Applied Evolutionary Economics

Applied Evolutionary Economics

New Empirical Methods and Simulation Techniques

Edited by Pier Paolo Saviotti

The expert contributors to this book examine recent developments in empirical methods and applied simulation in evolutionary economics. Using examples of innovation and technology in industry, it is the first book to address the following questions in a systematic manner: Can evolutionary economics use the same empirical methods as other research traditions in economics?; Is there a need for empirical methods appropriate to the subject matter chosen?; What is the relationship between appreciative theorising, case studies and more structured empirical methods?; and What is the relationship of modelling and simulation to empirical analysis?

Chapter 12: Selection and the Learning Curve

P.A. Geroski and M. Mazzucato

Subjects: economics and finance, evolutionary economics


P.A. Geroski and M. Mazzucato INTRODUCTION Evolutionary models of industrial dynamics differ from neo-classical models in two main respects. First, the focus is on the mechanisms that create differences between firms: in an evolutionary setting, the existence of representative average agents would imply no change. Replicator dynamics, where change is determined by the degree to which agents differ from the average agent (the representative agent), is an example of this perspective – as is Schumpeter’s account of ‘creative destruction’. Second, in evolutionary models competition between firms and/or technologies does not necessarily lead to the ‘survival of the fittest’. Which firms and/or technologies survive and grow is a result of a complex process, often characterized by increasing returns to scale and other types of positive feedback mechanisms.1 It is this second aspect of evolutionary models – competitive selection between firms – that is the focus of this chapter. We look at two aspects of selection that have not received much attention in the rich evolutionary literature on the subject: the degree to which selection is ‘myopic’ and the degree to which it is determined ‘endogenously’. Studies addressing reasons why best practice techniques do not always become dominant have focused on the role of positive feedback and network externalities in causing inefficient techniques to get ‘locked into’ (Arthur, 1989; David, 1985). This occurs due to the processes which block selection from rewarding higher fitness. The first aspect of selection that we study here looks at another angle of this issue: what...

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