Reviewed by
Federico Bianchi
Department of Economics and Management, University of Brescia
The very enterprise of understanding complex systems cannot be accomplished through isolated efforts. Accordingly, this book ought to be read as a successful outcome of a long-standing collaborative work whose roots date back to a 1998 European project on modelling emergent innovation networks. Undertaken by scholars with different social sciences backgrounds, the project resulted in what was dubbed the SEIN model (soon renamed SKIN, Simulating Knowledge Dynamics in Innovation Networks, whence the book title; see (Gilbert et al. 2001), jointly developed by Nigel Gilbert, Petra Ahrweiler, and Andreas Pyka, the three editors of that book.The book includes nine contributions from European scholars who applied the SKIN model to different facets of the study of inter-organisational networks in the innovation economy. The model is thoroughly described by the editors in Chapter 1 (Ahrweiler, Pyka & Gilbert, pp. 1-13). The complex process of selective transfer and adjustment of pieces of technological capital and knowledge resources is modelled through an elegant solution (a “kene”), which lies at the core of the SKIN model. Agents are endowed with a sequence of triplets whose values represent a firm’s capability (C) in a scientific, technological, or business domain, its ability (A) to apply a certain capability to a particular field, and a level of expertise (E) acquired with respect to that ability. Firms, then, pursue innovative processes by identifying an “innovation hypothesis”, which is derived from a subset of the firm’s kene triplets. Finally, cooperative interaction with partners allows firms to adjust its kene by changing expertise levels, i.e. by adding or dropping abilities or capabilities, thus mimicking various kinds of processes of organisational learning.
The remaining chapters are arranged into three sections whose structure is fashioned so as to resemble the broad scope of the ABM approach to the social sciences, which aims both at explaining socioeconomic phenomena more accurately and informing policy-makers more efficiently.
Part I deals with the effects of the dynamics of firm-level strategies on the emergence of innovation networks, thus testing micro-level hypotheses to explain different macro-level configurations. Chapter 2 (Blom & Hildrum, pp. 47-72) and Chapter 4 (Müller, Buchmann & Kudic, pp. 73-95) are especially relevant as they provide results of general interest for the study of interorganisational networks. The authors show the results of two-step partner selection strategies driven by preferences towards partners of partners (step 1, transitive closure) and firms with many partners (step 2, preferential attachment). The outcome is the emergence of networks with characteristics of both small-world (high clustering and short average path length) and scale-free models (power-law degree distribution). Crucially, while small-world properties yield positive effects in terms of rapidity of knowledge diffusion, the emerging similarity within clusters prevents firms from diversifying their knowledge bases, eventually generating bad performance on the end market. Moreover, by applying the SKIN model to the Nordic Internet Service Provider industry, simulations of the SKIN model show that such dysfunctional phenomena could arise if there are too many outward-oriented firms. This could affect smaller and more specialised firms more than larger and lesser specialised ones.
Part II moves beyond the mere scientific explanation of the emergence of innovation networks and shows the potential of the SKIN model for policy-making. Innovation policy can be informed both by ex-ante scenario simulations and by ex-post evaluations of programmes. In particular, Chapter 5 (Korber & Paier, pp. 99-130) explores pros and cons of different ways of government intervention in financing industrial research programmes. They simulated the introduction of direct funding vis-à-vis tax incentives on the local life sciences industrial research cluster in Vienna. In Chapter 7 (Ahrweiler, Pyka, Schilperhoord & Gilbert, pp. 155-183) a refined version of the model is presented as a tool to assess EU ICT programmes. In this case, heterogeneous agents represent different kinds of research organisations linked to various funded projects and generate a two-mode network that mirrors reality at a higher level of complexity.
Part III shifts the focus of the effects of innovation network dynamics to the systemic level of industrial sectors. In Chapter 9 (Schrempf & Ahrweiler, pp. 201-216), the authors show how to model the emergence of a whole general-purpose technology industrial system using the case of the Irish nanotechnology industry. Finally, Chapter 10 (Dilaver, Uyarra & Bleda, pp. 217-241) provides a particularly insightful example of the power of ABM in modelling complex multi-level architectures. Here, the SKIN model is adapted to encompass not only an interfirm network but also individuals interacting within firms as distributed knowledge systems.
In conclusion, the book demonstrates that ABM is a useful tool not only for pure scientific purposes – such as micro-level hypothesis testing (Ch. 1-3) and macro-level system modelling (Ch. 9-10) – but also for policy-making (Ch. 4-8) – through both ex-post evaluation and ex-ante decision-making. The relationship between the construction of a large-scale flexible model and its possible applications and adaptations is clearly shown in many chapters. In this way, the book also attests to the power of collaborative efforts for providing tools for cumulative scientific results. Moreover, the collection of studies lays bare the possibility of integrating various methods to calibrate and validate ABMs in socioeconomic research (e.g., stakeholder interviews and secondary data analysis). From a methodological point of view, the book shows that social simulation can complement for the weakness of network analysis in understanding complex dynamic socioeconomic systems like innovation (Ch. 3, 7, 10; see also Ahrweiler 2010). Moreover, the application of the SKIN model also offers insights for the general network science about the micro-level mechanisms underlying the emergence of networks among collective actors; by considering strategic decision-making or multi-level internal processes, ABM can help to overcome mere structural explanations of the emergence of interorganisational networks.
ReferencesGILBERT, N., Pyka, A. & Ahrweiler, P. (2001). Innovation Networks – A Simulation Approach. Journal of Artificial Societies and Social Simulation, 4 (3) 8. Available at: https://www.jasss.org/4/3/8.html
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