Innovation is the creation of new, technologically feasible, commercially realisable products and processes and, if things go right, it emerges from the ongoing interaction of innovative organisations such as universities, research institutes, firms, government agencies and venture capitalists.
Innovation in Complex Social Systems uses a "hard science" approach to examine innovation in a new way. Its contributors come from a wide variety of backgrounds, including social and natural sciences, computer science, and mathematics. Using cutting-edge methodology, they deal with the complex aspects of socio-economic innovation processes. Its approach opens up a new paradigm for innovation research, making innovation understandable and tractable using tools such as computational network analysis and agent-based simulation.
This book of new work combines empirical analysis with a discussion of the tools and methods used to successfully investigate innovation from a range of international experts, and will be of interest to postgraduate students and scholars in economics, social science, innovation research and complexity science.
'Complexity theory has tended to focus on natural systems, but has been increasingly applied to social systems (Luhmann 1995; Sawyer 2005). To be usefully applied to social systems, complexity theory must be extended, to address the nature of the elements of the system (whether individual human agents, or organizational entities), how the social systems are structured, and how the elements communicate. Luhmann (1995) and Sawyer (2005) both argued that communication was the key element of social systems that structure themselves and that manifest emergence.
Consistent with these more general theories of social complexity, this book proposes that the elements, their structure, and their communications are the key features of innovation networks (p. 11). But existing complexity theory is not yet sufficiently advanced to fully understand innovation networks; Ahrweiler's introduction argues that understanding innovation requires several advances in network analysis. First, innovation networks tend to be multi-level, and modeling multi-level networks remains a research challenge. Second, the structure of knowledge is critical, whereas most network analyses treat it as "a bag of potatoes" (p. 6). Third, most network analyses analyze innovation diffusion through networks, but we also need to understand how novelty emerges in the first place (p. 6). The papers in this volume attempt to move our understanding forward in each of these areas.
This is an important book. Its strength lies in its interdisciplinarity; it brings together complexity theory, empirical studies of regional innovation networks, and agent-based modeling of innovation networks. In its interdisciplinary scope, this book represents a novel and valuable contribution. The book originated in an international conference titled "Innovation in complex social systems" held at University College Dublin, 10-12 December 2008. Twenty one of the 22 chapters are new for this volume. The editor's introduction states the unifying assumption of the book: "Innovation is an emergent property of a complex social system involving heterogeneous agents" (p. 16), a network with many nodes, including universities, government agencies, venture capitalists, and small and large firms. The "hardware" of innovation "consists of inter-organizational innovation networks" (p. 4) and that's why many of the chapters draw on network analysis methodologies and on complexity theory. The "software" of innovation networks is "a set of communications, which includes actions" (p. 13).
Each of these three approaches - complexity theory, empirical studies of regional innovation networks, and agent-based modeling - is given one section in the volume.
Part I contains three theoretical essays on complexity topics, including emergence, levels of analysis, irreducible novelty, and evolutionary approaches. Chapter 2, by John Casti, applies complexity concepts - such as the power law - to technological inventions. He concludes that policy makers should foster an environment that encourages many "fringe" small-scale innovation bets, rather than a few carefully planned and designed efforts. Chapter 3, by Horst Hanusch and Andreas Pyka, applies their own Comprehensive Neo-Schumpeterian Economics (CNSE) to public sector economics, emphasizing concepts including technological clusters, innovation networks, and entrepreneurship. In this approach, "innovation is the proactive and therefore endogenous displacement and movement of restrictions" (p. 45), and socioeconomic systems develop within a narrow corridor "between the extremes of uncontrolled economic success (growth) and exploding bubbles, on the one hand, and stationarity, i.e. economic stagnancy, on the other" (p. 49). Chapter 4, by Pier Paolo Saviotti, describes his Tevecon model of economic systems as complex systems. Complexity theory suggests that social systems are never stable and static, but are always in constant change, and it is this change that provides the potential for innovation. In the Tevecon model, "the process of economic development is closely linked to the creation of new sectors" (p. 63).
Part II, "The actors and networks of innovation," includes 11 essays that report on empirical research, primarily in regional networks of relationships. These chapters are obviously influenced by the "National Innovation System (NIS)" approach first suggested by (Lundvall 1992). The editor's introduction distinguishes between systems, which are closed and best characterized by processes, and networks, which are open and best characterized by structures (p. 12), but the individual chapters use both the terms "innovation network" and "innovation system." The chapters in Part II analyze patent networks, publication networks, citation networks, contractual networks, and communication networks. An impressive range of European countries is represented in this section, along with the United States (but there are no studies of Asian countries). Chapter 5, by Thomas J. Allen, Ornit Raz, and Peter Gloor, discusses the biotech cluster centered around Boston, Massachusetts. They provide data showing that firms located within the geographic bounds of the cluster communicate more, and show greater centrality, than do nearby firms that are nonetheless outside the geographic boundaries of the cluster. Chapter 6, by Bernd Ebersberger and Florian M. Becke, analyzes regional innovation systems in Austria by analyzing patent and utility model documents from 1988 to 2008. The data show that Austria's nine regions have specialized in complementary technologies, and these patterns have changed very little over the twenty years studied. Chapter 7, By BjArn Asheim, reports on his SMEPOL project's study of Small and Medium Enterprise policy. Policies vary on two dimensions: a focus on providing resources vs. a focus on behavioral change and organizational learning; and policies focused within a firm vs. policies focused on regions and innovation systems. Sweden, Finland, and Denmark have pursued somewhat different policies. Chapter 8, by Rajneesh Narula and Julie Michel, analyze knowledge transfer and reverse knowledge transfer (from subsidiary to home country) when MNEs diversify globally. They provide data showing that Swiss firms tend to transfer knowledge to subsidiaries rather than vice-versa. Chapter 9, by Martin Heidenreich, Christoph Barmeyer, and Knut Koschatzky, analyzes product development in multinational companies (MNC) and report that reverse knowledge transfer is rare in practice: less than 20 percent of MNCs were able to do this successfully. They analyze two case studies, in the automobile and IT industries. Chapter 10, by Federica Rossi, Margherita Russo, Stefania Sardo, and Josh Whitford, analyzes how to design interventions to support innovation, with reference to a program administered in the Tuscany region of Italy 2001 to 2004. The program was designed to foster heterogeneous networks. They also analyze the CRIT organization, which acts as a technology broker for leading firms in the Modenese and Emilian mechanical industry. Although both interventions were initially conceived as conventional technology transfer activities, the ultimate benefit came from "the creation of spaces for open-ended discussion, where the interpretive ambiguity necessary for innovation could emerge" (p. 158). Chapter 11, by Henry Etzkowitz and Marina Ranga, analyzes a case study of an entrepreneurial university: a university that interacts most effectively with government and industry to foster the creation of new ventures. They contrast the role of MIT in the New England region of the USA, and the UK Science City strategy launched in the U.K. in 2004 in five reindustrializing cities. Chapter 12, by T. Austin Lacy, analyzes entrepreneurial universities by using national U.S. data. Both small focused institutions and large complex universities have an advantage over other institutions in commercializing intellectual property. Chapter 13, by Philipp Magin and Harald F. O. von Kortzfleisch, analyzes 183 institutions that support scientific entrepreneurship in Austria, Germany, and Switzerland. They find different structural and systemic patterns in each country. Chapter 14, by William Allen and Rory O'Shea, presents a case study of the commercialization efforts of University College Dublin, and conclude by identifying the factors associated with successful commercialization. Chapter 15, by Uwe Obermeier, Michael J. Barber, Andreas Krueger, and Hannes Brauckmann, analyzes co-authorship patterns among scientists at University College Dublin by using social network analysis.
Part III, "The systemic aspects of innovation," includes 6 essays that report on models of complex innovation processes. Chapter 16, by Nigel Gilbert, Petra Ahrweiler, and Andreas Pyka, presents the SKIN multi-agent model of firm innovation (Simulating Knowledge dynamics in Innovation Networks). Each agent possesses a number of "units of knowledge." The model demonstrates fairly incremental innovation and learning, unless the firm is near death - and then, a firm can "generate a new innovation hypothesis" (p. 244), possibly by collaborating with another firm. Chapter 17, by Marco Villani and Luca Ansaloni, propose that radical innovations result from a process analogous to biological exaptation - when an organ that evolved for one purpose is later used for completely different tasks (note the parallel with Chapter 10's concept of "interpretive ambiguity"). They describe their EMIS model (Exaptation Model in Innovation Studies) of the evolutionary process of exaptation in created artifacts; simulations suggest that the emergence of exaptations is more likely with asymmetrical communication, a high level of noise, and the plasticity of users' categories (p. 262). Chapter 18, by Tommaso Ciarli, Riccardo Leoncini, Sandro Montresor, and Marco Valente, analyzes the relationships between product architecture (defined as the interfaces among interacting physical components) and firm organization. Their simulation expands upon the "mirroring hypothesis" that organization design should match product architecture. Chapter 20, by Francesca Giardini and Federico Cecconi, analyzes reputation and gossip in social networks, and presented their SOCRATE model of firms as agents organized into three layers. Chapter 21, by Flaminio Squazzoni and Riccardo Boero, argues that policy making is "not equipped to tackle the challenge of the complexity of the innovation process" (p. 291); they propose that agent-based models can aid policy makers face this challenge. Chapter 21, by Ramon Scholz, Terhi Nokkala, Petra Ahrweiler, Andreas Pyka, and Nigel Gilbert, describes the NEMO project (Network Models, Governance, and R&D collaboration networks) and the SKEIN agent-based model (Simulating Knowledge Dynamics in EU-funded Innovation Networks), which simulates the emergence of collaboration networks. They demonstrate that the structure of scientific collaboration networks is strongly influenced by policy.
The editor's insightful conclusion contrasts the complex systems approach to an older linear model of innovation, an input-output model in which ideas move through a pipeline from basic research, to applied research, to technology development, to production and diffusion. Theoretical models based on the linear model rarely predict the observed empirical growth rates: "growth as a system level phenomenon is produced by a complex interaction pattern on the micro level ... This is why we have to investigate the role of collaborative arrangements in innovation" (p. 318).
I learned a lot from reading these chapters; the breadth of the topics covered is quite impressive, and the editor has succeeded in bringing together diverse chapters so that "the whole is greater than the sum of the parts," as we like to say in complexity research. Overall, this book will be of primary interest to scholars who study regional innovation networks and national innovation systems, and scholars who simulate these with agent-based modeling. Many of the chapters have clear and explicitly stated policy implications; for example, they provide confirmation for some of what policy makers are doing. Many government efforts are appropriately focused on research universities, and these chapters confirm that innovation is advanced by funding basic research in universities, funding efforts to commercialize those basic advances, and changing tax and intellectual property law to provide incentives for universities to commercialize their research. Regarding regional clusters, the practical lessons for governments are more complex - but several chapters suggest that the most effective policy is to increase density and communication within regional networks. Unfortunately, the book will probably be a bit too technical for policy makers, but the lessons of these studies should be considered by any national government that is implementing efforts to foster innovation.
But other government efforts may be off the mark, according to many of the authors in this volume. Far too many government efforts have been based on an overly simplistic, linear model of innovation - when empirical studies have demonstrated that innovation results from highly complex and hard-to-predict emergence processes. In the future, studies such as the ones represented in this book have the potential to guide national policy for maximum effectiveness.'
- Keith Sawyer, Washington University, USA for the Journal of Artificial Societies and Social Simulation, Volume 13, Number 4
Part 1: The Systemic Aspects Of Innovation (Theory) 1. Innovation is Emergent 2.Innovation is Evolutionary Part 2: The Actors and Networks of Innovation (Empirical Research) 3. Innovation is Regional 4. Innovation is Small 5.Innovation is Big 6. Innovation is Policy-Driven 7. Innovation is Academic Part 3: The Systemic Aspects of Innovation (Modeling) 8.Innovation is Computable