Chapter 25 Agent-based Models of Innovation and Technological Change

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Abstract

This chapter discusses the potential of the agent-based computational economics approach for the analysis of processes of innovation and technological change. It is argued that, on the one hand, several genuine properties of innovation processes make the possibilities offered by agent-based modelling particularly appealing in this field, and that, on the other hand, agent-based models have been quite successful in explaining sets of empirical stylized facts, which are not well accounted for by existing representative-agent equilibrium models. An extensive survey of agent-based computational research dealing with issues of innovation and technological change is given and the contribution of these studies is discussed. Furthermore a few pointers towards potential directions of future research are given.

Introduction

Innovation and technological change1 is today generally seen as one of the driving forces if not the driving force of economic growth in industrialized countries (see e.g. Maddison (1991) or Freeman (1994)). Whereas this aspect of economic activity has for a long time been largely neglected in mainstream economics, its importance has by now been recognized and a large rather diversified literature has evolved focusing on different aspects of technological change. Based on the fast growing empirical literature on this issue a rich set of well accepted facts concerning technological change have been established. Concepts of incremental/radical innovations or technological paradigms and trajectories have been developed to capture patterns holding across sectors. Typical patterns of industry evolution and the general importance and structure of knowledge accumulation processes have been established. Also, the existence of heterogeneity in employed technology and firm size within many industries as well as a large degree of sector specificity of patterns of technological change has been observed. The reader is referred to Dosi, 1988, Dosi et al., 1997, Freeman, 1994, Klepper, 1997, Kline and Rosenberg, 1986, Malerba, 1992, Pavitt, 1984, Pavitt, 1999, Rosenberg, 1994 for extensive discussions of empirical findings about technological change. Likewise, the set of modelling approaches and tools that have been used to gain theory-based insights about origins and effects of innovation and technological change is very wide including dynamic equilibrium analysis, static and dynamic games, theory of complex systems or evolutionary theorizing. Overviews over different strands of theory-oriented literature can be found e.g. in Dosi et al., 1988, Grossman and Helpman, 1994, Hall, 1994, Nelson and Winter, 2002, Stoneman, 1995, Sutton, 1997 or van Cayseele (1998).

The aim of this chapter is to highlight and discuss the past and potential future role of the agent-based computational economics (ACE)2 approach in the important endeavor to gain a better understanding of technological change. Two main arguments will be put forward to make the point that agent-based models might indeed contribute significantly to this literature. First, as will be argued below, predictions of standard equilibrium models do not provide satisfying explanations for several of the empirically established stylized facts which however emerge quite naturally in agent-based models. Second, the combination of very genuine properties of innovation processes call for a modelling approach that goes beyond the paradigm of a Bayesian representative-agent with full rationality and it seems to me that the possibilities of ACE modelling are well suited to incorporate these properties. The genuine properties I have in mind are: (i) the dynamic structure of the process(es); (ii) the special nature of ‘knowledge’, arguably the most important input factor for the ‘production’ of innovation; (iii) the strong substantive uncertainty involved; (iv) the importance of heterogeneity between firms with respect to knowledge, employed technology and innovation strategy for technological change.

Let us briefly discuss these four points. (i) The dynamic aspects of the process of innovation and technological change have been stressed at least since the seminal work of Schumpeter (1934, first published 1911 in German language). Technological change does not only lead to an increase in overall factor productivity but also has significant effects on the way the market and industry structure evolves over time. Schumpeter's trilogy of invention-innovation-diffusion already indicates that the innovation process per se has a time structure which should be taken into account. In particular, the speed of diffusion has important implications for the expected returns to innovation on one hand and for the evolution of the market structure on the other hand. The way innovations diffuse are industry specific and such processes typically involve path dependency and dynamic externalities. Also the other two stages in the trilogy involve truly dynamic processes. Investment decisions about innovation projects are typically not made once and for all but are continuously updated over time. This is necessary due to the substantive uncertainty involved in predicting markets and technological developments as well as the accumulation of own knowledge (see the comments below)3.

(ii) The success of innovative activities of a firm does not only depend on its current investment but also to a large extent on the size and structure of the knowledge base the firm has accumulated. The stock of knowledge of a firm is not uniform and has a lot of structure4. For example distinctions should be made between explicit and tacit knowledge as well as between general knowledge and specific skills. A large body of empirical evidence has demonstrated that the knowledge base (Dosi, 1988) needed for successful inventions and innovations has to be gradually accumulated over time. Several mechanisms have been identified to gain such knowledge, among them in-house R&D, informal transfer of knowledge between companies (spillovers) or learning by doing. In all cases the effect of current actions depends crucially on past experience and therefore the entire process of knowledge accumulation has to be considered when studying innovative activities. Studying accumulation of knowledge is however quite different from studying accumulation of physical capital. Knowledge can only to a certain extent be traded on a market. It is often embodied in individuals and groups of people (‘tacit knowledge’; see Polanyi, 1966), can almost without cost be duplicated by its owners and has a tendency to flow through several local and global channels of diffusion. Studying such flows means dealing also with issues of local interaction and communication network formation. Incorporation of explicit knowledge accumulation processes and non-market interactions between firms into an equilibrium model of technological change might in principle be possible, but this would most probably destroy any analytical tractability and to my knowledge has not been attempted yet.5

(iii) The level of uncertainty associated with innovations depends on the type of industry and the type of innovation we are dealing with. Typically a distinction is made between incremental innovations, where minor extensions to existing processes or products are introduced without leaving the current paradigm, and radical innovations which try to open new markets or to employ a new technique or organizational structure for the production of a good. Building beliefs about future returns of an attempt to develop a radical innovation is a very challenging task (see Freeman and Perez, 1988). There is uncertainty not only about the technical aspects (feasibility, reliability, cost issues) but also about market reaction. Whether an innovation turns out to be a market-flop, a solid profit earner or the founder of a new market depends on numerous factors and is ex ante hard to see6.

More generally, any economic agent operating in an environment influenced by innovations is subject to ‘strong substantive uncertainty’ (Dosi and Egidi, 1991) in the sense that it is impossible to foresee the content of inventions to be made in the future (otherwise it would not be a new invention) and therefore to anticipate all possible directions of future technological development. Put more formally, the current mental model of the agent cannot include all possible future contingencies. Accordingly, a standard Bayesian approach, which has to assume that the agent ex ante knows the set of all possible future states of the world, is not appropriate to capture the essence of the uncertainty involved with innovation processes. Or, as Fremman and Soete (1997) put it: ‘The uncertainty surrounding innovation means that among alternative investment possibilities innovation projects are unusually dependent on ‘animal spirits". [p. 251]. Furthermore, it has been argued in Dosi and Egidi (1991) that ‘procedural uncertainty’ referring to the inability of an agent to find the optimal solution in a choice problem—either due to her limited capabilities or due to actual problems of computability—is also of particular importance in many tasks associated with innovation and technological change (see also Dosi et al., 2003). It seems that a rule-based model of the decision making process which, on the one hand, makes constraints on computability explicit and, on the other hand, restricts usable information to what is available to the agent at a certain point in time, rather than assuming an ex-ante knowledge about the set of all possible future contingencies, is better able to capture decision making under strong substantive and procedural uncertainty than dynamic optimization models with Bayesian updating or even perfect foresight.

(iv) Finally, the study of processes and effects of innovation requires particular consideration of the heterogeneity between firms in a market. Different types of heterogeneity should be distinguished. I will mention here three types of heterogeneities relevant for understanding technological change, but this is certainly no complete list. First, it has been shown that the basic approach towards innovative activities—e.g. whether to focus efforts on product or process innovation, on incremental or radical innovation or even completely on imitation and reverse engineering—is in many instances quite heterogeneous even within one industry (e.g. Malerba and Orsenigo, 1996). Second, heterogeneity and complementarity of the knowledge held by different firms in an industry is an important factor in facilitating the generation of new knowledge through spillovers as well as in the exploration of the potential avenues of technological development. Third, heterogeneity is not only an important pre-requisite for the emergence of technological change, it is also a necessary implication of innovative activities. The whole point of innovating for firms is to distinguish themselves from the competitors in the market according to production technique or product range, thereby generating heterogeneities. Innovation incentives depend on (potential) heterogeneities between firms. So, whereas heterogeneity of agents is an important property in any market interaction, consideration of heterogeneities of firm characteristics, strategies, technologies and products seems essential if the goal is to understand the processes governing technological change. It is well established by now that in general aggregate behavior stemming from heterogeneous agents cannot be properly reproduced by using a representative agent instead (see e.g. Kirman, 1992) and therefore these heterogeneities should be properly represented in the models used to analyze technological change.

Summarizing the brief discussion of properties (i)–(iv) we conclude that when considering the process of technological change in an industry, we are looking at a highly decentralized dynamic search process under strong substantive and procedural uncertainty, where numerous heterogeneous agents search in parallel for new products/processes, but are interlinked through market and non-market interactions. So already from the purely theoretical perspective that a micro-founded economic model, even if highly stylized, should capture the essential effects influencing the phenomenon under examination, the possibilities offered by agent-based computational models are appealing. The modelling of the dynamic interaction between individuals who might be heterogenous in several dimensions and whose decisions are determined by evolving decision rules can be readily realized using ACE models.

Whereas my discussion so far has focused on the issue of realism of the assumptions underlying a model, there is a second argument of at least the same importance for the use of an ACE approach in this field, namely that of the explanatory power of the model. This is particularly true, if we compare the ACE modelling with neoclassical equilibrium analysis. The problems of neoclassical models to explain and reproduce important stylized facts about innovation, technological change and industry evolution have been discussed among other places in Dosi et al., 1995, Dosi et al., 1997, Sutton (1997) or Klepper and Simmons (1997). Here, no extensive discussion of this issue is possible. I restrict myself to sketching a few of the empirically supported observations which are at odds with or at least not satisfactorily explained by a neoclassical approach, particularly if we consider several of these facts jointly (for more details on these ‘stylized facts’ see the references given above, Silverberg and Verspagen (2005a) and a special issue of Industrial and Corporate Change (Vol. 6, No. 1, 1997)).

  • In almost all industries a relatively stable skewed firm size distribution can be observed, i.e. there is persistent co-existence of plants and firms of different sizes.

  • Persistent heterogeneities between firms with respect to employed technology, productivity and profits rather than convergence to a common rate of return can be observed in many industries.

  • In general, there is a positive correlation between entry and exit rates of firms across industries. Industry profitability does not seem to have a major effect on entry and exit rates.

  • Patterns of industry evolution and demographics vary considerably from industry to industry. On the other hand, there are strong similarities of these patterns across countries in the same technological classes. In particular, the knowledge conditions shaping the technological regime underlying an industry have substantial influence on the observed pattern.

  • The arrival of major innovations appears to be stochastic, but clustering of major innovations in a given time interval is stronger than one would expect under a uniform distribution.

As will be demonstrated in Subsections 3.4 Micro-founded models of economic growth, 3.5 Industry studies and ‘history-friendly’ models, quite a few of these observed patterns can be rather robustly reproduced using ACE models. This is particularly encouraging since these patterns are in no way explicitly incorporated into these models, but are emergent properties of the aggregate behavior in complex models, which in many cases are built upon rich micro foundations incorporating at least some of the key features of the processes involved in actual technological change. This highlights another important feature of ACE models: namely, that due to its reliance on computer simulations, this approach can easily link the interplay of individual innovation strategies, market structure and micro effects to the development of industry-wide or even economy-wide variables like average factor productivity, number of firms or economic growth. The emergence of regular macro patterns based on decentralized uncoordinated micro interaction is an important general feature of agent-based models. The fact that ACE models are well able to reproduce actual aggregate behavior under given economic conditions becomes particularly relevant if ACE models are used to predict and evaluate the effects of policy measures that might change the industry or market environment (see e.g. Kwasnicki, 1998 or Pyka and Grebel, 2006 for more extensive discussions of the potential of agent-based modelling in evolutionary economics).

Despite the apparent merit of the agent-based simulation approach for the analysis of a wide range of issues in the economics of innovation and technological change, the amount of relevant ACE-based work in this area is not huge. A large fraction of this work has been conducted in the tradition of the evolutionary economics approach pioneered by Nelson and Winter (1982). However, the amount of work in this area substantially increased during the last few years where also several issues outside the scope of evolutionary analyses were addressed. This chapter will give an overview over the issues addressed in the different types of ACE studies in this area and highlight some examples of the kinds of models which were developed to do this. The presentation will be organized around the two main arguments for the use of ACE models in the domain of the economics of innovation which were discussed in this introduction. I will first illustrate the different ways ACE researchers have tried to address each of the four discussed specific properties of technical change processes in their models.7 Afterwards, I will discuss a number of ACE models which have been successful in reproducing stylized patterns of industry evolution and economic growth. Although there will be some coverage of ACE models of economic growth, the overall focus of the chapter is on the micro foundations and industry level behavior rather than on economic growth. A more extensive discussion of the potential of ACE models for the analysis of economic growth from a broader perspective can be found in the chapter by Howitt (2006) in this handbook. It is also important to point out a few topics what will not be covered in this chapter in spite of their relevance for the understanding of economic change. I will not discuss issues associated with organizational change (this is at least partly covered in the chapter by Chang and Harrington, 2006 in this handbook). I will only touch upon the important relationship between organizational and technological change and the crucial role of organizational structure of a firm for the success of its innovative activities. Also, there will be little discussion of networks emergence and information diffusion models although such models are of obvious relevance for the understanding of several aspects of the process of technological change (e.g. knowledge spillovers, speed of diffusion of new technologies). Models of this kind are discussed in the chapters by Vriend (2006) and Wilhite (2006) in this handbook. See also Cohendet et al. (1998) for a collection of surveys and papers dealing with this issue.

The plan for the remainder of this chapter is the following. In Section 2 the evolutionary approach is briefly discussed and in Section 3 I survey some of the existing literature8 where ACE models have been developed to address issues of innovation and technological change. In Section 4 I will briefly discuss whether my statements in this introduction concerning the potential of ACE research in this domain can be justified based on the work surveyed in Section 3. I conclude with Section 5, where a few challenges and promising topics for future work are highlighted.

Section snippets

The evolutionary approach

The dynamic process of technological change has been extensively analyzed in the field of evolutionary economics. The range of work which is subsumed under the label evolutionary economics is quite broad and heterogenous. According to Boulding (1991) ‘evolutionary economics is simply an attempt to look at an economic system, whether of the whole world or of its parts as continuing process in space and time.’ Clearly the notion of some kind of ‘selection’ process which determines the direction

Agent-based models of technological change

In this section I will discuss a number of ACE studies dealing with different aspects of innovation and technological change. The presentation is organized according to the main themes discussed in the introduction. I will first focus again on the four important properties of technological change processes discussed in the introduction. For each of the properties (ii)–(iv)16

Discussion

I have started this chapter by arguing that there are two main reasons why agent-based models should be particularly useful for the analysis of processes of innovation and technological change. First, several of the crucial defining aspects of the process of innovation and technological change are readily incorporated in ACE models but can hardly be captured in neoclassical equilibrium analyses. Second, ACE models seem to be able to reproduce a number of stylized facts in this domain which are

Outlook

An important aspect of the overall ACE research agenda is the provision of micro-founded explanations for meso-level and macro-level phenomena. Quite a bit has been done in this respect also with respect to the analysis of innovation and technological change, but obviously there is still much more to do.

The process of technological change and the associated economic processes are extremely rich and many aspects have so far been only lightly touched or even completely ignored in the literature.

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      This means that, once taking into account the transaction costs, all consumption firms have same access to technological innovations and, for a given price, will choose the most advanced ones, i.e., the machineries implying the lowest unit labor cost, without facing any technical constraints. On the other hand, numerous studies in the micro-evolutionary literature stress the role of technological knowledge and capability accumulation in the success of innovative activities carried out by (capital good) firms (Cantner and Pyka, 1998; Cohen and Levinthal, 1989; Dawid, 2006). This paper proposes a synthesis of the two approaches by conceiving technological knowledge as a means to improve the C-firms’ ability to employ the best machines produced by K-firms.

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    I am grateful to Giovanni Dosi, Giorgio Fagiolo, Ken Judd, Leigh Tesfatsion, Klaus Wersching and five anonymous referees for very helpful comments and suggestions.

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