Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)
AbstractThis article aims to test the relevance of learning through Genetic Algorithms (GA) and Learning Classifier Systems (LCS), in opposition with fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These three R&D strategies are compared from the points of view of industry performance (welfare): the results of simulations clearly show that learning is a source of technological and social efficiency.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Equipe Industries Innovation Institutions, Université Bordeaux IV, France in its series Working Papers with number 2001-1.
Date of creation: 2001
Date of revision:
Learning; Learning Classifier Systems; Bounded Rationality; Technical Progress; Innovation;
Find related papers by JEL classification:
- O3 - Economic Development, Technological Change, and Growth - - Technological Change; Research and Development; Intellectual Property Rights
- L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-09-26 (All new papers)
- NEP-CMP-2001-09-26 (Computational Economics)
- NEP-EVO-2001-09-26 (Evolutionary Economics)
- NEP-INO-2001-09-26 (Innovation)
- NEP-TID-2001-09-26 (Technology & Industrial Dynamics)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Murat YILDIZOGLU (Université Aix-Marseille3), 2009. "Evolutionary approaches of economic dynamics (In French)," Cahiers du GREThA 2009-16, Groupe de Recherche en Economie Théorique et Appliquée.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Vincent Frigant).
If references are entirely missing, you can add them using this form.