New Empirical Methods and Simulation Techniques
Edited by Pier Paolo Saviotti
12. Selection and the learning curve 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...
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