Order this book
Urban & Regional Research Centre Utrecht (URU), Faculty of Geosciences, Utrecht University
In 2001, an international conference was organised in Aalborg (Denmark) in honour of Nelson and Winter. I remember well that during a panel discussion on the future of evolutionary economics, three visions were presented that were quite different. Soete envisaged evolutionary economics as an interdisciplinary science interacting with as many disciplines as possible. This perspective is quite realistic because, indeed, evolutionary economics is typically characterised as a platform that allows people to cross-over between economics and other disciplines such as biology, geography, sociology and history. Lundvall proposed evolutionary economists to be more active in policy circles, since policy makers are increasingly interested in alternatives to neoclassical policies. Finally, Dosi argued that evolutionary economics should write down its core propositions and methodologies in rigorous handbooks as to protect and codify the intellectual capital that had been built up over 25 years. Put differently, more efforts should be devoted to develop evolutionary economics into a true research paradigm.
The book Applied Evolutionary Economics and the Knowledge-Based Economy edited by Pyka and Hanusch mostly follows the Dosi-proposal. It is an attempt to develop more systematic methodologies for evolutionary economics. The idea behind the book is that without a set of solid methods and approaches, evolutionary economics will not develop into a truly cumulative research program. The same objective was followed in three other volumes in this series edited by Saviotti (2003), Foster and Hölzl (2004) and Frenken (2007). All these books stem from the bi-annual European Meeting of Applied Evolutionary Economics (EMAEE, see: http://www.emaee.net).
The book consists of ten contributions covering a variety of topics, while being united in the use of evolutionary economics. The methodological advances can be found primarily in the chapters by Nesta, Menhart et al., Morone and Taylor, Los and Bottazzi and Secchi. Yet, as we shall see, the book also contributes to the Soete's interdisciplinary agenda and, to a lesser extent, to Lundvall's policy agendas.
Nesta's chapter deals with the effect of knowledge integration (or 'relatedness') on firm performance. This question is important because the cornerstone of the evolutionary theory of the firm is exactly the idea that firms are social entities that exploit complementarities between knowledge bases. Nesta applies a particular relatedness metric on patent data and convincingly shows, using many controls, that knowledge integration indeed contributes substantially to firm performance.
The chapter by Menhart et al. stands in the organizational ecology tradition, which - surprisingly - only recently attracted the attention of many evolutionary economists (on this, see Geroski 2001). The main contribution of this paper, apart from successfully merging the organizational ecology literature with evolutionary economics, is its application to service sectors highlighting some specifics of this sector.
Morone and Tayler in their chapter propose an agent-based approach to learning among agents in social networks. This topic has been addressed in many papers recently, ranging from complexity models to game theory models. Their chapter, however, is important in that they treat knowledge explicitly as an evolving tree structure. The main idea, then, is to model the effect of knowledge exchange among agents not only as a diffusion process, but also as a process that directs learning into specific directions. The combination of diffusion and learning renders the model a truly collective learning model.
In the chapter by Los, a non-linear statistical methodology is developed that is potentially very useful in different domains of application. Evolutionary economics has developed many theoretical models and concept that emphasise that certain dynamics only take-off after a critical threshold has been passed. Los shows how thresholds can be analysed empirically and uses the example of catching up by countries in terms of productivity as an empirical example. Clearly, any other growth process in which catching up is dependent on threshold values can be modelled in similar manner.
The final chapter by Bottazzi and Secchi provides a simulation model inspired by stochastic models of firm growth in the Simonian tradition. Their motivation stems from empirical research showing that the assumption that growth rates are independent across firms and over time, is typically violated. The model they develop is a model in which growth opportunities captured by a firm at one moment in time increase the probability to capture such an opportunity in the next moment. They relate such a self-reinforcing mechanism to Arthur's (1994) work on positive feedbacks. Again, this model is very useful not only in that it goes beyond the purely random models of firm growth, but also because it general setup allows for transferring the model to other domain of application (think of city size distribution, word frequencies, citation frequencies, et cetera).
A second set of chapters deals with interdisciplinary excursions within the field of evolutionary economics. In particular, we find three chapters applying evolutionary economic theory to the field of economic geography, a marriage that has received a lot of attention in geography journals recently (Boschma and Frenken 2006; Martin and Sunley 2006).
Stam provides in his chapter a conceptual framework of firm growth and firm location. The two processes are intertwined because firm growth leads a firm to reconsider its location, while relocation in turn affects firm growth. This process leads to unique firm trajectories ranging from firms growing in situ, multi-locational firms growing through setting up subsidiaries, and firms growing through relocation. These different patterns can be analysed in a single framework where past decisions affect future locational trajectories.
The chapter by Chabchoub and Niosi provides an analysis of software clusters inorth America using patent data and suggests that clusters are fuelled by the presence of an 'anchor tenant'. These are large firms or public organisations that attract smaller firms to a region or creating smaller firms within the region through spin-off.
Nuvolari et al. present an empirical study of the adoption of early steam engines across British regions in the eighteenth century. As expected, the regional differences between the price of coal greatly influenced the adoption rates across regions. However, the absorptive capacity of regions also played an important role reflecting regional learning trajectories regarding the use of new technology.
Finally, there is a chapter that combines insights from economics and psychology to develop a conceptual framework of innovation and entrepreneurship. Using the concept of imagineering, and theories from cognition sciences, Wentzel provides some building blocks of a true theory of innovation. Indeed, many studies have shown that innovations often stem from conceptual breakthroughs rather than from technology advances per se (even though the two are not mutually exclusive). The remarks by Wentzel are quite important to go beyond the usual way in evolutionary economic to model innovation as somehow random.
Policy issues are somewhat under-represented in this book. However, I do not regard this as a shortcoming as many books now appear that are specifically focuses on certain policy issues. There is however, one innovative policy chapter by Cowan et al. on how to learn from disasters. They consider the case of a computer-controlled radiation therapy machine introduced in Canada, which after some years without incident suddenly overdosed six people. The interesting aspect of this tragic case is that the disaster provided unprecedented opportunities to learn about software and safety in general. The lesson here is to use disaster as 'experiments' that provide unique opportunities to create knowledge. Organisations, then, should think about their ways of dealing with such disasters as to maximize the potential benefits of learning. Again, this is a challenging topic for applied evolutionary economics in that we are dealing with the economics of extreme low probability events. Traditional theories of learning no longer apply and different types of frameworks are needed (some are of course already elaborated, for example, in environmental sciences).
In sum, the book provides an overview where (applied) evolutionary economics stands now. It shows that most advances are achieved in the development of sound methodologies as well as continued interaction with other disciplines. Both trends, I believe, deserve following in the future.
BOSCHMA E and FRENKEN K (2006) Why Is Economic Geography not an Evolutionary Science? Towards an Evolutionary Economic Geography. In Journal of Economic Geography, 6, 3: 273-302.
FOSTER J and HOLZL W (2004) Applied Evolutionary Economics and Complex Systems. Cheltenham UK: Edward Elgar.
KRENKEN K (2007) Applied Evolutionary Economics and Economic Geography. Cheltenham UK: Edward Elgar.
GEROWSKI PA (2001) Exploring the Niche Overlaps between Organizational Ecology and Industrial Economics. In Industrial and Corporate Change, 10, 2: 507-540.
MARTIN R and SUNLEY P (2006) Path Dependence and Regional Economic Evolution. In Journal of Economic Geography, 6, 4: 395-437.
NELSON RR and WINTER SG (1982) An Evolutionary Theory of Economic Change. Cambridge, MA and London: The Belknap Press.
SAVIOTTI PP (2003) Applied Evolutionary Economics. Cheltenham UK: Edward Elgar.
Return to Contents of this issue
© Copyright Journal of Artificial Societies and Social Simulation, 2007