Edited by John Foster and Werner Hölzl
Chapter 4: The use of genetic programming in evolutionary economics
Bernd Ebersberger and Andreas Pyka 1. INTRODUCTION Evolutionary economics has recently been labeled as ‘among the most hopeful, and . . . most fruitful, developments in economics’ (Blaug 1998, p. 31). This favorable assessment is based on the new modeling approaches that have largely replaced the ‘social mathematics’ (Blaug 1998, p. 11) that mainstream economic analysis tends to favor. These new modeling approaches in evolutionary economics reﬂect its demand for a new type of modeling (cf. Boulding 1991, p. 51). Among others, they include techniques such as cellular automata,1 neural networks,2 master equation approaches,3 and genetic algorithms and genetic programming.4 In this chapter we will build on the successful introduction of those new modeling techniques and genetic programming in particular which have been applied to various types of modeling tasks in evolutionary economics. We suggest in this chapter that genetic programming can also be an instrument in the economists’ toolbox to improve existing models or to support the generation of new economic models that relate to empirical observations. Hence, we claim that genetic programming can also be used for empirical analysis. We do not conﬁne the term empirical analysis to the mere testing of hypotheses. Rather, we maintain a broad concept of the term empirical analysis including any step in the research process that confronts economic theory with observed data. In this chapter we want to introduce a rationale for the use of genetic programming in empirical analysis. In doing so we want to start from the very...
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