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 co‐operations 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.

Innovation contributes to corporate competitiveness, economic performance and environmental sustainability. In the Internet era, innovation intelligence is transferred across borders and languages at an unprecedented rate, yet the ability to benefit from it seems to become more divergent among different corporations and countries. How much an organization can benefit from innovation largely depends on how well innovation is managed in it. Thus, there is a discernible increase in interest in the study of innovation management. This handbook provides a comprehensive guide to this subject. The handbook introduces the basic framework of innovation and innovation management. It also presents innovation management from the perspectives of strategy, organization and resource, as well as institution and culture. The book’s comprehensive coverage on all areas of innovation management makes this a very useful reference for anyone interested in the subject. Chapter 5 of this book is freely available as a downloadable Open Access PDF under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license available at http://www.taylorfrancis.com/books/9781315276670

This book brings together original research on the role of networks in regional economic development and innovation. It presents a comprehensive framework synthesizing extant theories, a palette of real-world cases in the aerospace, automotive, life science, biotechnology and health care industries, and fundamental agent-based computer models elucidating the relation between regional development and network dynamics. The book is primarily intended for researchers in the fields of innovation economics and evolutionary economic geography, and particularly those interested in using agent-based models and empirical case studies. However, it also targets (regional) innovation policy makers who are not only interested in policy recommendations, but also want to understand the state-of-the-art agent-based modeling methods used to experimentally arrive at said recommendations.

The science of graphs and networks is now an established tool for modeling and analyzing systems with a large number of interacting components. The contributions to this anthology address different aspects of the relationship between innovation and networks.

This book explores how complexity science and social simulation can be used to improve and inform policy-making in both research and innovation. Beginning with an introduction to conceptual definitions of complexity science and social simulation, the book demonstrates the validity of the underlying integrated research framework used throughout. It is then divided into two parts, with the first investigating the effects and impacts of policy making on the structure, composition and outputs of research and innovation networks using the agent-based SKIN platform (Simulating Knowledge Dynamics in Innovation Networks, http://cress.soc.surrey.ac.uk/SKIN/). The second half of the book discusses a research initiative funded by the Irish government focusing on innovation policy simulation for economic recovery. This consists of empirical research on Irish research and innovation networks, and SKIN-based simulations of technology transfer issues and the commercialization of research in areas with high potential for innovation and economic growth. The book concludes with reflections on the maturity and utility of an approach combining complexity science and social simulation for research and innovation policy. Joining Complexity Science and Social Simulation for Innovation Policy will be of particular interest to scientists concerned with innovation and complex systems, including economists, sociologists, and complexity researchers, as well as students and practitioners, such as innovation policymakers and innovation business managers.

Technological innovation is fundamental to firm performance and economic prosperity. The aim of this book is to contribute to an in-depth understanding of collective innovation processes by analyzing publicly funded R&D cooperation and innovation networks in the German laser industry. Standing in a neo-Schumpeterian tradition, it employs interdisciplinary analytical concepts and draws upon a unique longitudinal dataset from the laser industry that covers more than two decades of observations. In brief, the book makes a valuable contribution by exploring how and why firm-specific R&D cooperation activities and network positions, large-scale network patterns, and evolutionary network change processes affect the innovative performance of laser source manufacturers in Germany.

The explosive growth in data, computational power, and social media creates new opportunities for innovating the processes and solutions of Information and communications technology (ICT) based policy-making and research. To take advantage of these developments in the digital world, new approaches, concepts, instruments and methods are needed to navigate the societal and computational complexity. This requires extensive interdisciplinary knowledge of public administration, policy analyses, information systems, complex systems and computer science. This book provides the foundation for this new interdisciplinary field, in which various traditional disciplines are blending. Both policy makers, executors and those in charge of policy implementations acknowledge that ICT is becoming more important and is changing the policy-making process, resulting in a next generation policy-making based on ICT support. Web 2.0 and even Web 3.0 point to the specific applications of social networks, semantically enriched and linked data, whereas policy-making has also to do with the use of the vast amount of data, predictions and forecasts, and improving the outcomes of policy-making, which is confronted with an increasing complexity and uncertainty of the outcomes. The field of policy-making is changing and driven by developments like open data, computational methods for processing data, opining mining, simulation and visualization of rich data sets, all combined with public engagement, social media and participatory tools.

This book explores the question of whether and how meme theory or “memetics” can be fruitfully utilized in evolutionary economics and proposes an approach known as “economemetics” which is a combination of meme theory and complexity theory that has the potential to combat the fragmentation of evolutionary economics while re-connecting the field with cultural evolutionary theory. By studying the intersection of cultural and economic evolution, complexity economics, computational economics, and network science, the authors establish a connection between memetics and evolutionary economics at different levels of investigation. The book first demonstrates how a memetic approach to economic evolution can help to reveal links and build bridges between different but complementary concepts in evolutionary economics. Secondly, it shows how organizational memetics can help to capture the complexity of organizational culture using meme mapping. Thirdly, it presents an agent-based simulation model of knowledge diffusion and assimilation in innovation networks from a memetic perspective. The authors then use agent-based modeling and social network analysis to evaluate the diffusion pattern of the Ice Bucket Challenge as an example of a “viral meme.” Lastly, the book discusses the central issues of agency, creativity, and normativity in the context of economemetics and suggests promising avenues for further research.