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2009 | Buch

Introduction: Network Perspectives on Innovations: Innovative Networks – Network Innovation Kapitel lesen Erstes Kapitel lesen

Innovation Networks

New Approaches in Modelling and Analyzing

herausgegeben von: Prof. Dr. Andreas Pyka, Dr. Andrea Scharnhorst

Verlag: Springer Berlin Heidelberg

Buchreihe : Understanding Complex Systems

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction: Network Perspectives on Innovations: Innovative Networks – Network Innovation
Abstract
The idea for this book started when we organized a topical workshop entitled “Innovation Networks – New Approaches in Modeling and Analyzing” (held in Augsburg, Germany in October 2005), under the auspices of Exystence, a network of excellence funded in the European Union’s Fifth Framework Program. Unlike other conferences on innovation and networks, however, this workshop brought together scientists from economics, sociology, communication science, science and technology studies, and physics. With this book we aim to build further on a bridge connecting the bodies of knowledge on networks in economics, the social sciences and, more recently, statistical physics.
Andreas Pyka, Andrea Scharnhorst

Innovation Networks in Economics

Frontmatter
Chapter 2. Knowledge Networks: Structure and Dynamics
Abstract
There has been recently an upsurge of interest in networks in a literature which belongs to many different disciplines, ranging from physics to biology to the social sciences. Interestingly enough, it seems that in spite of the wide differences between the entities constituting such networks, ranging from the interactions of biological molecules in cells to the Internet to citations (Barabasi et al., 1999, Barabasi, 2002; Barabasi and Bonabeau, 2003; Cohen, 2002; Watts and Strogatz, 1998), most such studies claim some kind of common intellectual framework rooted in complexity science. Yet the literature on networks does not provide any link between networks and other parts of the science of complexity which could be considered more fundamental. This contribution will proceed first to identify some possible connections between networks and other theories of complexity; second, to describe and analyse some networks of knowledge and innovation and to interpret their properties in terms of recent studies of networks; third, to formulate some generalizations about the dynamics of these networks and about their connection to the dynamics of variety and efficiency.
Pier Paolo Saviotti
Chapter 3. Death of Distance in Science? A Gravity Approach to Research Collaboration
Abstract
One of the major transitions in recent scientific research is the rise of network theory motivating a variety∈dexvariety of new research programmes in and across various disciplines. Economic geography∈dexgeography has been no exception. The work on networks in economic geography can be divided into two types of research. First, there are studies on inter-firm networks and their impact on firm performance. For a large part, such studies have been carried out in the context of geographical clusters, which are often characterised by strong network relations (Uzzi, 1997). A second approach, an example of which is presented below, concerns the study of inter-regional networks and their impact on regional growth. Here, the unit of analysis are territories, typically sub-national regions. The interest in this topic stems from Castells (1996) and others who have argued that regional growth increasingly depends on a region’s position in global networks rather than its specific local characteristics such as institutions, endowments and amenities (‘space of flows’ versus the ‘space of places’).
Koen Frenken, Jarno Hoekman, Suzanne Kok, Roderik Ponds, Frank van Oort, Joep van Vliet
Chapter 4. Evolution and Dynamics of Networks in ‘Regional Innovation Systems’ (RIS)
Abstract
In 1992, Cooke introduced the notion of ‘regional innovation system∈dexRegional Innovation Systems’ (RIS) (Cooke 1992),. The notion synthesizes two strands of research: the discussion about ‘national innovation systems’ (NIS) and empirical studies on regional development dynamics.
Frank Beckenbach, Ramón Briegel, Maria Daskalakis
Chapter 5. Agent-Based Modelling of Innovation Networks – The Fairytale of Spillover
Abstract
Today’s knowledge-based economies are more than places where goods and services are bought and sold; they are the sites where complex logistic processes are coordinated, where innovation takes place, where knowledge is generated, communicated, re-combined and exchanged. In such competitive and knowledgeintensive environments characterized by price as well as innovation competition and in which there are quickly changing global technological and economic requirements (Bahlmann, 1990; Hanusch and Pyka, 2007a) and a variety of institutional infrastructures (Amable, 2003; Hanusch and Pyka, 2007b), a firm can improve its performance only by exploiting resources more creatively and intelligently than its competitors (Lam, 2003).
Andreas Pyka, Nigel Gilbert, Petra Ahrweiler
Chapter 6. Structural Holes, Innovation and the Distribution of Ideas
Abstract
This contribution examines the relationship between the architecture of an industrial R&D network and efficiency in knowledge distribution, both from the point of view of individual firm performance, and at the level of the system.
Robin Cowan, Nicolas Jonard

Innovation Networks in Complex Theories

Frontmatter
Chapter 7. Tools from Statistical Physics for the Analysis of Social Networks
Abstract
This article is to introduce the social scientist concerned with social network analysis∈dexSocial Network Analysis and an affinity to quantitative methods to parts of the research done by physicists in the field of complex networks∈dexcomplex networks. In fact, much of the research done by physicists has been inspired by examples and problems from sociology. We believe that the methods developed by natural scientists will prove to be valuable tools that allow new insights into data arising in the social sciences. We hope that these methods find their way back into social sciences and find ample application on the problems by which they were originally inspired.
Jürg Reichardt, Stefan Bornholdt
Chapter 8. Modeling Evolving Innovation Networks
Abstract
Economists widely agree on technological change and innovation being the main components of economic growth [Aghion and Howitt 1998, Tirole 1988]. In the absence of ongoing technological improvements, economic growth can hardly be maintained [Barro and Sala-i Martin 2004]. The close link between innovation and economic performance has become generally accepted. Following this insight, in recent years of economic growth, OECD countries have fostered investments in science, technology, and innovation[OECD 2006].
Michael D. König, Stefano Battiston, Frank Schweitzer
Chapter 9. Propagation of Innovations in Complex Patterns of Interaction
Abstract
In recent times the possibility of using the tools of statistical physics to analyze the rich dynamical behaviors observed in social, technological, and economical systems has attracted a lot of attention from the physics community (Arthur et al. 1997, Mantegna and Stanley 1999, Bouchaud and Potters 2000). So far, one of the main contributions to these fields has been the analysis of simple models that capture the basic features of the investigated phenomena. The goal is to identify the relevant parameters as well as the essential mechanisms governing their dynamics with the hope that this information will help us to understand the physical behavior of real complex systems. A real part of this effort has been devoted to the characterization of real networks, identifying their main features, and understanding how they arise (Watts and Strogatz 1998, Barabasi and Albert 1999, Strogatz 2001, Dorogovtsev and Mendes 2002, Albert and Barabasi 2002).
Albert Diaz-Guilera, Sergio Lozano, Alex Arenas
Chapter 10. Sensitive Networks – Modelling Self-Organization and Innovation Processes in Networks
Abstract
This contribution is devoted to the interdisciplinary theory of self-organization processes, paying particular attention to stochastic effects connected with innovations in network systems. On our understanding “self-organization” is the spontaneous formation of structures (Ebeling and Feistel, 1982, 1994; Feistel and Ebeling, 1989). An “innovation”, on a general system-theoretical understanding, is the appearance of, for example, a new species, a new mode of behaviour, a new technology, a new product or a new idea (Ebeling and Sonntag, 1986; Bruckner et al., 1989, 1990, 1996; Ebeling et al., 1999).
Ingrid Hartmann-Sonntag, Andrea Scharnhorst, Werner Ebeling
Backmatter
Metadaten
Titel
Innovation Networks
herausgegeben von
Prof. Dr. Andreas Pyka
Dr. Andrea Scharnhorst
Copyright-Jahr
2009
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-92267-4
Print ISBN
978-3-540-92266-7
DOI
https://doi.org/10.1007/978-3-540-92267-4

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