Applied Evolutionary Economics and Complex Systems

Applied Evolutionary Economics and Complex Systems

Edited by John Foster and Werner Hölzl

This book takes up the challenge of developing an empirically based foundation for evolutionary economics built upon complex system theory. The authors argue that modern evolutionary economics is at a crossroads. At a theoretical level, modern evolutionary economics is moving away from the traditional focus of the operation of selection mechanisms and towards concepts of ‘complex adaptive systems’ and self-organisation. On an applied level, new and innovative methods of empirical research are being developed and considered. The contributors take up this challenge and examine aspects of complexity and evolution in applied contexts.

Chapter 4: The use of genetic programming in evolutionary economics

Bernd Ebersberger and Andreas Pyka

Subjects: economics and finance, 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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