SKIN - Simulating Knowledge Dynamics in Innovation Networks

Events

Past, present and future events related to the SKIN Model

1st SKIN workshop

University of Koblenz, Germany, March 31 – April 1, 2011

2nd SKIN workshop

University of Koblenz, Germany May 31 – June 1, 2012

3rd SKIN workshop

Joining Complexity Science and Social Simulation for Policy A workshop at Eötvös Loránd University, Budapest, Hungary, 22-23 May, 2014

4th SKIN Workshop

SKIN 4 Workshop and SKIN Summer School Federico II University, Naples, Italy, 19–22 May 2015

Social Simulation Conference 2016:

A session organised by the ESSA Special Interest Group (SIG) “Policy Modelling“

SKIN has been used in many studies, by scholars all over the world.

About SKIN

Simulating Knowledge Dynamics in Innovation Networks
SKIN is an agent-based model to simulate the behaviour of innovation networks in complex social systems

Learning about innovation processes and networks
Using conceptual models based on robust empirical studies, SKIN is the ideal platform for learning about different processes for creating, transferring and distributing knowledge, collaborating for innovation, models of innovation networks and governance of these processes, collaborations and networks.

Applying ABM to real-world policy contexts
Developed in European studies with case studies in different technological and institutional contexts, SKIN is one of the leading platforms for applying agent-based modelling (ABM) to innovation networks found in a variety of different, real world contexts.

Combining innovation research methods
Add SKIN to the innovation policy-making toolbox. The mix of traditional analytical methods and the powerful SKIN approach, combining robust empirical studies, computational network analysis and ABM, allows for cross-fertilization between disciplines.

Testing innovation policies in advance
Test policy to have the best chance of achieving the desired effect. SKIN allows policy- and decision-makers to test their ideas and initiatives in advance. They can identify possible scenarios changing the structure of innovation networks and examine the unexpected effects.

People

Some of those who have contributed to the development of SKIN
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Petra Ahrweiler

EA European Academy
E-Mail: petra.ahrweiler@ea-aw.de Internet: http://www.ea-aw.de
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Nigel Gilbert

University of Surrey, UK
Prof. Nigel Gilbert Centre for Research in Social Simulation Department of Sociology Faculty of Arts and Human Sciences University of Surrey Guildford Surrey GU2 7XH Tel: +44(0)1483-683762 Fax: +44(0)1483-689551 E-Mail: n.gilbert@surrey.ac.uk Internet: http://www.soc.surrey.ac.uk/staff/ngilbert/index.html
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Andreas Pyka

Universität Hohenheim
Universität Hohenheim Lehrstuhl für Innovationsökonomik (520I) Wollgrasweg 23 D-70599 Stuttgart
Tel: +49 711 459 24481 Fax: +49 711 459 24488 E-Mail: a.pyka@uni-hohenheim.de Internet: http://www.inno.uni-hohenheim.de
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Michel Schilperoord

EA European Academy
Senior Researcher EA European Academy GmbH Wilhelmstraße 56 53474 Bad Neuenahr-Ahrweiler +49 (0) 26 41 973-310 +49 (0) 26 41 973-320 Email: Michel.Schilperoord@ea-aw.de
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Benjamin Schrempf

EA European Academy
Dipl.-oec. Benjamin Schrempf EA European Academy GmbH Wilhelmstraße 56 53474 Bad Neuenahr-Ahrweiler
+49 (0) 26 41 973-307 +49 (0) 26 41 973-320 Email: Benjamin.Schrempf@ea-aw.de

SKIN (Simulating Knowledge Dynamics in Innovation Networks) is a multi-agent model of innovation networks in knowledge-intensive industries grounded in empirical research and theoretical frameworks from innovation economics and economic sociology.
The agents represent innovative firms who try to sell their innovations to other agents and end users but who also have to buy raw materials or more sophisticated inputs from other agents (or material suppliers) in order to produce their outputs. This basic model of a market is extended with a representation of the knowledge dynamics in and between the firms. Each firm tries to improve its innovation performance and its sales by improving its knowledge base through adaptation to user needs, incremental or radical learning, and co-operation and networking with other agents.
SKIN is written using NetLogo 5.1.

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