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.

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.

Agent-based simulation has become increasingly popular as a modeling approach in the social sciences because it enables researchers to build models where individual entities and their interactions are directly represented. The Second Edition of Nigel Gilbert′s Agent-Based Models introduces this technique; considers a range of methodological and theoretical issues; shows how to design an agent-based model, with a simple example; offers some practical advice about developing, verifying and validating agent-based models; and finally discusses how to plan an agent-based modelling project, publish the results and apply agent-based modeling to formulate and evaluate social and economic policies.

Migration is conceived differently in Europe compared with countries like the US, Canada or Australia. International Innovation Networks and Knowledge Migration confronts traditional views on migration with modern theories of brain circulation and innovation networks, showing that migration leads to mutual benefits for both the home and host countries This new volume brings together several case studies and empirical in-depth analyses which are constructed from the strong migration relationship between Turkey and Germany that has existed for more than 50 years. Bringing together over 20 international contributors, this book highlights that knowledge migration and cultural diversity can strongly stimulate entrepreneurial activities, competence acquisition and economic development of countries and regions. The authors highlight the considerable scope for improvement of European migration policies in order to be better prepared to successfully process structural changes stemming from an aging society in Europe, and an increasing international division of labour. This volume is suitable for those who study industrial economics, international economics and European economics. It is also of interest to those who want to delve deeper into the Turkish-German migration nexus.

Innovation is the creation of new, technologically feasible, commercially realisable products and processes and, if things go right, it emerges from the ongoing interaction of innovative organisations such as universities, research institutes, firms, government agencies and venture capitalists. Innovation in Complex Social Systems uses a "hard science" approach to examine innovation in a new way. Its contributors come from a wide variety of backgrounds, including social and natural sciences, computer science, and mathematics. Using cutting-edge methodology, they deal with the complex aspects of socio-economic innovation processes. Its approach opens up a new paradigm for innovation research, making innovation understandable and tractable using tools such as computational network analysis and agent-based simulation. This book of new work combines empirical analysis with a discussion of the tools and methods used to successfully investigate innovation from a range of international experts, and will be of interest to postgraduate students and scholars in economics, social science, innovation research and complexity science.

There is no doubt that behavioral economics is becoming a dominant lens through which we think about economics. Behavioral economics is not a single school of thought but representative of a range of approaches, and uniquely, this volume presents an overview of them. The wide spectrum of international contributors each provides an exploration of a central approach, aspect or topic in behavorial economics. Taken together, the whole volume provides a comprehensive overview of the subject which considers both key developments and future possibilities. Part One presents several different approaches to behavioural economics, including George Katona, Ken Boulding, Harvey Leibenstein, Vernon Smith, Herbert Simon, Gerd Gigerenzer, Daniel Kahneman, and Richard Thaler. This section looks at the origins and development of behavioral economics and compares and contrasts the work of these scholars who have been so influential in making this area so prominent. Part Two presents applications of behavioural economics including nudging; heuristics; emotions and morality; behavioural political economy, education, and economic innovation. The Routledge Handbook of Behavioral Economics is ideal for advanced economics students and faculty who are looking for a complete state-of-the-art overview of this dynamic field.

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.

Companies are constantly faced with the need to grow and advance in order to compete with other corporations. The implementation of computer innovations allows for smoother transitions to adaptive changes through the use and understanding of analytical tools. Modeling and Simulation Techniques for Improved Business Processes is a critical scholarly resource that examines the systems currently implemented in companies and how they can be upgraded and advanced through various computer design methods. Featuring coverage of a broad range of topics including scenario planning, casual modeling, and system dynamics, this publication is targeted toward researchers, professionals, and engineers searching for current research on corporate innovations created through computer design methods.