Joining Complexity Science And Social Simulation For Innovation Policy

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.

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.

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.

Innovation is nowadays a question of life and death for many of the economies of the western world. Yet, due to our generally reductionist scientific paradigm, invention and innovation are rarely studied scientifically. Most work prefers to study its context and its consequences. As a result, we are as a society, lacking the scientific tools to understand, improve or otherwise impact on the processes of invention and innovation. This book delves deeply into that topic, taking the position that the complex systems approach, with its emphasis on ‘emergence’, is better suited than our traditional approach to the phenomenon. In a collection of very coherent papers, which are the result of an EU-funded four year international research team’s effort, it addresses various aspect of the topic from different disciplinary angles. One of the main emphases is the need, in the social sciences, to move away from neo-darwinist ‘population thinking’ to ‘organization thinking’ if we want to understand social evolution. Another main emphasis is on developing a generative approach to invention and innovation, looking in detail at the contexts within which invention and innovation occur, and how these contexts impact on the chances for success or failure. Throughout, the book is infused with interesting new insights, but also presents several well-elaborated case studies that connect the ideas with a substantive body of ‘real world’ information.

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.

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.

“Social Simulation for a Digital Society” provides a cross-section of state-of-the-art research in social simulation and computational social science. With the availability of big data and faster computing power, the social sciences are undergoing a tremendous transformation. Research in computational social sciences has received considerable attention in the last few years, with advances in a wide range of methodologies and applications. Areas of application of computational methods range from the study of opinion and information dynamics in social networks, the formal modeling of resource use, the study of social conflict and cooperation to the development of cognitive models for social simulation and many more. This volume is based on the Social Simulation Conference of 2017 in Dublin and includes applications from across the social sciences, providing the reader with a demonstration of the highly versatile research in social simulation, with a particular focus on public policy relevance in a digital society. Chapters in the book include contributions to the methodology of simulation-based research, theoretical and philosophical considerations, as well as applied work. This book will appeal to students and researchers in the field.

This book provides a comprehensive approach to the study of policy analytics, modelling and informatics. It includes theories and concepts for understanding tools and techniques used by governments seeking to improve decision making through the use of technology, data, modelling, and other analytics, and provides relevant case studies and practical recommendations. Governments around the world face policy issues that require strategies and solutions using new technologies, new access to data and new analytical tools and techniques such as computer simulation, geographic information systems, and social network analysis for the successful implementation of public policy and government programs. Chapters include cases, concepts, methodologies, theories, experiences, and practical recommendations on data analytics and modelling for public policy and practice, and addresses a diversity of data tools, applied to different policy stages in several contexts, and levels and branches of government. This book will be of interest of researchers, students, and practitioners in e-government, public policy, public administration, policy analytics and policy informatics.

This handbook presents the state of the art of quantitative methods and models to understand and assess the science and technology system. Focusing on various aspects of the development and application of indicators derived from data on scholarly publications, patents and electronic communications, the individual chapters, written by leading experts, discuss theoretical and methodological issues, illustrate applications, highlight their policy context and relevance, and point to future research directions. A substantial portion of the book is dedicated to detailed descriptions and analyses of data sources, presenting both traditional and advanced approaches. It addresses the main bibliographic metrics and indexes, such as the journal impact factor and the h-index, as well as altmetric and webometric indicators and science mapping techniques on different levels of aggregation and in the context of their value for the assessment of research performance as well as their impact on research policy and society. It also presents and critically discusses various national research evaluation systems. Complementing the sections reflecting on the science system, the technology section includes multiple chapters that explain different aspects of patent statistics, patent classification and database search methods to retrieve patent-related information. In addition, it examines the relevance of trademarks and standards as additional technological indicators. The Springer Handbook of Science and Technology Indicators is an invaluable resource for practitioners, scientists and policy makers wanting a systematic and thorough analysis of the potential and limitations of the various approaches to assess research and research performance.

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.