Simulating Innovation: Computer-based Tools for Rethinking Innovation

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ISBN 10: 1849801606 / ISBN 13: 9781849801607

Published by Edward Elgar Publishing, 2014

About this Item

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Bibliographic Details

• Title: Simulating Innovation: Computer-based Tools for Rethinking Innovation
• Publisher: Edward Elgar Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 1849801606
• ISBN 13: 9781849801607
• Binding: Hardcover
• Condition: Brand New

About this title

Synopsis

This book brings together computer models and simulation approaches that allow the investigation of a wide range of innovation related issues, and hence will be of interest for academics and researchers from a variety of innovation related disciplines.'
- Mercedes Bleda, Journal of Artificial Societies and Social Simulation

Christopher Watts and Nigel Gilbert explore the generation, diffusion and impact of innovations, which can now be studied using computer simulations.

Agent-based simulation models can be used to explain the innovation that emerges from interactions among complex, adaptive, diverse networks of firms, people, technologies, practices and resources. This book provides a critical review of recent advances in agent-based modeling and other forms of the simulation of innovation. Elements explored include: diffusion of innovations, social networks, organizational learning, science models, adopting and adapting, and technological evolution and innovation networks. Many of the models featured in the book can be downloaded from the book's accompanying website.

Bringing together simulation models from several innovation-related fields, this book will prove a fascinating read for academics and researchers in a wide range of disciplines, including: innovation studies, evolutionary economics, complexity science, organization studies, social networks, and science and technology studies. Scholars and researchers in the areas of computer science, operational research and management science will also be interested in the uses of simulation models to improve the understanding of organization.

About the Author

Christopher Watts, formerly Ludwig-Maximilians University, Munich, Germany and Nigel Gilbert, University of Surrey, UK