Andreas Pyka

Innovation networks in economics: from the incentive‐based to the knowledge‐based approaches

Innovation networks have become a persistent organisational phenomenon in industrial innovation processes. However, in economics they were considered in the first place only as a temporary hybrid phenomenon between markets and a hierarchical organisation within a single firm. The main focus of traditional neo‐classical analysis simply was on cost reduction of R&D within a network. Only with the coming up of evolutionary economics with its prevailing knowledge orientation, learning and synergistic partnering also move to the centre of interest. Without a consideration of the roles strong uncertainty, heterogeneity as well as the historical character of time play in innovation, networks cannot be explained as a self‐organisational persistent phenomenon. The present paper brings together different strands of the new theory of innovation and develops an evolutionary theory of innovation networks.

Classifying technology policy from an evolutionary perspective

Asked for the most important driving forces of economic development, most economists do not hesitate to state, that it is technical progress which is the main source of quanti-ta-ti-ve and qualitative economic development generated in National Systems of Innovation (NSI). To classify and analyze NSIs the con-cepts of mission- and diffusion-oriented policy designs were introduced. Although, we suppose this taxonomy to be well suited to analyze techno-logy policy, it seems to us in its basic formulation somewhat crude, especially with respect to the supposed characteristics to assign a specific innovation system to the one or the other policy design. To surmount these shortcomings we develop a new classificatory scheme building on a questionnaire approach and suggesting four categories to spread out between the tech-nology and the economic side. This scheme allows for deeper insights and more evident com-pa-risons of different NSIs.

Agent-Based Modelling: A Methodology for Neo-Schumpeterian Economics

Modellers have had to wrestle with an unavoidable trade-off between the demand of a general theoretical approach and the descriptive accuracy required to model a particular phenomenon. A new class of simulation models has shown to be well adapted to this challenge, basically by shifting outwards this trade-off: So-called agent-based models (ABMs henceforth) are increasingly used for the modelling of socio-economic developments. Our paper deals with the new requirements for modelling entailed by the necessity to focus on qualitative developments, pattern formation, etc. which is generally highlighted within Neo-Schumpeterian Economics and the possibilities given by ABMs.

Why do Firms Co-operate for Innovation? - A comparison of Austrian and Finnish CIS 3 results

This paper analyses differences in the cooperative behaviour of innovative firms in Finland and Austria. We use data from the third wave of the Community Innovation Survey (CIS3). Descriptive statistics indicate that the rate of innovators is quite similar in Austria and Finland, while the number of cooperating enterprises is considerably higher in Finland. Econometric analysis reveals that a number of factors that determine cooperative arrangements are only significant in one or the other country. We conclude that cooperative behaviour in the two countries is much more dependent on national factors and much deeper rooted in the underlying innovation systems than the existing literature may assume.

Simulating Knowledge Dynamics in Innovation Networks

The competitiveness of firms, regions and countries greatly depends on the generation, dissemination and application of new knowledge. Modern innovation research is challenged by the need to incorporate knowledge generation and dissemination processes into the analysis so as to disentangle the complexity of these dynamic processes. With innovation, however, strong uncertainty, nonlinearities and actor heterogeneity become central factors that are at odds with traditional modeling techniques anchored in equilibrium and homogeneity. This text introduces SKIN (Simulation Knowledge Dynamics in Innovation Networks), an agent-based simulation model that primarily focuses on joint knowledge creation and exchange of knowledge in innovation cooperations and networks. In this context, knowledge is explicitly modeled and not approximated by, for instance, the level of accumulated R&D investment. The SKIN approach supports applications in different domains ranging from sector-based research activities in knowledge-intensive industries to the activities of international research consortia engaged in basic and applied research. Following a general description of the SKIN model, several applications and modifications are presented. Each chapter introduces in detail the structure of the model, the relevant methodological considerations and the analysis of simulation results, while options for empirically validating the models structure and outcomes are also discussed. The book considers the scope of further applications and outlines prospects for the development of joint modeling strategies.

What drives the internationalisation of innovation? Evidence from European patent data

This paper analyses the current internationalisation of innovation activities and identifies the main drivers for the countries of the European Union (EU). We employ patent applications at the European Patent Office covering the period 2000–2005. Our results show that the internationalisation of innovation is mainly due to an intensified co-operation between EU member states, as well as stronger ties between Europe and the USA. Innovative activity of EU enterprises is hardly globalised in the sense of being equally distributed around the world. Multivariate analysis reveals that cross-border patents between two countries increase with absolute market size of the host country, with rising levels of research and development in the home and host country and with a stronger protection of intellectual property rights in the host country. Distance between home and host country is negatively related to the number of cross-border patents. A common language between two countries and joint membership in the EU are also factors that considerably spur overseas innovation activity.

SIMULATING KNOWLEDGE-GENERATION AND DISTRIBUTION PROCESSES IN INNOVATION COLLABORATIONS AND NETWORKS

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach allows the representation of heterogenous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.

Informal networking and industrial life cycles

Modern industrial development processes of new technologies are characterized by an increasing complexity and interdependence of different actors combining different knowledge assets. Today, hardly any innovation can be assigned to a specific technological field. Also, the sciences are becoming increasingly differentiated and specialized, thus enhancing the necessity of horizontal and vertical knowledge transfer between the actors of innovation processes. In this context, where single actors and even single firms are unable to keep pace with technological progress, the access to external knowledge sources via informal know-how exchange networks increasingly gains in importance. In the paper a synergetic modelling framework of the evolution of informal networks is combined with time patterns of the industrial evolution sketched by the theory of industry life cycles. Integrating timeindependent transition rates excludes the possibility of analytical solutions, so numerical simulation experiments have to be performed. The results of these experiments show structural developments at least qualitatively according to the predictions of life cycle theory. Most unexpectedly - from a traditional point of view - large informal networks as a potential source of technological spillovers can be observed in a state of the industry life cycle where R&D endeavours of firms are assumed to be already concentrated on exploiting scale economies and process technologies and where intuitively a strategy of keeping new know-how secret would be expected.

Mapping National Innovation Systems in the OECD Area

The purpose of this paper is to present new findings about the structure and the organization of innovative activities in selected OECD countries. By using the approach of national sys-tems of innovation as a conceptual framework and by applying multivariate data analysis techniques, this paper aims to add new insights into the specific structures of the eighteen national systems of innovation under study. A central result from this comparative study is a categorisation of national systems of innovation into different clusters, with each cluster rep-resenting distinctive cross-national structural similarities. By accounting for sectoral specif-ics, the commonly taken perspective on national innovation systems is extended. Thereby, a more precise picture of the structural composition of the analyzed national innovation sys-tems accrues. Also, a new linkage between the two approaches of national and sectoral inno-vation systems is created.

The evolution of R&D networking in the biotech industries

The paper deals with the evolution of R&D networks in the biopharmaceutical industries. For this purpose, an agent-based-computational model towards evolutionary economics is developed which is able to generate results which closely correspond to developments in the real world. With this approach, the permanent nature of R&D networks in the industrial organisation of innovation processes in this increasingly important industry as well as the coexistence of large diversified firms and dedicated biotechnology firms, can be explained.