Turbulent markets and the complex dynamics of science and technology create an uncertain environment for the development of innovations necessary for company competitiveness. Go-it-alone’ strategies are becoming more and more risky in the current climate.
Innovation networks aims to reduce this uncertainty through the co-operation of government, universities and companies. They are a new and increasingly important model for generating new ideas. They bring together, in a formal partnership, the multidisciplinary knowledge that is needed for the creation and development of new products. A computer industry innovation, for example, may involve solid-state physics to mathematics, and language theory to management science.
Economic success depends on this new form of co-operation. Yet little is known about innovation networks. Open questions include: What are the different types of innovation networks and their characteristics compared to classical forms of organisation? What is their structure, coordination mechanisms and the dynamics of how they work? How do they react to changes in their environment? What are the implications of these findings for policy-makers and managers of innovation?
The SEIN project set out to answer these questions. It combined a number of disciplines – science policy studies, evolutionary economics, sociology and computer science.
Its specific objectives were:
SEIN started with an analysis of existing innovation networks in four fields – biotechnology, mobile communication, energy technology and e-commerce.
Using the case study findings, it developed a comparative model to identify common patterns in innovation networks. It then went on to develop a computer simulation model – the General Simulation Model (GenSEIN) – to analyse the possible impact of each actor’s behaviour and the influence of changes in the networks’ environment on performance.
Each actor is represented by an ‘intelligent agent’ whose attributes include autonomy, ability to interact with others, reactivity to signals from the environment, and an ability to engage proactively in goal-directed behaviour. Each actor is given a ‘kene’, a structured collection of technological, political, social and economic capabilities. Kenes change as actors acquire knowledge from other actors and as they refine their knowledge through research and development.
Using the knowledge represented in their kenes, actors produce artefacts, such as a new design or drug, amounting to potential innovations. An ‘innovation oracle’ then identifies the artefacts that will become innovations, i.e. successful new products and processes, and rewards the actors. The oracle maps the artefacts on an ‘innovation landscape’. Successful innovations deform the landscape so that the reward for a second identical artefact is reduced reflecting the real world.
The project also used GenSEIN to develop a new evaluation tool, which concentrates on the comparison of innovation networks and their performance instead of their output.
SEIN made a number of policy relevant conclusions:
SEIN shows that the network model may be appropriate for describing innovation processes in many different fields. But it urged caution in adopting a single model. It recommends a differentiated approach to innovation networks in European RTD. Centres of scientific excellence, for example, will require a different model than networks of applied industrial research or networks of knowledge production with a high degree of public involvement.
SEIN set up a website (http://www.uni-bielefeld.de/iwt/sein/), published a newsletter and 15 project papers, produced a literature list on innovation research and networks, and organised four workshops and a closing conference.
|Full title||Simulating Self-Organising Innovation Networks (SEIN)|
|Keywords||networks, innovation, research, S&T knowledge, simulation|
|Main contractor||University of Bielefeld|
|Institute for Science and Technology Studies|
|Phone: +49 521 106 4674|
|Fax.: +49 521 106 6418|
|Scientific Coor.||Kuppers Gunter|
|EC Contribution||€670 000|
|EC Scientific Officer||M. Carvalho-Dias|
|Final Report||Download PDF - [149 Kb]|