Petra Ahrweiler

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.

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.

Computer simulations in science and technology studies

This chapter outlines the history of a growing research community: the “invisible college” (Mullins 1973) of scientists who work on computer simulations in Science and Technology Studies (STS). Their common interest enables at least two possible research areas which are only just emerging.

Agent-Based Modelling of Innovation Networks – The Fairytale of Spillover

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).

The Epistemologies of Social Simulation Research

What is the best method for doing simulation research? This has been the basis of a continuing debate within the social science research community. Resolving it is important if the community is to demonstrate clearly that simulation is an effective method for research in the social sciences. In this paper, we tackle the question from a historical and philosophical perspective. We argue that the debate within social simulation has many connections with the debates that have echoed down the years within the wider social science community about the character of social science knowledge and the appropriate epistemological and methodological assumptions on which social science research should rest.

Computational Modelling of Public Policy: Reflections on Practice

Computational models are increasingly being used to assist in developing, implementing and evaluating public policy. This paper reports on the experience of the authors in designing and using computational models of public policy (‘policy models’, for short). The paper considers the role of computational models in policy making, and some of the challenges that need to be overcome if policy models are to make an effective contribution. It suggests that policy models can have an important place in the policy process because they could allow policy makers to experiment in a virtual world, and have many advantages compared with randomised control trials and policy pilots. The paper then summarises some general lessons that can be extracted from the authors’ experience with policy modelling. These general lessons include the observation that often the main benefit of designing and using a model is that it provides an understanding of the policy domain, rather than the numbers it generates; that care needs to be taken that models are designed at an appropriate level of abstraction; that although appropriate data for calibration and validation may sometimes be in short supply, modelling is often still valuable; that modelling collaboratively and involving a range of stakeholders from the outset increases the likelihood that the model will be used and will be fit for purpose; that attention needs to be paid to effective communication between modellers and stakeholders; and that modelling for public policy involves ethical issues that need careful consideration. The paper concludes that policy modelling will continue to grow in importance as a component of public policy making processes, but if its potential is to be fully realised, there will need to be a melding of the cultures of computational modelling and policy making.

Modelling innovation networks of general purpose technologies: the case of nanotechnology

With the emergence of nanotechnology a new General Purpose Technology is shaping the evolution of many economies. Knowledge intensive industries such as nanotechnology evolve in innovation networks consisting of various actors. With their wide ranging applicability innovation networks of General Purpose Technologies differ greatly from other innovation networks. Based on the multiagent simulation model “Simulating Innovation Networks in Knowledge Intensive Industries” we propose a framework to model and simulate the emergence of General Purpose innovation networks.

Die Simulation von Lernen in Innovationsnetzwerken

In diesem Beitrag stellen wir ein Multiagenten-System vor, welches das Lernverhalten von Akteuren in sogenannten Innovationsnetzwerken simuliert (vgl. Gilbert/Pyka/Ahrweiler 2003). Die Akteure, in unserem Modell meist Firmen, versuchen, in Auseinandersetzung mit sich standig andernden technologischen und okonomischen Umweltanforderungen ihre Innovationsleistungen zu optimieren. Dabei kann ein einzelnes Unternehmen externe Ressourcen nutzen, welche allerdings oft auch Konkurrenten zur Verfugung stehen, sowie auf interne Ressourcen zuruckgreifen, die jedoch in ihrer Verteilung auch bei den Konkurrenten zu finden sind. Hier kommt es fur die einzelne Firma darauf an, die zur Verfugung stehenden Ressourcen kreativer und intelligenter zu nutzen, als es den Konkurrenten moglich ist. Um solche Konkurrenzvorteile zu erhalten, mussen Firmen standig lernbereit sein und zwischen verschiedenen Moglichkeiten, sich in Lernaktivitaten zu engagieren, wahlen.

SiSiFOS — simulating studies on the internal formation and the organization of science

Applying “social simulation” to the field of “social studies of science” is commonly thought to be a contradiction in terms2. Simulations have to rely on adequate “representations” of their targets; but this very reliability has been dismissed by social constructivism. This dismissal includes advice on how to avoid shortsighted perspectives: “Any study of mathematics, calculations, theories and forms in general should […] look at how observers move in space and time, how the mobility, stability and combinability of inscriptions are enhanced, how the networks are extended, how all the information is tied together in a cascade of rerepresentation” (Latour 1987: 246f). For social constructivists there is no way to model these overlapping processes of continuous composition and de-composition, of differentiation and fusion. What really happens cannot be objectified in modelling “objects” and the influence of society on them: “there is no pure object that first comes to the attention of the AI (Artificial Intelligence) manager […] and then is brought to the sociological manager for prioritising and dissemination. The sociological agency initiates and ‘runs’ the program” (Brannigan 1989: 606). The only software package which social studies of science would accept is the non-computational and implicit creation mode of society itself.