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Manchester Institute of Innovation Research, University of Manchester
In the simulation models surveyed by the authors, innovation is understood in a broad sense as the generation and diffusion of new ideas, knowledge, technologies, strategies, practices, and/or organisational routines. The book is structured in six core chapters with an excellent and comprehensive introductory chapter on complexity science and complex adaptive systems theory, and how the study of innovation fits within this conceptual framework. Chapter 2 presents epidemic, probit, stock and evolutionary models of the diffusion of innovations, and compares three different simulation approaches namely system dynamics, discrete-event simulation and agent based modelling. Chapter 3 centres on epidemic type models and the effect that the properties of the network of social interactions of the agents involved in innovative activities have on the diffusion of innovations. Models of collective problem-solving and organisational learning are the focus of Chapter 4 which shows how the behavioural practices and the social network structure of bounded rational individuals influence organisations’ innovative ability. In this chapter particular attention is given to the effects that exploration and exploitation activities as defined by March (1991) have in the long term performance of organisations. Chapter 5 covers models of scientists generating and diffusing new ideas and knowledge through academic publications. Drawing upon actor network theory and the social construction of technology, Chapter 6 presents models that represent innovation as a dynamic process that satisfies a variety of physical, socio-economic and political contextual constraints. Finally, simulation models of complex technological evolution and knowledge creation are surveyed in Chapter 7.
The 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. The authors present the main models’ assumptions, purposes and simulation results together with useful comparisons and an assessment of their strengths and limitations. They also discuss potential improvements and provide research directions which contribute to further simulation based advances in innovation research. The discussion is carried out in a clear and concise style with only the strictly necessary technical details to understand the models’ implementation and simulation which will appeal to both experts and non-experts modellers. In addition, readers can download the programs used for the simulations to run and replicate the models from the book’s accompanying website. The book is therefore not only an excellent read for academics interested in computer models of innovation but also a highly valuable tool for lecturers, policy makers, managers, and in general anyone with an interest in understanding and advancing research in the innovation field through the use of simulation modelling.
It is worth noting that although the authors do not explicitly include introductory concepts on simulation modelling and/or its general use in the social sciences (they do however provide key references on the topic), throughout the book the readers will find highly useful insights into the value of computer simulation as a tool for thinking and explaining complex phenomena in the socio-economic realm, particularly when combined with both empirical studies and other quantitative and qualitative approaches. Innovation is probably one of the most complex amongst these phenomena. Its very essence is novelty and surprise, uncertainty and ignorance are its most pervasive features. In order to improve our understanding of innovation, its origins, and its impacts one should make use of all the tools at one's disposal. This book clearly shows how computer simulation modelling holds an extremely promising role in this task.
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