| Abstract: | Recently, genetic programming has been proposed to model agents' adaptive behavior in a complex transition process where uncertainty
cannot be formalized within the usual probabilistic framework. However, this approach has not been widely accepted by economists.
One of the main reasons is the lack of the theoretical foundation of using genetic programming to model transition dynamics.
Therefore, the purpose of this paper is two-fold. First, motivated by the recent applications of algorithmic information theory
in economics, we would like to show the relevance of genetic programming to transition dynamics given this background. Second,
we would like to supply two concrete applications to transition dynamics. The first application, which is designed for the
pedagogic purpose, shows that genetic programming can simulate the non-smooth transition, which is difficult to be captured
by conventional toolkits, such as differential equations and difference equations. In the second application, genetic programming
is applied to simulate the adaptive behavior of speculators. This simulation shows that genetic programming can generate artificial
time series with the statistical properties frequently observed in real financial time series.
This revised version was published online in June 2006 with corrections to the Cover Date. |
