What is it about the structure and organisation of science and technology that has led to the spectacularly successful growth of knowledge during this century? This book explores this important and much debated question in an innovative way, by using computer simulations. The computer simulation of societies and social processes is a methodology which is rapidly becoming recognised for its potential in the social sciences. This book applies the tools of simulation systematically to a specific domain: science and technology studies. The book shows how computer simulation can be applied both to questions in the history and philosophy of science and to issues of concern to sociologists of science and technology. Chapters in the book demonstrate the use of simulation for clarifying the notion of creativity and for understanding the logical processes employed by eminent scientists to make their discoveries. The book begins with three introductory chapters. The first introduces simulation for the social sciences, surveying current work and explaining the advantages and pitfalls of this new methodology. The second and third chapters review recent work on theoretical aspects of social simulation, introducing fundamental concepts such as self organisation and complexity and relating these to the simulation of scientific discovery."

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.

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.

This book presents the state of the art in social simulation as presented at the Social Simulation Conference 2019 in Mainz, Germany. It covers the developments in applications and methods of social simulation, addressing societal issues such as socio-ecological systems and policymaking. Methodological issues discussed include large-scale empirical calibration, model sharing and interdisciplinary research, as well as decision-making models, validation and the use of qualitative data in simulation modeling. Research areas covered include archaeology, cognitive science, economics, organization science and social simulation education. This book gives readers insight into the increasing use of social simulation in both its theoretical development and in practical applications such as policymaking whereby modeling and the behavior of complex systems is key. The book appeals to students, researchers and professionals in the various fields.

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 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.

What is it about the structure and organisation of science and technology that has led to the spectacularly successful growth of knowledge during this century? This book explores this important and much debated question in an innovative way, by using computer simulations. The computer simulation of societies and social processes is a methodology which is rapidly becoming recognised for its potential in the social sciences. This book applies the tools of simulation systematically to a specific domain: science and technology studies. The book shows how computer simulation can be applied both to questions in the history and philosophy of science and to issues of concern to sociologists of science and technology. Chapters in the book demonstrate the use of simulation for clarifying the notion of creativity and for understanding the logical processes employed by eminent scientists to make their discoveries. The book begins with three introductory chapters. The first introduces simulation for the social sciences, surveying current work and explaining the advantages and pitfalls of this new methodology. The second and third chapters review recent work on theoretical aspects of social simulation, introducing fundamental concepts such as self organisation and complexity and relating these to the simulation of scientific discovery."

This book presents the state of the art in social simulation as presented at the Social Simulation Conference 2019 in Mainz, Germany. It covers the developments in applications and methods of social simulation, addressing societal issues such as socio-ecological systems and policymaking. Methodological issues discussed include large-scale empirical calibration, model sharing and interdisciplinary research, as well as decision-making models, validation and the use of qualitative data in simulation modeling. Research areas covered include archaeology, cognitive science, economics, organization science and social simulation education. This book gives readers insight into the increasing use of social simulation in both its theoretical development and in practical applications such as policymaking whereby modeling and the behavior of complex systems is key. The book appeals to students, researchers and professionals in the various fields.