This book addresses the role of cooperation, networks and institutions in the context of regional innovation systems. It emphasises the importance of these factors in the emergence of local innovation systems, using detailed examples of clusters which have reached different stages of maturity. The authors address the topic from an empirical, theoretical and political perspective, and highlight the local mechanisms which are involved in the development of innovation systems. They offer a comprehensive overview of different approaches in the field and present numerous case studies which stress the influence of networks and local institutions. Significantly, they also introduce several new approaches to regional innovation systems, including contributions which explicitly discuss the design and potential of policy measures to promote regional development. The policy recommendations are based on sound theorising which, in turn, is based on extensive empirical research. This book is a valuable addition to a complex and growing literature which offers new perspectives and insights on cooperation, networks and institutions, and their role in the development of local systems of innovation. The combination of empirical, theoretical and policy-oriented approaches will ensure this book is essential reading for academics and policymakers in the fields of regional economics, innovation research and economic geography.

Local industrial clusters, such as Silicon Valley in the US, have become an important subject of scholarly inquiry. This text offers a unifying view by capturing the general characteristics and prerequisites of local industrial clusters both on a theoretical as well as an empirical level.

Local industrial clusters, such as Silicon Valley in the United States, have become an important subject of scholarly inquiry in recent years. This book offers a unifying view by capturing the general characteristics and prerequisites of local industrial clusters both on a theoretical as well as an empirical level. The book establishes a mathematical model to analyse the dynamics of clustering and the conditions that are to be satisfied if a local industrial cluster is to evolve. This model allows predictions about the spatial distribution of firms to be deducted, which are empirically tested in the book. This thorough methodology allows the author to postulate upon whether the number of local clusters that emerge in an industry is random, or whether it is predetermined. An impressive scholarly exercise, this book also contains important policy lessons. As such, Local Industrial Clusters will be a valuable read for policy-makers as well as academics.

Computational Techniques for Modelling Learning in Economics offers a critical overview of the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.

Providing an important and timely overview of research on the exciting area of entrepreneurship in biotechnology, The Handbook of Bioentrepreneurship examines one of the most promising industries of the 21st century. While genetically engineered food and biopharmaceuticals have made biotechnology part of our everyday life, starting a bioventure is among the most complex and risky entrepreneurial tasks given long development cycles, high technological and market uncertainty, and high capital intensity. Providing unparalleled in-depth and detailed analysis, this Handbook sheds light on business models and strategies, financing, cooperation networks between firms and universities, among other issues. With new developments in biotechnology increasingly in the news, this is an important source for readers interested in public policy, entrepreneurship, and business in the 21st century.

Providing an important and timely overview of research on the exciting area of entrepreneurship in biotechnology, The Handbook of Bioentrepreneurship examines one of the most promising industries of the 21st century. While genetically engineered food and biopharmaceuticals have made biotechnology part of our everyday life, starting a bioventure is among the most complex and risky entrepreneurial tasks given long development cycles, high technological and market uncertainty, and high capital intensity. Providing unparalleled in-depth and detailed analysis, this Handbook sheds light on business models and strategies, financing, cooperation networks between firms and universities, among other issues. With new developments in biotechnology increasingly in the news, this is an important source for readers interested in public policy, entrepreneurship, and business in the 21st century.

Computational Techniques for Modelling Learning in Economics offers a critical overview of the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.

Modelling Learning in Economics provides a comprehensive study of how learning processes can be modelled in an economic context. This innovative book investigates the processes involved in economic learning by categorizing different ways of learning, and using mathematical models for their description. The author distinguishes three types of learning processes - non-cognitive, routine-based and associative learning - and, for each of these, a model is proposed. Thomas Brenner also provides an overview of the psychological literature on learning and on the learning models most frequently used in economics. He then goes on to present applications of these models of learning to various economic topics including evolutionary game theory, consumption behaviour, investment behaviour of savers and the diffusion of innovation. In applying the models to economic topics, the author not only presents new insights into learning but also contributes to many topics within economic research. This book will be of interest to economic psychologists and economists working in the areas of game theory, consumer and investment behaviour and the diffusion of innovation.