technological knowledge. Their higher
productivity parallels a strong increase of total output. Table 3 – Concentration
Figure 1 – Comparison of the trend of production between Marshallian and Schumpeterian scenarios.
In the Schumpeterian scenario the system is always in evolution, with a growth that, in the presented case, is 10 times larger with respect to the results obtained by the Marshallian hypothesis, where the output reached a stable level when the system went to equilibrium. The Schumpeterian dynamic implies the impossibility to reach a stable equilibrium, with cycles of growth and decline, and a total output that oscillates with a trend to grow: the interpolation of the output level could be
represented by the simple linear function (the straight line called “linear” in the graph) in equation 13, the interpolation is quite good, the r-square value is: R2=0.9005
13)8,146.400 + 32.881x.
The graph in figure 2 shows the output trend between the 500th and the 1000th cycles, in order to
easier the catching of the cycles: there cycles where periods of decay and growth alternate are clearly visible.
Figure 2 – Comparison of the trend of production between Marshallian and Schumpeterian scenarios: zooming cycles between 500 and 1000.
In the Marshallian scenario the growth of output and productivity is larger, the larger the variance of the given distribution of firms: growth is exogenous and bound to the maximum given level of productivity at the onset of the process. The variance of the distribution of firms, at the onset, instead, has no impact on the Schumpeterian scenario: growth is endogenous.
The robustness of the exposed results has been investigated by computing correlation indexes between key parameters and results. In order to exclude that the tolerance value, used only for computational matter, would affect the results, a correlation has been computed between its values and the correspondent outputs. The named indexes have been computed using the results obtained by dedicated batches of simulations, where the parameters under investigation were randomly tossed each simulation and the other ones - including random distributions with the exception of the distribution used to toss the parameter under investigation - were fixed in order to isolate the effect of each single investigated parameter. Table 4 reports the values of the correlation between the key parameters and output values.
Table 4 shows the weak correlation between imitation and the levels of productivity achieved at the end of the simulations (the weakness of the correlation is witnessed by the variance of the achieved output level that was 0.0038 to be compared with an average value of 9,822.104, even if the values for the parameter imitation were tossed into the range ]0,0.5[). Imitation level matters for concentration indexes, because determines the speed for reaching the equilibrium and the time better firms have to grow due to their adaptive reaction (highly productive enterprises make profits and each time upgrade their input by increasing the input, so their production grows and the system becomes more and more concentrated). Under the Marshallian legacy enterprises are doomed to reach the best productivity due to the working of imitation, so the intensity of the phenomenon has influence only on the pattern the system follow to reach the results, not on the final result. As expected thecRate and GovernancePerformance exert a positive and significant impact on the Schumpeterian based simulations.
The results of Table 5 show that the “tolerance” parameters do not affect the results of the simulations: the correlation between its value and the results have been computed through one hundred simulations with configuration High Gov High React and tolerance value randomly tossed between 0 and 0.0011. The correlation values are meaningless and confirm that even with smaller or even null values for tolerance the results of the simulations are the same.
Table 5 – Correlation between values for the parameter “tolerance” and results of the simulations.
No other correlations have been investigated as this paper aims to highlighting the differences of the results stemming from alternative theoretical configurations rather than forecasting values: the results of the simulation support the hypotheses outlined without any need of empirical evidence about the parameters configuration.
5. Economic interpretation of the results of the simulation
The results obtained running the simulation model under various configurations (scenarios) and different random values for the key parameters demonstrated that the dynamic theorized by Marshall and Schumpeter is fully reproduced by the model. The results confirm the strong and effective role of the microeconomic foundations of the innovation process. The stronger is the reactivity of firms and the more effective the role of knowledge externalities, the more dynamics is the system. The response of Schumpeterian innovative entrepreneurs to the unexpected mismatches in the product and factor markets supported by a rich environment able to secure the provision of high-quality knowledge externalities accounts for economic growth. Reactivity and knowledge
governance are clearly the key parameters that enable the Schumpeterian scenario to outperform the Marshallian. These results of the ABM confirm the hypotheses and validate at the same time the proximity and yet discontinuity of the Marshallian and the Schumpeterian analytical framework. The introduction of reactivity conditional to the quality of the environment is the element that distinguishes and qualifies the Schumpeterian approach from the Marshallian one. The results have proven to be robust because the described effects emerged independently from different values of innovation risks and possibility that new firms can enter the market.
ABM enable to explore the working of the system of interactions, transactions and feedbacks between individual actions and the structure of the system, that qualify the simple but articulated Marshallian and Schumpeterian frames outlined in section 3. ABM provide the opportunity to grasp the dynamics of competitive interactions among heterogeneous agents, that, because of the working of endogenous externalities are able to affect the structure of the system itself. This approach is actually able to model in a parsimonious and simple way the intrinsic complexity of the nested interactions among agents and the endogenous changes in the structure of the system that lay at the heart of both the Marshallian and the Schumpeterian frames (Mueller and Pyka, 20916).
ABM operationalizes, through the interactions among a large number of agents of our systems, the comparison between the working of a typical complex process characterized by the key role of Marshallian externalities and the Schumpeterian notion of creative reaction conditional to the actual availability of knowledge externalities (Schumpeter, 1947), enriched by the explicit assumption that the action of agents affects the structure of the environment including the actual amount of pecuniary knowledge externalities (Lane, 2002 and 2009). In so doing the ABM enables to identify and highlight both the complementarities and the sequential implementations and the theoretical differences between the Marshallian and the Schumpeterian frames. The Marshallian framework can be regarded as the case of zero reactivity and constrained externalities. The Schumpeterian frame starts as son as reactivity is larger than 0 and externalities affect the possibility to innovate. The results of the Marshallian scenario show the high levels of elasticity of output to the distribution of productivity of firms. The larger is initial variance, the larger is output growth. The results of the replicator analysis are fully confirmed. The results of the Marshallian scenario also confirm that i) growth is driven by exogenous assumptions about the distribution of the heterogeneity of firms; ii) growth is bounded to the productivity of best performing firm. When all –surviving- firms reach it, growth stops. Growth can be resumed only by tossing new innovators with higher levels of productivity. Only the introduction of exogenous innovations enables the Marshallian scenario to generate further growth: this is exactly the basic engine of the biological evolutionary approach15. The automatic introduction of exogenous innovations can take place either
by entrepreneurs as suggested by Marshall (1890/1920) and Schumpeter (1911/1934) or by the unexplained upgrading of the routines of corporations as in Nelson and Winter (1982). The hypothesis of exogenous innovation, as the single possibility to keep the dynamics of the system, retained by the evolutionary literature, is far away from the approach eventually sketched by Schumpeter in 1928, articulated in 1942 and fully elaborated in the 1947 contribution.
The results of the Schumpeterian scenario, in fact, confirm that the dynamics of the system is fully endogenous: the larger is the reactivity of firms and the better the quality of knowledge externalities, and the larger output growth. A growth that is endless, provided the system is able to regenerate high quality knowledge externalities. Table 16 provides a synthesis of the different
Schumpeterian combinations of high and low levels of reactivity and poor and good knowledge governance that the ABM allows to explore.
Table 6 – The ingredients of the different levels of Schumpeterian dynamics
The results of the simulation make it possible to compare the and the and the Schumpeterian simulations and to appreciate their substantial complementarity, highlighting the key innovations introduced by Schumpeter (1947 and 1928) and their implementation (Antonelli, 2008 and 2011) upon the Marshallian frame.
The results of the simulation of the Marshallian model confirm five relevant issues: i) in the Marshall innovation is exogenous. Marshall, as much as Schumpeter (1911-1934), assumes that some firms and agents are occasionally and randomly able to introduce better technologies and more effective organizations. ii) Limited knowledge appropriability was well known by Marshall. All existing firms can imitate the superior technology. In Marshall, however, imitation can benefit only laggards. Marshallian externalities consist in imitation effects. Imitation externalities augment and accelerate the reduction of variety brought about by the parallel selection process. The increase of output stems from the selection and imitation processes with the exclusion of less performing firms and the progressive convergence of all production units to the best practice. When variety is erased and all the firms share the best practice, there is no longer room for growth and the positive effects of imitation level off. iii) the Marshallian model is the first and most effective attempt to graft the Darwinian selection process in economics. Innovation is the exogenous source of variety. Variety is transient. Variety in fact exists at the onset of the process and is wiped out by the process itself. Growth is explained by the extent of variety and by the selection process itself. The larger is the variety at the onset of the process and the larger are the rates of growth. Growth stems from the sorting process of the firms that are less efficient and by the generalization of the best practice to all the surviving firms. The rate of growth declines together with the reduction of variety brought about by the selection process. Equilibrium, reduction of variety, exhaustion of endogenous externalities and the end of growth coincide. When the selection process is over, all firms are able to use the best practice and the allocation of resources cannot be improved any longer. There is no endogenous mechanism in the Marshallian process by means of which variety can be reproduced. iv) The replicator dynamics introduced eventually both in biology and in economics is clearly at work in the Marshallian model (Foster and Metcalfe, 2012). The evolutionary applications of the Marshallian model however make clear its intrinsic limitations: in the replicator dynamics variety is exogenous. v) Biological evolutionary economics impinges upon the Marshallian legacy much more than currently assumed and, in any event, its exhibits stronger elements of continuity with the Marshallian framework than with the Schumpeterian approach.
In the scenarios that build upon Schumpeter (1947) innovation is the endogenous source of variety: it is path dependent as it may be continuously recreated by the endogenous dynamics of the system (Page, 2011). Innovation and hence variety are generated by the reaction of firms to the changing conditions of product and factor markets. Innovation and variety are the product of internal feedback supported by the working of pecuniary knowledge externalities. Firms caught in out-of-equilibrium conditions by unexpected factor and product markets try and cope with either extra-profits or losses with the introduction of innovations. All firms, both advanced and laggard, try and
