%0 Journal Article %1 Chen:2000:AOR %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %D 2000 %J Annals of Operations Research %K Kolmogorov algorithms, bounded complexity, description genetic length minimum principle, programming, rationality, selling short %N 1-4 %P 265--286 %R doi:10.1023/A:1018972006990 %T Simulating economic transition processes by genetic programming %V 97 %X Recently, genetic programming has been proposed to model agents' adaptive behaviour in a complex transition process where uncertainty cannot be formalised 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.

@article{Chen:2000:AOR, abstract = {Recently, genetic programming has been proposed to model agents' adaptive behaviour in a complex transition process where uncertainty cannot be formalised 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Chen, Shu-Heng and Yeh, Chia-Hsuan}, biburl = {https://www.bibsonomy.org/bibtex/243621b2fa33526227432fa7f8744e16b/brazovayeye}, doi = {doi:10.1023/A:1018972006990}, interhash = {398982d84d2a50d837ab32a83fc88197}, intrahash = {43621b2fa33526227432fa7f8744e16b}, issn = {0254-5330}, journal = {Annals of Operations Research}, keywords = {Kolmogorov algorithms, bounded complexity, description genetic length minimum principle, programming, rationality, selling short}, month = {December}, number = {1-4}, pages = {265--286}, timestamp = {2008-06-19T17:37:43.000+0200}, title = {Simulating economic transition processes by genetic programming}, volume = 97, year = 2000 }