Edited by Dominique Foray
Chapter 4: Increase Learning, Break Knowledge Lock-ins and Foster Dynamic Complementarities: Evolutionary and System Perspectives on Technology Policy in Industrial Dynamics
4. Increase learning, break knowledge lock-ins and foster dynamic complementarities: evolutionary and system perspectives on technology policy in industrial dynamics Franco Malerba INTRODUCTION 4.1 Which is the basic rationale for technology policy put forward by evolutionary theory and the innovation system perspective? In this chapter, I will focus on industrial dynamics and I will concentrate on the basic points of the two approaches. Given the limited space, I will not discuss the market failure approach, already done very effectively by Metcalfe (1995). I will discuss mainly technology policy, although innovation policies will also be examined. Evolutionary theory emphasizes learning, problem-solving activities and the competences of heterogeneous actors in uncertain and changing environments. The innovation system literature highlights interdependencies and complementarities among a wide set of different agents. Both approaches have several common aspects: the relevant effects of institutions on innovation, the differences in the sectoral and technological contexts and the role of interaction among heterogeneous actors in affecting the rate of technological change. In terms of public policies, evolutionary theory and the innovation system approach concentrate on dynamics, processes, relationships and interdependencies. The focus is on four key dimensions: why intervene, how, where and when. The why and how to intervene are also basic issues of the traditional approach to public policy. In an evolutionary and system view when to intervene (the timing) and where (in which part of the system) become crucial for policy. In fact policy intervention could take place too soon or too late. And policy needs...
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